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What do you do with your data once you have collected it? This chapter will elucidate the procedures for judicious handling of a large body of natural speech materials, such as audio files, interview reports, and consent forms.
Early CALL researchers focused primarily on the technical aspects of language learning, such as computer program design and the use of multimedia. However, over time the field has evolved to encompass a wider range of pedagogical and psychological considerations. For example, researchers have investigated the efficacy of different teaching approaches, such as task-based language learning and communicative language teaching through technology. They have also explored the role of learner motivation in technology-mediated language learning, as well as the impact of technology on learners’ attitudes toward language learning. In terms of research design and settings, CALL studies have become increasingly diverse. While early studies often relied on small-scale, laboratory-based experiments, more recent research has taken place in real-world educational settings, such as classrooms and online learning environments using a wider range of research methods, including both quantitative and qualitative approaches. Overall, while there have been significant developments in CALL research over the past four decades, there are still many questions that remain unanswered. As technology continues to evolve, it is likely that researchers will continue to explore new avenues for integrating technology into language education and addressing the challenges that arise along the way.
Chapter 2 presents the bounded accountability theory of incumbency bias and its main empirical predictions and outlines the core empirical strategy for testing the theory across the country–office cases. After offering a conceptualization and typology of incumbency bias, the chapter explains how the nature of the information environment encourages retrospective voting and leads to the emergence of incumbency bias. Based on this general mechanism, the chapter predicts that the alignment of policy scope and fiscal institutions explains why some democracies exhibit incumbency advantage while others display an incumbency disadvantage, and demonstrates how exogenous shocks may lead to within-country changes in incumbency bias. The chapter also derives predictions about why there are differences between personal and party incumbency bias. It concludes by developing a novel estimation framework that extends the close-election regression discontinuity design to measure incumbency bias in different political systems and document variation in direction and type within them.
This chapter motivates the book’s central questions: Why is incumbency an electoral blessing for politicians in some countries but an electoral curse in others? Why does incumbency bias emerge? What are the consequences of incumbency bias for democracy? The chapter then presents the book’s bounded accountability theory in brief: incumbency bias emerges and varies because democratic institutions generate a mismatch between citizens’ expectations of incumbent performance and incumbents’ capacity to deliver. The chapter clarifies how this argument builds and expands upon prior work on incumbency bias in Latin America and the US, and how it draws theoretical insights from theoretical and empirical work on electoral accountability. The chapter also distils contrasting predictions between bounded accountability and theories that stress corruption and clientelism as the drivers of incumbency advantage and disadvantage. The chapter closes by describing the case selection and outlining its nested-multilevel research design that combines cross-country and within-country comparisons and employs tools of causal inference to examine incumbency bias in Argentina, Brazil and Chile.
The diversity gap in precision medicine research (PMR) participation has led to efforts to boost the inclusion of underrepresented populations. Yet our prior research shows that study teams need greater support to identify key decision-making issues that influence diversity and equity, weigh competing interests and tradeoffs, and make informed research choices. We therefore developed a Diversity Decision Map (DDM) to support the identification of and dialogue about study practices that impact diversity, inclusion, and equity.
Methods:
The DDM is empirically derived from a qualitative project that included a content analysis of documents, observations of research activities, and interviews with PMR stakeholders. We identified activities that influenced diversity goals and created a visual display of decision-making nodes, their upstream precedents, and downstream consequences. To assess the potential utility of the DDM, we conducted engagements with stakeholder groups (regulatory advisors, researchers, and community advisors).
Results:
These engagements indicated that the DDM helped diverse stakeholder groups trace tradeoffs of different study choices for diversity, inclusion, and equity, and suggest paths forward. Stakeholders agreed that the DDM could facilitate discussion of tradeoffs and decision-making about research resources and practices that impact diversity. Stakeholders felt that different groups could use the DDM to raise questions and dilemmas with each other, and shared suggestions to increase the utility of the DDM.
Conclusion:
Based on a research life course perspective, and real-world research experiences, we developed a tool to make transparent the tradeoffs of research decisions for diversity, inclusion, and equity in PMR.
This chapter describes the data collection strategy and multimethod research design employed to test the theory in the subsequent chapters of the book. The structure of the empirical analysis mirrors the book’s primary argument: to show how peacekeeping works from the bottom up, from the individual to the community to the country. Given that UN peacekeepers deploy to the most violent areas, the design needed to account for selection bias as well as other confounding variables in order to make causal inference possible. Using data from individual- and subnational/community-level data from Mali as well as cross-national data from the universe of multidimensional PKOs deployed in Africa, the book employs a three-part strategy to test the hypotheses in the next few chapters. First, the book considers the micro-level behavioral implications of the theory using a lab-in-the-field experiment and a survey experiment, both implemented in Mali. Second, it test whether UN peacekeepers’ ability to increase individual willingness to cooperate aggregates upward to prevent communal violence in Mali. Third, the book considers whether these findings extend to other countries.
