A state-of-the-art approach to evaluating research design for students and scholars across the social sciences Assessing the properties of research designs before implementing them can be tricky for even the most seasoned researchers. This book provides a powerful framework—Model, Inquiry, Data Strategy, and Answer Strategy, or MIDA—for describing any empirical research design in the social sciences. MIDA enables you to characterize the key analytic features of observational and experimental designs, qualitative and quantitative designs, and descriptive and causal designs. An accompanying algorithm lets you declare designs in the MIDA framework, diagnose properties such as bias and precision, and redesign features like sampling, assignment, measurement, and estimation procedures. Research Design in the Social Sciences is an essential tool kit for the entire life of a research project, from planning and realization of design to the integration of your results into the scientific literature. A must-have resource for current and future researchers who want to learn about the properties of their designs before they implement them Includes a library of the most common designs in the social sciences Provides a complete declaration of the canonical design for each library entry, describes the circumstances under which the design can be strong or weak, and explores the consequences of the choices under the research designer’s control Accompanied by online resources that can be used in conjunction with the book An ideal textbook for graduate students and advanced undergraduates

There is a growing consensus in the social sciences on the virtues of research strategies that combine quantitative with qualitative tools of inference. Integrated Inferences develops a framework for using causal models and Bayesian updating for qualitative and mixed-methods research. By making, updating, and querying causal models, researchers are able to integrate information from different data sources while connecting theory and empirics in a far more systematic and transparent manner than standard qualitative and quantitative approaches allow. This book provides an introduction to fundamental principles of causal inference and Bayesian updating and shows how these tools can be used to implement and justify inferences using within-case (process tracing) evidence, correlational patterns across many cases, or a mix of the two. The authors also demonstrate how causal models can guide research design, informing choices about which cases, observations, and mixes of methods will be most useful for addressing any given question.

A state-of-the-art approach to evaluating research design for students and scholars across the social sciences Assessing the properties of research designs before implementing them can be tricky for even the most seasoned researchers. This book provides a powerful framework—Model, Inquiry, Data Strategy, and Answer Strategy, or MIDA—for describing any empirical research design in the social sciences. MIDA enables you to characterize the key analytic features of observational and experimental designs, qualitative and quantitative designs, and descriptive and causal designs. An accompanying algorithm lets you declare designs in the MIDA framework, diagnose properties such as bias and precision, and redesign features like sampling, assignment, measurement, and estimation procedures. Research Design in the Social Sciences is an essential tool kit for the entire life of a research project, from planning and realization of design to the integration of your results into the scientific literature. A must-have resource for current and future researchers who want to learn about the properties of their designs before they implement them Includes a library of the most common designs in the social sciences Provides a complete declaration of the canonical design for each library entry, describes the circumstances under which the design can be strong or weak, and explores the consequences of the choices under the research designer’s control Accompanied by online resources that can be used in conjunction with the book An ideal textbook for graduate students and advanced undergraduates

The wealth derived from natural resources can have a tremendous impact on the economics and politics of producing countries. In the last quarter century, we have seen the surprising and sobering consequences of this wealth, producing what is now known as the "resource curse." Countries with large endowments of natural resources, such as oil and gas, often do worse than their poorer neighbors. Their resource wealth frequently leads to lower growth rates, greater volatility, more corruption, and, in extreme cases, devastating civil wars.

In this volume, leading economists, lawyers, and political scientists address the fundamental channels generated by this wealth and examine the major decisions a country must make when faced with an abundance of a natural resource. They identify such problems as asymmetric bargaining power, limited access to information, the failure to engage in long-term planning, weak institutional structures, and missing mechanisms of accountability. They also provide a series of solutions, including recommendations for contracting with oil companies and allocating revenue; guidelines for negotiators; models for optimal auctions; and strategies to strengthen state-society linkages and public accountability.

The contributors show that solutions to the resource curse do exist; yet, institutional innovations are necessary to align the incentives of key domestic and international actors, and this requires fundamental political changes and much greater levels of transparency than currently exist. It is becoming increasingly clear that past policies have not provided the benefits they promised. "Escaping the Resource Curse" lays out a path for radically improving the management of the world's natural resources.

There is a growing consensus in the social sciences on the virtues of research strategies that combine quantitative with qualitative tools of inference. Integrated Inferences develops a framework for using causal models and Bayesian updating for qualitative and mixed-methods research. By making, updating, and querying causal models, researchers are able to integrate information from different data sources while connecting theory and empirics in a far more systematic and transparent manner than standard qualitative and quantitative approaches allow. This book provides an introduction to fundamental principles of causal inference and Bayesian updating and shows how these tools can be used to implement and justify inferences using within-case (process tracing) evidence, correlational patterns across many cases, or a mix of the two. The authors also demonstrate how causal models can guide research design, informing choices about which cases, observations, and mixes of methods will be most useful for addressing any given question.

Throughout the world, voters lack access to information about politicians, government performance, and public services. Efforts to remedy these informational deficits are numerous. Yet do informational campaigns influence voter behavior and increase democratic accountability? Through the first project of the Metaketa Initiative, sponsored by the Evidence in Governance and Politics (EGAP) research network, this book aims to address this substantive question and at the same time introduce a new model for cumulative learning that increases coordination among otherwise independent researcher teams. It presents the overall results (using meta-analysis) from six independently conducted but coordinated field experimental studies, the results from each individual study, and the findings from a related evaluation of whether practitioners utilize this information as expected. It also discusses lessons learned from EGAP's efforts to coordinate field experiments, increase replication of theoretically important studies across contexts, and increase the external validity of field experimental research.