Evidence and Inference in the Comparative Case Study

Relative enrollment in online classes has tripled over the last ten years, but the efficacy of learning online remains unclear. While two recent Meta analyses report higher exam grades for online vs. traditional classes, this body of research has been marked by two recurrent limitations: (1) a possible problem of selection bias wherein students self select the mode of course delivery and (2) a relative lack of proctoring of exams in online sections. Both of these confounders contribute to observed differences in performance. The present study addresses these limitations. Data refer to 64 students enrolled in criminology classes at a Carnegie research extensive university. Due to an administrative error in the course schedule, which failed to list one section as online, students were unable to self select into the online section, creating a rare opportunity for quasi randomization of students into sections. Both sections were taught by the same instructor. The dependent variable is the score on the standardized final examination. All exams were proctored by the instructor. The central independent variable is method of delivery of content: online vs. the traditional classroom. Controlling for other constructs, there was no significant difference between exam scores. Also, student evaluations did not differ between sections. Controlling for selection effects and the proctoring of exams, the academic performance of online students was the same as that of traditional students. Future work is needed for other courses, other fields, and other types of academic institutions.

1 Introduction The question raised in this article is about how to theoretically and methodologically apprehend public policy controversies (1). More precisely, the role of ideas during public policy controversies between different policy coalitions is put into perspective. The starting point is the will to analytically construct ideas as dependent variables. Public policy scholars have underlined the confusion that lies at the heart of public policy analysis, where the status of ideas as causes or as variables is often blurred (Hassenteufel/Smith 2002: 60). When ideas are considered as independent variables, the analysis is likely to be more descriptive than explicative. In order to construct ideas as dependent variables, the focus has to be on the progressive formation of belief through the coalitions' adversarial activities. Hence, what will be questioned here is the process by which individuals do engage--or not-in public policy debate regarding harm reduction. We will not consider actors' engagement in a cause as unilaterally deriving from their beliefs. This line of questioning is closely linked to the ontological and epistemological choices. We assume that a processualist ontological stance is well-fitted to avoid the tautology induced by considering ideas as deja-la. By viewing militant commitment as a social and dynamic activity, a processualist focus highlights how ideas occur in the course of action (Fillieule 2001: 199-200). From this point of view, mobilized groups are by no means seen as preexisting entities driven by fixed ideas (Offerle 1994). This is particularly salient in the case of highly emotional controversies, where actors' ideas are often considered as if they had always existed. Furthermore, actors engaged in the same struggle do not necessarily share homogenous beliefs. Analyzing the heterogeneity of investments enables to understand the dynamics of the collective action (Mathieu 2004: 19). Similarly, the focus on the process of idea formation during policy controversies provides an opportunity to attain a deep understanding of the dynamics of the controversy. Analyzing ideas as dependent variables has important methodological repercussions. It shifts the location to where the explicative factors -and hence the data- are searched. The present reflection on public policy controversies is based on a case study research on harm reduction policies in matters of drug addiction in two Swiss cantons, Vaud and Geneva. The theoretical starting point of this study is the Advocacy Coalition Framework (ACF). Daniel Kubler (2000) has convincingly illustrated that this particular subject is well captured with the help of ACF's theoretical framework. The formulation of harm reduction policies are regularly the scene of sharp confrontations between policy coalitions, and ACF's concepts are particularly well-suited to the analysis of the Swiss multilevel politico-administrative system. Meanwhile, the benefits of using concepts from the social movement theory (Kubler 2001: 623) or the political sociology (Hassenteufel/Smith 2002: 63) in the public policy analysis has been asserted. The paper is structured as follows: In the first section, we review the contributions of the ACF to public policy analysis, and point out two theoretical points that appeared to be worth specification in the light of our case studies. These two points, already identified in the literature, concern the study of the long-term coalitions' birth and structuring process (Kubler 2001: 623; Schlager 1995), and the analysis of the specific arenas where the controversy takes place (Hassenteufel/Smith 2002:70-71; Muller 2006: 52). The second section is a theoretical discussion where we detail the concepts drawn from political sociology we used in our study. In the third section, we discuss the methodological operationalization of these concepts. We finally turn to the case studies on harm reduction policies. 2 Advocacy Coalition Framework By founding the Advocacy Coalition Framework at the end of the 1980s, Paul A. …

