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Despite the tremendous renaissance of comparative constitutional law, the comparative aspect of the enterprise, as a method and a project, remains under-theorized and imprecise. Methodological self-awareness has not been one of the field’s strengths. In comparative constitutional law (and Constitutionalism in Context more generally) the term “comparative” is often used indiscriminately to describe what, in fact, are several different types of scholarship, each with its own meanings, aims and purposes. What is more, various vocational, jurisprudential, academic, and scientific stakeholders involved in practicing the art of constitutional comparison. This chapter will explore the various types, aims and methodologies deployed in exploring constitutional phenomena comparatively across time and space. In so doing, it will identify some gaps in the field’s contemporary methodological matrix and suggest ways in which these deficiencies may be addressed and overcome.
Behavioural animal experimentation is an inseparable part of research trying to understand the biological underpinnings of human behaviour, diseases and disorders. Working with animals comes with great responsibility to achieve reliable and reproducible results of highest scientific quality. In a simple step-by-step fashion, we highlight some common issues that may occur along the path to conducting behavioural animal experimentations and posit some solutions and grounds to ensure the excellence of work done in this research area while aspiring to improve conditions for laboratory animals. It entails topics of study design, animal and experimenter welfare, experimental considerations and frequentist biostatistics. At the end, we direct to some guidelines and manuals that may prove valuable to researchers in this field. Our ten simple tips and traps are meant for students who are learning about important concepts for the first time; graduates whose statistics training all too often has neglected the concept of power in experimental design; and researches who would like a light-hearted refresher on these topics. With this perspective, we hope that you will avoid falling into traps and find answers to what you always wanted to know about conducting behavioural animal experimentation.
In this chapter I develop my argument to explain variation in the processes and mechanisms that lead to distinct strategies of resistance to criminal extortion. I first define the core concepts that readers will encounter throughout the book. Next I explain the logic of the argument to show how the intersection between the time horizons of criminal actors, the nature of local political economies, and whether there is criminal capture of the police shapes the strategies of resistance that victims pursue. I then outline the parameters under which I expect the argument to hold, and discuss how my study builds on insights into existing research. I conclude by discussing the research design, case selection, and the methodologies that I used to collect and analyze data.
This last, consolidating chapter has four goals. The first is consolidation: we specify appropriate case selection strategies for research using QCA and summarize an integrated protocol for analyzing set relations in an iterative manner, which bases inferences on cross-case patterns, knowledge of individual cases, and external knowledge. This protocol follows three main steps both for necessary and for sufficient conditions: determining empirical consistency, empirical importance, and substantive importance. The second goal is to broaden the perspective: we discuss the diverse variants, uses, and analytic goals of QCA in different research designs. We argue that for deriving valid inferences with QCA, it is important to coherently choose tools in line with analytic approaches. Third, we summarize and update good practices for conducting QCA and presenting and visualizing its results. Lastly, we map exciting developments that are likely to shape the field in the foreseeable future, including a summary of prominent software functionality.
- Consolidated knowledge of the analytic protocol of QCA.
- Overview of different uses of QCA, their analytic goals and corresponding tools.
- Overview of recommendations for good practice and transparency before, during, and after the analytic moment.
This chapter uses an empirical example to explain what Qualitative Comparative Analysis (QCA) is and how it works. We familiarize the reader with the basic analytic goals and steps of QCA and the results this method produces. We also sketch the empirical spread of QCA and related software. We explain how this book is structured and how the reader can best use it.
QCA identifies necessary and sufficient conditions for an outcome by modeling core aspects of causal complexity. As QCA is a set-theoretic method, we attribute cases to sets that represent the outcome we want to explain, the conditions we assume to be relevant for this outcome, and we analyze necessary and sufficient conditions as set relations. Before the analytic moment, we design our research, conceptualize cases and sets, and transform them into ‘data’ (‘calibration’). The ‘analytic moment’ refers to the actual analyses of necessity and sufficiency. Finally, we interpret the results and check how ‘good’ they are.