Field research refers to research conducted with a high degree of naturalism. The first part of this chapter provides a definition of field research and discusses advantages and limitations. We then provide a brief overview of observational field research methods, followed by an in-depth overview of experimental field research methods. We discuss randomization schemes of different types in field experimentation, such as cluster randomization, block randomization, and randomized rollout or waitlist designs, as well as statistical implementation concerns when conducting field experiments, including spillover, attrition, and noncompliance. The second part of the chapter provides an overview of important considerations when conducting field research. We discuss the psychology of construal in the design of field research, conducting non-WEIRD field research, replicability and generalizability, and how technological advances have impacted field research. We end by discussing career considerations for psychologists who want to get involved in field research.
In most social psychological studies, researchers conduct analyses that treat participants as a random effect. This means that inferential statistics about the effects of manipulated variables address the question whether one can generalize effects from the sample of participants included in the research to other participants that might have been used. In many research domains, experiments actually involve multiple random variables (e.g., stimuli or items to which participants respond, experimental accomplices, interacting partners, groups). If analyses in these studies treat participants as the only random factor, then conclusions cannot be generalized to other stimuli, items, accomplices, partners, or groups. What are required are mixed models that allow multiple random factors. For studies with single experimental manipulations, we consider alternative designs with multiple random factors, analytic models, and power considerations. Additionally, we discuss how random factors that vary between studies, rather than within them, may induce effect size heterogeneity, with implications for power and the conduct of replication studies.
This chapter describes the book's case study approach, which compares Ethiopia, Ghana, and Kenya. All three countries experienced the regional trend of increased borrowing from China and in international bond markets in the 2000s. However, the countries vary in strategic significance and donor trust, allowing for tests of heterogeneity in the financial statecraft of borrowers. The chapter discusses the data collection process for the case studies, with over 170 elite interviews, mostly with government and donor officials participating in aid negotiations, and how this data is used to trace debt-based financial statecraft in each country. The chapter briefly provides background on each country's political and economic context and previews findings on how their external finance portfolios impacted aid negotiations with traditional donors.
This chapter introduces the concept of community policing and provides a brief history of the practice and its spread. The chapter then identifies a significant gap in rigorous evidence of its efficacy, especially as the practice has been adopted by police agencies in the Global South and describes the core enterprise of the research agenda: a set of coordinated, randomized-control trials evaluating the impact of community policing in Latin America, Africa, and Asia. The chapter concludes with a summary of the findings and a discussion of broader implications for the study of policing.
There is a growing awareness that diversity, health equity, and inclusion play a significant role in improving patient outcomes and advancing knowledge. The Pediatric Heart Network launched an initiative to incorporate diversity, health equity, and inclusion into its 2021 Scholar Award Funding Opportunity Announcement. This manuscript describes the process of incorporating diversity, health equity, and inclusion into the Pediatric Heart Network Scholar Award and the lessons learned. Recommendations for future Pediatric Heart Network grant application cycles are made which could be replicated by other funding agencies.
A user-friendly introductory guide to the empirical study of social networks. Jennifer M. Larson presents the fundamentals of social networks in an intuition-forward way which guides theory-driven research design. Substantial attention is devoted to a framework for developing a network theory that will steer data collection to be maximally informative and minimally frustrating. Other features include: Coverage of a range of practical topics including selecting operationalizations, cutting survey costs, and cleaning data; A tutorial for getting started in analyzing networks in R; Technical sections full of examples, points to hone intuition, and practice problems with solutions. Designing Empirical Social Networks Research will be a valuable tool for advanced undergraduates, Ph.D. students in the social sciences, especially political science, and researchers across the social sciences who are new to the study of networks.
Taking a simplified approach to statistics, this textbook teaches students the skills required to conduct and understand quantitative research. It provides basic mathematical instruction without compromising on analytical rigor, covering the essentials of research design; descriptive statistics; data visualization; and statistical tests including t-tests, chi-squares, ANOVAs, Wilcoxon tests, OLS regression, and logistic regression. Step-by-step instructions with screenshots are used to help students master the use of the freely accessible software R Commander. Ancillary resources include a solutions manual and figure files for instructors, and datasets and further guidance on using STATA and SPSS for students. Packed with examples and drawing on real-world data, this is an invaluable textbook for both undergraduate and graduate students in public administration and political science.
From education to healthcare and management processes, it is important to address the experience in health within its own complexity, context, and uniqueness. At this point, qualitative studies come to the fore and this increases the need for practical guides and models for qualitative studies. Qualitative studies have a paradigm that is different from quantitative research and its paradigm ontologically, epistemologically, and methodologically. These differences are reflected in the design of the research as well as the analysis, interpretation, and reporting of qualitative data. From such a point of view, this paper first briefly outlines the design process of qualitative studies and then proposes a model for the analysis, interpretation, and reporting of qualitative data.
Conceptual/theoretical framework:
The three core concepts of the model are ‘contextuality’, ‘reflectivity’, and ‘narrativity’. Such a conceptual/theoretical framework transforms qualitative data analysis, interpretation, and reporting processes into processes that are carried out with a reflective approach within their specific contexts.