Human–Environment Relationship: Comparative Case Studies

Abstract: The integration of blockchain into smart cities represents a paradigm shift in how urban environments are conceptualized, governed, and sustained. This book, Blockchain in Smart Cities: Tools for Building Connected Urban Environments, provides a comprehensive analysis of blockchain as an enabling technology for resilient, transparent, and citizen-centric urban ecosystems. It examines the historical trajectory of smart cities, the foundational principles of blockchain, and their convergence in transforming critical infrastructures such as energy, mobility, healthcare, and governance. Through a multidimensional framework, the book explores blockchain’s potential to enhance transparency in public services, ensure secure property registries, and facilitate urban token economies. At the same time, it addresses pressing challenges such as scalability, cybersecurity, regulatory uncertainty, and social acceptance. Comparative global case studies—from Estonia’s e-governance to Dubai’s blockchain strategy—offer empirical insights into successes and limitations, highlighting the necessity of context-sensitive implementation. By synthesizing technological, economic, legal, and ethical perspectives, the book positions blockchain not merely as a digital tool but as an institutional innovation shaping the future of connected urban environments. It concludes with strategic policy recommendations and a roadmap for future research, underscoring blockchain’s transformative potential in building sustainable, inclusive, and adaptive cities for the 21st century and beyond. Keywords Blockchain, Smart Cities, Urban Innovation, Digital Governance, Token Economies, Smart Contracts, Decentralized Finance (DeFi), Urban Infrastructure, Sustainability, Cybersecurity, Internet of Things (IoT), Artificial Intelligence (AI), Urban Planning, Circular Economy, Decentralized Autonomous Governance, Urban Resilience, Digital Transformation, Comparative Case Studies

The implementation of healthcare policies in healthcare organizations is a pivotal issue for managers. They generally require a change in professional practices. In previous work, we developed the Integrative Framework for Implementation of change in Nursing Practices (IFINP) to support implementation initiatives for such change in nursing practices. We aimed to assess the generalizability of IFINP in other organizational settings and explore links between strategic and socio-material factors during implementation. We used a comparative qualitative case study at three French hospitals to assess the implementation of certification procedures. Data were collected from 33 semi-structured interviews with managers and nurses. Narratives reflecting actions and interactions were extracted and deductively analyzed using IFINP components. The results showed that the framework was flexible and captured the different aspects of implementation actions and interactions at the three hospitals. Strong interferences were identified between mobilization mechanisms and strategic elements. Interferences were observed mostly between ‘reflexive monitoring and work articulation’, and ‘reflexive monitoring and sense-making’ mechanisms. Leadership was integrated into the different mechanisms, especially the ‘translation’ mechanism. The IFINP facilitated a greater understanding of strategic elements and associated relationships with social and material factors during implementation. It helps to provide a clear definition of the managers’ role when implementing new nurse practices.

'Civil society', 'the market' and 'democracy' are the conceptual 'good guys' that dominate current Western thinking about China's present and future in the social, economic and political realms respectively. Though these ideas are often used in imprecise and tendentious ways, they have considerable analytical and practical power; they reflect real processes and point toward real solutions. This paper sets out to examine how useful the notion of 'civil society' is in describing and explaining social change in contemporary Chinese society. I shall proceed, flrst, by clarifying the specific way in which I intend to use the term 'civil society' and, second, by investigating the empirical utility of the idea through a case-study of one Chinese city.

Before a scholar can begin case-study research to test, refine, or interpret a regression analysis, a surprisingly complex prior challenge needs to be addressed: selecting cases for in-depth analysis from the comparatively large data set necessary for regression. Seawright and Gerring (2008) have proposed a set of systematic case-selection rules as a solution to this challenge. Yet the question remains, which of the several available methods should be used in a given project? Several scholars arguing in favor of deliberate case selection have focused on three of these alternatives: typical cases, deviant cases, and extreme cases on the dependent variable. Others have argued for random sampling (Fearon and Laitin 2008: 764–66) or deliberate sampling intended to represent the full range of variation in the data (King et al. 1994: 139–46). I argue that the existing advice is incomplete or misleading when the goal of case-study research is discovery. I develop this argument by showing that, across a wide range of goals, the alternatives with the best chances of facilitating discovery are either deviant-case selection or the rarely discussed alternative of selecting extreme cases on the main independent variable. This argument is developed with reference to a variety of discovery-related goals: searching for sources of measurement error in the dependent or key independent variables; testing for or trying to discover an omitted variable, which may or may not be correlated with the independent variable of interest; exploring hypotheses about causal pathways; finding a case with a causal effect close to the population mean; and discovering substantive sources of causal heterogeneity. Unlike much existing research on qualitative and case-study methods, this discussion is not driven by reference to examples of excellent research. In many other kinds of qualitative research practices, the analyst's judgment is a central ingredient in the application of the research tool; consequently, there is a great deal to learn from studying how judgment was employed in notably successful applications of the research tools in question. By contrast, systematic case selection is an algorithmic process: it takes a certain kind of information as an input, and then follows logical or mathematical rules to convert that input into a case. Because systematic case-selection rules reduce scholars’ reliance on judgment, the statistical properties of case-selection algorithms are more important than notably successful examples of their application.

