Sean Gailmard – författare

Although their leaders and staff are not elected, bureaucratic agencies have the power to make policy decisions that carry the full force of the law. In this groundbreaking book, Sean Gailmard and John W. Patty explore an issue central to political science and public administration: How do Congress and the president ensure that bureaucratic agencies implement their preferred policies? The assumption has long been that bureaucrats bring to their positions expertise, which must then be marshaled to serve the interests of a particular policy. In "Learning While Governing", Gailmard and Patty overturn this conventional wisdom, showing instead that much of what bureaucrats need to know to perform effectively is learned on the job. Bureaucratic expertise, they argue, is a function of administrative institutions and interactions with political authorities that collectively create an incentive for bureaucrats to develop expertise. The challenge for elected officials is therefore to provide agencies with the autonomy to do so while making sure they do not stray significantly from the administration's course.To support this claim, the authors analyze several types of information-management processes. "Learning While Governing" speaks to an issue with direct bearing on power relations between Congress, the president, and the executive agencies, and it will be a welcome addition to the literature on bureaucratic development.

To understand the foundations of American political institutions, it's necessary to understand the rationale for British colonial institutions that survived the empire. Political institutions in England's American colonies were neither direct imports from England, nor home-grown creations of autonomous colonists. Instead, they emerged from efforts of the English Crown to assert control over their colonies amid limited English state and military capacity. Agents of Empire explores the strategic dilemmas facing a constrained crown in its attempts to assert control. The study argues that colonial institutions emerged from the crown's management of authority delegated to agents-first companies and proprietors establishing colonies; then imperial officials governing the polities they created. The institutions remaining from these strategic dynamics form the building blocks of federalism, legislative power, separation of powers, judicial review, and other institutions that comprise the American polity today.

To understand the foundations of American political institutions, it's necessary to understand the rationale for British colonial institutions that survived the empire. Political institutions in England's American colonies were neither direct imports from England, nor home-grown creations of autonomous colonists. Instead, they emerged from efforts of the English Crown to assert control over their colonies amid limited English state and military capacity. Agents of Empire explores the strategic dilemmas facing a constrained crown in its attempts to assert control. The study argues that colonial institutions emerged from the crown's management of authority delegated to agents-first companies and proprietors establishing colonies; then imperial officials governing the polities they created. The institutions remaining from these strategic dynamics form the building blocks of federalism, legislative power, separation of powers, judicial review, and other institutions that comprise the American polity today.

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist''s toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist''s toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.