Methodological Tools in the Social Sciences - Böcker

Experiments have gained prominence in sociology in recent years. Increased interest in testing causal theories through experimental designs has ignited a debate about which experimental designs can facilitate scientific progress in sociology. This book discusses the implications of research interests for the design of experiments, identifies points of commonality and disagreement among the different perspectives within sociology, and elaborates on the rationales of each. It helps experimental sociologists find appropriate designs for answering specific research questions while alerting them to the challenges. Offering more than just a guide, this book explores both the historical roots of experimental sociology and the cutting-edge techniques of rigorous sociology. It concludes with a tantalizing peek into the future and provides a roadmap to the exciting prospects and uncharted territories of experimental sociology.

Progress in the social sciences entails developing and improving theoretical understanding of social phenomena and improving methods for collecting and analyzing data. Theories organize what we know or expect to learn about phenomena and methods provide the evidential basis for the theories. While we have witnessed great strides in the development of statistical methods, there is less information for developing theories. In Developing Theories in the Social Sciences, Jane Sell and Murray Webster Jr. describe an approach for logical, consistent, and useful explanatory theories for social scientists. They emphasize properly defining concepts to embed in theoretical propositions, while providing guidelines for avoiding missteps that can occur, including imprecise definitions, incomplete assumptions, and missing scope conditions. Offering examples from different disciplines, the authors propose a structured method vital for building and refining theories about social phenomena.

Progress in the social sciences entails developing and improving theoretical understanding of social phenomena and improving methods for collecting and analyzing data. Theories organize what we know or expect to learn about phenomena and methods provide the evidential basis for the theories. While we have witnessed great strides in the development of statistical methods, there is less information for developing theories. In Developing Theories in the Social Sciences, Jane Sell and Murray Webster Jr. describe an approach for logical, consistent, and useful explanatory theories for social scientists. They emphasize properly defining concepts to embed in theoretical propositions, while providing guidelines for avoiding missteps that can occur, including imprecise definitions, incomplete assumptions, and missing scope conditions. Offering examples from different disciplines, the authors propose a structured method vital for building and refining theories about social phenomena.

In recent decades, game theory has been extensively used in academic research throughout the social sciences, including international relations. The typical format of applied game theoretic journal articles is theorem-proof, but while the proof demonstrates that the theorem is true, it doesn't typically show how the researcher actually "discovered" the theorem. Ahmer Tarar's Game-Theoretic Models of International Crisis Bargaining explains how to derive the equilibria of (sometimes complicated) game-theoretic models. Examining central results on international crisis bargaining, using a unified modeling framework, he presents simplified versions of important published game-theoretic models in international conflict to demonstrate how to construct and solve game-theoretic models for academic research. He provides detailed derivations for each result, presenting a proposition summarizing the entire equilibrium strategy profile. With over 300 exercises, ranging from easy to difficult, Tarar provides readers with extensive practice for honing their skills and becoming skilled modelers.

In recent decades, game theory has been extensively used in academic research throughout the social sciences, including international relations. The typical format of applied game theoretic journal articles is theorem-proof, but while the proof demonstrates that the theorem is true, it doesn't typically show how the researcher actually "discovered" the theorem. Ahmer Tarar's Game-Theoretic Models of International Crisis Bargaining explains how to derive the equilibria of (sometimes complicated) game-theoretic models. Examining central results on international crisis bargaining, using a unified modeling framework, he presents simplified versions of important published game-theoretic models in international conflict to demonstrate how to construct and solve game-theoretic models for academic research. He provides detailed derivations for each result, presenting a proposition summarizing the entire equilibrium strategy profile. With over 300 exercises, ranging from easy to difficult, Tarar provides readers with extensive practice for honing their skills and becoming skilled modelers.

The uncertainty that researchers face in specifying their estimation model threatens the validity of their inferences. In regression analyses of observational data, the 'true model' remains unknown, and researchers face a choice between plausible alternative specifications. Robustness testing allows researchers to explore the stability of their main estimates to plausible variations in model specifications. This highly accessible book presents the logic of robustness testing, provides an operational definition of robustness that can be applied in all quantitative research, and introduces readers to diverse types of robustness tests. Focusing on each dimension of model uncertainty in separate chapters, the authors provide a systematic overview of existing tests and develop many new ones. Whether it be uncertainty about the population or sample, measurement, the set of explanatory variables and their functional form, causal or temporal heterogeneity, or effect dynamics or spatial dependence, this book provides guidance and offers tests that researchers from across the social sciences can employ in their own research.