Introduction: Work fatigue is a condition of decreased efficiency and resilience of workers, which can interfere the companys’ productivity. Job fatigue can be caused by the performance, duration, and effort (work position) of the workers. Based on the observations, workers at the expedition sub units of PT X needed to lift 200 to 400kg heavy loads. The activity of lifting very heavy weights can cause fatigue. One form of physical fatigue indicators is an increase in lactic acid in the blood. The purpose of this study was to analyze the correlation between workers’ characteristics and work position with work fatigue among workers in the expedition sub units of PT X. Methods: This research was a quantitative study with a cross sectional design. The population of this study was 36 people. This research used total sampling. The independent variable were individual characteristics and work position as measured using REBA tools, and the dependent variable was work fatigue as measured using lactic acid levels in blood. Data was analyzed using statistical tools to obtain the value of correlation coefficient between variables. Result: The results showed that there was a weak positive correlation between workers’ characteristics and physical fatigue. The results showed that the coefficient value between work position and physical fatigue was 0.354. The coefficient figure indicated a weak positive correlation between work position and work fatigue. Conclusion: There was a correlation between work position and work fatigue in workers.Keywords: ergonomics, lactic acid, REBA, work fatigue, work position .

Slums are densely populated settlements that are not habitable, the building density is very high in a very limited area, causing various problems such as being prone to social diseases and diseases. The distribution of slums in an area can be analyzed using geographic information systems as an innovative strategy to visualize geospatial information accurately, allowing for more in-depth and data-based analysis to support decision making. This study uses the Geographically Weighted Regression (GWR) method. Geographically Weighted Regression (GWR) is a statistical analysis that allows researchers to evaluate spatial variations in the relationship between dependent and independent variables, using the Fixed Gaussian kernel function to provide constant weighting across research locations, so that they can understand complex and different spatial relationships in each location. Based on data processing on seven indicators and 16 slum parameters stipulated in PUPR Regulation No. 14 of 2018, Panjang District, Bandar Lampung City, there are 5 sub-districts that are categorized as light slums with slum distribution points spread across various locations without clear concentration. The results of the Geographically Weighted Regression (GWR) model show that the independent variables (X) and dependent variables (Y) reflect a complex relationship with variable X 11 (non-standard wastewater management system) having the highest coefficient of 0.213830. The results of the GWR model in the ANOVA table show that the GWR model does not provide significant improvement.

This paper examines the methodological issues encountered in a comparative case study, which investigated how discourses shaped and legitimated policy maintenance and policy change in Germany before and after unification. The research strategy involved discourse analysis of party political and parliamentary documents, integrating detailed textual and contextual analysis of the institutional framework within which discourses were articulated. The paper examines the possibility of using comparative case studies to develop concepts, and reviews the relationship between concepts and context in qualitative comparative research. A critique is then offered of the process of analysis, ranging from the initial data analysis using NUD*iST, to the re-contextualisation of the textual analysis, and the reintegration of context, theory and textual analysis in writing up the research.

This book is a comparative case study in the historical sociology of modern social restorations. This is a new field in comparative social research designed to extend and complete comparative historical sociological research on social revolutions. Comparative research on revolutions is a well-established research field, as at least four generations of theory have changed to date (cf. Goldstone 2001, 2014; DeFronzo 2006, 2021). It originated with the comparison of 1789 French and 1917 Russian revolutions (Shlapentokh 1999), culminating in the famous study by Theda Skocpol (1979), which is one of the most influential and cited works in comparative social research (Goodwin 1996). Puzzlingly, as yet no authors have taken into due account the fact that both great modern social revolutions did in fact end (in 1815 and 1989 correspondingly) with restorations of the prerevolutionary regimes.