- Familiarity with the general analytic goals and motivations underlying the use of QCA.
- Basic understanding of the main analytic steps involved in doing a QCA.
- Basic understanding and interpretation of QCA results.
This chapter examines the trajectory of a research project on militant organizations’ adaptation that began as a “classic” case comparison and was “re-cased” into an explicitly network-based comparison of intra-organizational networks. In doing so, it outlines a method of comparison focused primarily on roles, relations, and emergence rather than on organizational form or behavior. The chapter starts by discussing the project’s initial research design, which proposed a study of militant organizations across three Palestinian refugee camps in Lebanon that largely adhered to Millian logic. The project dedicated extensive research time to establishing a pre-invasion “control” by seeking to demonstrate pre-shock organizational uniformity across the communities under study. However, the evidence gathered often complicated or contradicted logics of control, independence, causality, and identification that undergird dominant approaches to comparison. Rather, it repeatedly indicated that complex, relational, often contingent interactions among geographic environment, communities’ interpretations of violence, and organizational structures influenced outcomes of interest. The chapter leverages this experience to establish core tenets of a broader approach to studying organizational change in comparative perspective.
What does it mean to advance women’s status and well-being? And how should we think about the role of the state in bringing about that advancement? Our work analyzes the approach and role of the state in promoting women’s empowerment, drawing on large-N country-level data and in-depth case studies of state action in the United States, Norway, and Japan. Our three country cases vary greatly in terms of the state’s approach to women’s rights; we picked them because we believe them to be extreme examples of how state action is driven by different visions of what women’s empowerment is about. Conducting fieldwork in these different contexts allows us to study some of the variation in people’s views of both state action and empowerment. It sharpens our awareness of important assumptions that underlie studies of empowerment. It also helps us determine the right questions to ask. To the extent that we study causal relationships, we do so based on large-N data within cases, not across them. And rather than assume that the same causal patterns apply across cases, we draw on our fieldwork to better understand why the same policies produce vastly different effects in different contexts. This chapter is a reflection on some of the goals of comparative studies that are unrelated to drawing causal inferences, and how to think about research design and case selection to achieve these goals.
The aim of this study is to comprehensively evaluate quantitative experimental mobile-assisted language learning (MALL) studies published between 1994 and 2019 that meet minimal conditions of research design and statistical analysis. Starting with a bibliographical database of 1,144 references to experimental MALL implementations, of which there were 700 objectively substantiated by quantitative experimental language learning outcomes, only 84 experimental MALL studies met the inclusion requirements. Their analysis addresses two critical sets of research questions. First, what are the general characteristics of the selected studies and, second, what are their language learning outcomes in terms of measured effect size. Nine general characteristics are considered: publication source, chronological distribution, country of origin, institutional environment, sample size, intervention duration, targeted language, language learner competence level, and learning focus. Effect size was calculated separately for between-group (independent, experimental) and within-group (quasi-experimental) treatment studies. In both cases, the overall results were quite large: 0.72 for the former and 1.16 for the latter. An analysis of four critical moderator variables (language learner competence level, language area focus, institutional environment, and intervention duration) revealed similarly large effect sizes. Notwithstanding, analysis of the data also confirmed obvious publication bias and a very high level of heterogeneity that frequently approached 100%. The relevance of positive language learning outcome conclusions thus needs to be tempered by these shortcomings.
In the last decades, ‘research design’ has become a strategic topic across political science. An emerging discourse relies on it to encompass paradigmatic oppositions and cultivate a pluralist approach to causation. As an introduction to the special issue on the topic, we offer an outline of the roles that the discipline recognizes to design in its relation to models and contend that, in a time of fascination for predictors, political science pluralism allows for balancing interpretability and validity of findings at once.