Model:
Taking this into account, by considering a contextual, reflective, and narrative approach, two frameworks, namely, the ‘Contextual (Multiple) Reading and Analysis Framework’ consisting of three stages and seven steps, and the ‘Contextual Understanding, Interpretation and Reporting Framework’ consisting of four stages, were developed. This provides a practical guide to contextual and reflective data analysis, interpretation, and reporting for the use of those conducting qualitative studies.
The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Cross-Trial Statistics Group gathered lessons learned from statisticians responsible for the design and analysis of the 11 ACTIV therapeutic master protocols to inform contemporary trial design as well as preparation for a future pandemic. The ACTIV master protocols were designed to rapidly assess what treatments might save lives, keep people out of the hospital, and help them feel better faster. Study teams initially worked without knowledge of the natural history of disease and thus without key information for design decisions. Moreover, the science of platform trial design was in its infancy. Here, we discuss the statistical design choices made and the adaptations forced by the changing pandemic context. Lessons around critical aspects of trial design are summarized, and recommendations are made for the organization of master protocols in the future.
Research study complexity refers to variables that contribute to the difficulty of a clinical trial or study. This includes variables such as intervention type, design, sample, and data management. High complexity often requires more resources, advanced planning, and specialized expertise to execute studies effectively. However, there are limited instruments that scale study complexity across research designs. The purpose of this study was to develop and establish initial psychometric properties of an instrument that scales research study complexity.
Methods:
Technical and grammatical principles were followed to produce clear, concise items using language familiar to researchers. Items underwent face, content, and cognitive validity testing through quantitative surveys and qualitative interviews. Content validity indices were calculated, and iterative scale revision was performed. The instrument underwent pilot testing using 2 exemplar protocols, asking participants (n = 31) to score 25 items (e.g., study arms, data collection procedures).
Results:
The instrument (Research Complexity Index) demonstrated face, content, and cognitive validity. Item mean and standard deviation ranged from 1.0 to 2.75 (Protocol 1) and 1.31 to 2.86 (Protocol 2). Corrected item-total correlations ranged from .030 to .618. Eight elements appear to be under correlated to other elements. Cronbach’s alpha was 0.586 (Protocol 1) and 0.764 (Protocol 2). Inter-rater reliability was fair (kappa = 0.338).
Conclusion:
Initial pilot testing demonstrates face, content, and cognitive validity, moderate internal consistency reliability and fair inter-rater reliability. Further refinement of the instrument may increase reliability thus providing a comprehensive method to assess study complexity and related resource quantification (e.g., staffing requirements).
This book explores how the EU has attempted to balance its energy security objectives in the twenty-first century, to achieve security of supply, reasonable prices and ambitious climate goals. Specifically, the book focuses on how these challenges have played out in Central and Eastern Europe in the context of their accession to the EU, as members are both subject to and shape the EU’s agenda and legislative outputs. Here we introduce how general prioritisation of security of supply concerns has constrained and at times enabled energy transitions in the region, and how a consistent concern with import dependence on Russia was discursively adopted by the wider EU in the late 2000s, and as a policy goal from 2022. The introduction presents two main arguments of the book (priority of energy security of the CEE countries over climate goals and heterogeneity of the region) and its research design.
In recent years, the rising number of LGBTIQ+ politicians across the world has been matched by an increase in academic attention on which factors foster or hinder their careers. Here, we provide a comprehensive analytical review of the relevant literature, with the goal of illustrating both its synergies and imbalances. We show that most of the existing evidence specifically concerns LGBTIQ+ politicians' electoral performance. Moreover, this knowledge has largely been produced in very similar contexts politically and socioculturally. Finally, we highlight the potential of investigating a number of additional factors that may impact LGBTIQ+ political careers, such as intersectional dynamics that may have a differentiated impact within this population. Future works could expand the scope of this literature by considering these elements and focussing more on the direct experience of LGBTIQ+ politicians.
In this part of the book, I move from a comparative historical analysis of Poland and East Germany in Part II to an analysis of quantitative data drawn from all the socialist dictatorships of Bulgaria, Czechoslovakia, East Germany, Hungary, Poland and Romania. The purpose of the following two chapters is to test whether the argument developed in Chapter 2 can travel beyond the Polish and East German cases examined above to explain variation in the turnover of coercive elites and the size of coercive institutions across the region from 1945 to 1989.
Maggie B. Gale explores ways of both framing and structuring the beginnings of a research project, and finding what might be called a ‘research niche’. She uses the case study of an emerging research project to articulate different possible approaches to conceptualizing the starting point, direction, and shape of a project, as well as working practices which might be useful in research design and method. The chapter also explores a series of working principles for avoiding the pitfalls of research distractions, without missing out on the serendipitous discoveries which a more unstructured process might allow. Gale’s own research on Elsa Lanchester illustrates the principles.