BackgroundDespite policy prominence and frameworks focusing on health inequalities, healthcare leaders do not feel they have the skills and knowledge to reduce health inequalities. This comparative case study explored four areas in England to understand the challenges, opportunities, and drivers of local health systems addressing health inequalities and what ‘good’ practice might look like.MethodsInterviews were held with 46 people working in health care services across the NHS, local authority or voluntary, community, social enterprise sectors. Key documents (n = ~ 10) in each of the four areas relating to reducing health inequalities were analysed using documentary analysis methods. Interviews and documents were coded and analysed independently before being integrated to synthesise findings. Analysis was conducted using a two-stage approach - firstly, an inductive analysis of emergent themes; secondly, to build knowledge on each case studys’ system approach of reduction of health inequalities, principles of the Action Scales Model were used.ResultsNineteen themes were identified across the four case studies; some themes were not apparent in all the case studies, nor in either the documentary analysis or interviews. These themes allowed us to compare between cases to explore what might be contributing to good practice. Themes identified included: understanding the local context; facilitators of how to tackle health inequalities and improve health and wellbeing; and future concerns. The secondary analysis highlighted potential levers for action from each case study; these included optimising retention and recruitment of workforce and allowing time and resources for longer-term planning. Two case study areas which appeared to have system resilience, demonstrated having a shared vision, strong partnerships, understanding of the system, and putting people and communities at the heart of decision making.ConclusionThis comparative case study makes a crucial contribution in the understanding of health systems addressing health inequalities in their local areas. The combined interview and documentary analysis findings provide rich insights of local systems’ documented strategies, plans and what is happening ‘on the ground’.

This study examines the pedagogical conditions that contribute to a productive teaching and learning experience in diversity courses, using the University of Utah (UU) as the case study. The purpose of this research is to assess whether diversity courses are effective (successful) in helping students gain more information about nondominant groups that might influence their attitudes about and behavior towards disaffected members of American society; or, whether diversity courses engender student resentment. A survey of the literature identifies classroom climate, course content, and teacher credibility as having explanatory value. These constitute the study's independent variables. Two dependent variables are measured, “successful teaching” and “student resentment.” A random sample of 26 diversity courses (including five political science classes) was selected for analysis. The methodology includes classroom visits, quasi-structured surveys of faculty and students, interrogation of student course evaluations, content analyses of syllabi, textbooks, exams, and other teaching materials, and aggregate data analysis. Findings support the significance of classroom climate and teacher credibility, the importance of course size and instructor rank, and the weak influence of course content. Of the total sample, political science courses are the least successful in delivering the diversity message. Despite concerted efforts to promote diversity in their teaching and an intellectual bias towards tolerance, teachers in the discipline often fail to create an environment that models respect for difference in practice.

Prevention research concerning alcohol, tobacco and other drugs faces a number of challenges as the scientific foundation is strengthened for the future. Seven issues which the prevention research field should address are discussed: lack of transparency in analyses of prevention program outcomes, lack of disclosure of copyright and potential for profit/income during publication, post-hoc outcome variable selection and reporting only outcomes which show positive and statistical significance at any follow-up point, tendency to evaluate statistical significance only rather than practical significance as well, problem of selection bias in terms of selecting subjects and limited generalizability, the need for confirmation of outcomes in which only self-report data are used and selection of appropriate statistical distributions in conducting significance testing. In order to establish a solid scientific base for alcohol, tobacco and drug prevention, this paper calls for discussions, disclosures and debates about the above issues (and others) as essential. In summary, the best approach is always transparency.

SummaryThis paper analyzes the endogeneity bias problem caused by associations of members within a network when the spatial autoregressive (SAR) model is used to study social interactions. When there are unobserved factors that affect both friendship decisions and economic outcomes, the spatial weight matrix (sociomatrix; adjacency matrix) in the SAR model, which represents the structure of a friendship network, might correlate with the disturbance term of the model, and consequently result in an endogenous selection problem in the outcomes. We consider this problem of selection bias with a modeling approach. In this approach, a statistical network model is adopted to explain the endogenous network formation process. By specifying unobserved components in both the network model and the SAR model, we capture the correlation between the processes of network and outcome formation, and propose a proper estimation procedure for the system. We demonstrate that the estimation of this system can be effectively done by using the Bayesian method. We provide a Monte Carlo experiment and an empirical application of this modeling approach on the friendship networks of high school students and their interactions on academic performance in the Add Health data. Copyright © 2015 John Wiley & Sons, Ltd.