Experimental political science has changed. In two short decades, it evolved from an emergent method to an accepted method to a primary method. The challenge now is to ensure that experimentalists design sound studies and implement them in ways that illuminate cause and effect. Ethical boundaries must also be respected, results interpreted in a transparent manner, and data and research materials must be shared to ensure others can build on what has been learned. This book explores the application of new designs; the introduction of novel data sources, measurement approaches, and statistical methods; the use of experiments in more substantive domains; and discipline-wide discussions about the robustness, generalizability, and ethics of experiments in political science. By exploring these novel opportunities while also highlighting the concomitant challenges, this volume enables scholars and practitioners to conduct high-quality experiments that will make key contributions to knowledge.
Recent decades have seen large tax increases in Latin America. The conventional wisdom that Latin American tax systems generate too little revenue seems harder to sustain today than in the past. What continues to be striking about the region’s tax burdens, however, is the great disparity between them. This book sheds light on this question through a comparison of Argentina, Brazil, Chile and Mexico. It argues that tax burden variance reflects the impact of historical episodes of redistribution that threatened private property. Where they occurred, such episodes impeded future taxation by prompting economic elites and social conservatives to organize to defend their interests, thus forging strong, enduring anti-statist blocs. These blocs hindered taxation both directly, by combatting efforts to boost revenue, and indirectly, by undermining statist actors, especially labor unions. This introductory chapter consists of five sections: the first provides an overview of Latin American tax systems, the second reviews the scholarship on tax burden determinants, the third sketches the book’s argument, the fourth explains the research design and the fifth describes subsequent chapters.
Evidence is limited on how to synthesize and incorporate the views of stakeholders into a multisite pragmatic trial and how much academic teams change study design and protocol in response to stakeholder input. This qualitative study describes how stakeholders contributed to the design, conduct, and dissemination of findings of a multisite pragmatic clinical trial, the COMprehensive Post-Acute Stroke Services (COMPASS) Study. We engaged stakeholders as integral research partners by embedding them in study committees and community resource networks that supported local sites. Data stemmed from formal focus groups and continuous participation in working groups. Guided by Grounded Theory, we extracted themes from focus group and meeting notes. These were discussed as a team and with other stakeholder groups for feasibility. A consensus approach was used. Stakeholder input changed many aspects of the study including: the care model that treated stroke as a chronic condition after hospital discharge, training for hospital-based providers who often lacked awareness of the barriers to recovery that patients face, support for caregivers who were essential for stroke patients’ recovery, and for community-based health and social service providers whose services can support recovery yet often go underutilized. Stakeholders brought value to both pragmatic research and health service delivery. Future studies should test the impact of elements of study implementation informed by stakeholders vs those that are not.
Paradoxically, doing corpus linguistics is both easier and harder than it has ever been before. On the one hand, it is easier because we have access to more existing corpora, more corpus analysis software tools, and more statistical methods than ever before. On the other hand, reliance on these existing corpora and corpus linguistic methods can potentially create layers of distance between the researcher and the language in a corpus, making it a challenge to do linguistics with a corpus. The goal of this Element is to explore ways for us to improve how we approach linguistic research questions with quantitative corpus data. We introduce and illustrate the major steps in the research process, including how to: select and evaluate corpora, establish linguistically-motivated research questions, observational units and variables, select linguistically interpretable variables, understand and evaluate existing corpus software tools, adopt minimally sufficient statistical methods, and qualitatively interpret quantitative findings.
This chapter discusses salient methodological considerations and challenges in undertaking empirical research with young, newly arrived migrant students. This includes questions relating to negotiating access, sampling of core participants, the role of language and use of interpreters, and the importance of giving migrant students a voice as part of an overall holistic approach which focuses on the student perspective and the relationship of this to school and parental perspectives. Approaches to assessing language development and social integration are explored. Such considerations raise questions about the relevance of conducting research with newcomer migrant students in a range of different countries and contexts. This chapter also provides an overview of the research design adopted in the studies funded by the Bell Foundation and explores how such methodological considerations were taken into account throughout the study.
The main feature of observational studies is the representation of naturalistic treatment conditions. In contrast to clinical trials, they allow the evaluation and quantification of adverse event profiles of drugs under “real life” conditions. The price for this unquestionable chance is the proneness to distorting factors, which may aggravate the interpretation of the study results. Analysis of observational study results therefore has to control for potentially influential factors and reconsider possible alternatives explaining observed associations. The most important distorting factors, which should be taken into account during analysis and interpretation are under-reporting, event selection, bias, confounding and misusage? Authors and readers of such study results should be aware of this possible sources of error, in order to derive optimal benefit from this study approach.
Under which conditions will a public authority intervene in private governance such as certification and eco-labeling schemes for sustainably produced goods? This chapter introduces this research question by presenting the empirical puzzle the book addresses: Why has the European Union (EU) intervened in private governance that deals with organic agriculture and biofuels, but has not intervened in private governance dealing with fair trade and fisheries? The chapter distinguishes between a public authority intervening with standards regulation that involves creating a public definition of sustainable production, and with procedural regulation that addresses the way private governance schemes are organized. The argument the book develops is that whether a public authority intervenes with standards and/or procedural regulation depends on the interplay of two variables: the domestic benefits of product differentiation by a public authority and the fragmentation of the private governance market. The chapter situates the book in the current state of the literature on the interactions between public and private governance and explains the research design and research contributions.
Experimental psychopathology is the psychological science discipline that uses the methods of the experimental psychology laboratory in conjunction with quantitative analytic approaches to gain leverage on the etiology and pathogenesis of psychopathology, within a brain-based (genomic, endophenotype, neurobiological) diathesis-stressor matrix. Laboratory methods provide precision in measurement not attainable through clinical rating approaches and experimental design options allow the investigator to better identify potentially causal as well as maintaining processes in psychopathology. The chapter provides both a historical context within which experimental psychopathology can be placed and identifies conceptual and methodological features of the approach. A number of issues are addressed: (a) the value of clinical observation; (b) context of discovery; (c) counting vs. rating in data collection; (d) the falsity of the null hypothesis in statistical testing; (e) levels of analysis; (f) how predictors are conceived of in many instances; (g) the importance of embracing heterogeneity in empirical data; (h) specific etiology and genetics; (i) emergence; and (j) causality in a correlational framework. This overview is intended to convey defining features of the experimental psychopathology approach.
Health psychology and behavioral medicine are founded on the biopsychosocial model, in which health and disease reflect reciprocal influences among biological, psychosocial, and sociocultural processes. As a result, research methods in these fields draw on concepts and methods from several disciplines and often require their integration. Health psychology and behavioral medicine include three major topics: health behavior and risk reduction; psychosocial aspects of medical illness and medical care; and psychosocial and psychobiological influences on disease. This chapter emphasizes methodological challenges in the third topic, although the issues discussed are broadly relevant to the others. Conceptualization and measurement of health endpoints presents evolving challenges in which measured outcomes must capture specific and well-defined aspects of health and disease. In the identification of psychosocial predictors of health outcomes, psychosocial epidemiology research must address a variety of challenges, including the conceptualization, measurement, and analysis of overlapping risk factors. In research on the psychobiological mechanisms linking risk and resilience factors with health outcomes, theory-driven research should consider a broad range of interrelated physiological processes and multiple sources of pathogenic physiological activation. Across the various research topics, clear ties to conceptual models, consideration of developmental issues across the lifespan, the need to examine both between- and within-person associations in many research questions, and the importance of health disparities and related aspects of ethnic and cultural diversity are important in measurement, design, and analysis of biopsychosocial research.
This chapter outlines critical design decisions for longitudinal research and provides practical tips for managing such studies. It emphasizes that generative longitudinal studies are driven by conceptual and theoretical insights and describes four foundational design issues including questions about time lags and sample sizes. It then provides advice about how to manage a longitudinal study and reduce attrition. The chapter concludes by considering how the advice offered comports with recent discussions about ways to improve psychological science and providing recommended further reading.
Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.