The radical interdependence between humans who live together makes virtually all human behavior conditional. The behavior of individuals is conditional upon the expectations of those around them, and those expectations are conditional upon the rules (institutions) and norms (culture) constructed to monitor, reward, and punish different behaviors. As a result, nearly all hypotheses about humans are conditional - conditional upon the resources they possess, the institutions they inhabit, or the cultural practices that tell them how to behave. Interaction Models provides a stand-alone, accessible overview of how interaction models, which are frequently used across the social and natural sciences, capture the intuition behind conditional claims and context dependence. It also addresses the simple specification and interpretation errors that are, unfortunately, commonplace. By providing a comprehensive and unified introduction to the use and critical evaluation of interaction models, this book shows how they can be used to test theoretically-derived claims of conditionality.

Understanding change over time is a critical component of social science. However, data measured over time - time series - requires their own set of statistical and inferential tools. In this book, Suzanna Linn, Matthew Lebo, and Clayton Webb explain the most commonly used time series models and demonstrate their applications using examples. The guide outlines the steps taken to identify a series, make determinations about exogeneity/endogeneity, and make appropriate modelling decisions and inferences. Detailing challenges and explanations of key techniques not covered in most time series textbooks, the authors show how navigating between data and models, deliberately and transparently, allows researchers to clearly  explain their statistical analyses to a broad audience.

The uncertainty that researchers face in specifying their estimation model threatens the validity of their inferences. In regression analyses of observational data, the 'true model' remains unknown, and researchers face a choice between plausible alternative specifications. Robustness testing allows researchers to explore the stability of their main estimates to plausible variations in model specifications. This highly accessible book presents the logic of robustness testing, provides an operational definition of robustness that can be applied in all quantitative research, and introduces readers to diverse types of robustness tests. Focusing on each dimension of model uncertainty in separate chapters, the authors provide a systematic overview of existing tests and develop many new ones. Whether it be uncertainty about the population or sample, measurement, the set of explanatory variables and their functional form, causal or temporal heterogeneity, or effect dynamics or spatial dependence, this book provides guidance and offers tests that researchers from across the social sciences can employ in their own research.

The radical interdependence between humans who live together makes virtually all human behavior conditional. The behavior of individuals is conditional upon the expectations of those around them, and those expectations are conditional upon the rules (institutions) and norms (culture) constructed to monitor, reward, and punish different behaviors. As a result, nearly all hypotheses about humans are conditional - conditional upon the resources they possess, the institutions they inhabit, or the cultural practices that tell them how to behave. Interaction Models provides a stand-alone, accessible overview of how interaction models, which are frequently used across the social and natural sciences, capture the intuition behind conditional claims and context dependence. It also addresses the simple specification and interpretation errors that are, unfortunately, commonplace. By providing a comprehensive and unified introduction to the use and critical evaluation of interaction models, this book shows how they can be used to test theoretically-derived claims of conditionality.

Understanding change over time is a critical component of social science. However, data measured over time - time series - requires their own set of statistical and inferential tools. In this book, Suzanna Linn, Matthew Lebo, and Clayton Webb explain the most commonly used time series models and demonstrate their applications using examples. The guide outlines the steps taken to identify a series, make determinations about exogeneity/endogeneity, and make appropriate modelling decisions and inferences. Detailing challenges and explanations of key techniques not covered in most time series textbooks, the authors show how navigating between data and models, deliberately and transparently, allows researchers to clearly  explain their statistical analyses to a broad audience.

A vast literature exists on theories of public opinion - how to measure, analyze, predict, and influence it; however, there is no synthesis of best practices for interpreting public opinion: existing knowledge is disparate and spread across many disciplines. Polls, Pollsters, and Public Opinion presents a systematic analytical approach for understanding, predicting, and engaging public opinion. It tells the story through the eyes of the pollster and draws an analytical road map for examining public opinion, both conceptually and practically. Providing a theoretical and conceptual foundation, as well as debunking popular myths, this book delves into the science of polling, offering tools analysts can use to assess the quality of polls. It also introduces methods that can be used to predict elections and other socio-political outcomes while understanding the nuances of messaging, engaging, and moving public opinion.

Survey research is in a state of crisis. People have become less willing to respond to polls and recent misses in critical elections have undermined the field's credibility. Pollsters have developed many tools for dealing with the new environment, an increasing number of which rely on risky opt-in samples. Virtually all of these tools require that respondents in each demographic category are a representative sample of all people in each demographic category, something that is unlikely to be reliably true. Polling at a Crossroads moves beyond such strong limitations, providing tools that work even when survey respondents are unrepresentative in complex ways. This book provides case studies that show how to avoid underestimating Trump support and how conventional polls exaggerate partisan differences. This book also helps us think in clear and sometimes counterintuitive ways and points toward simple, low-cost changes that can better address contemporary polling challenges.

Survey research is in a state of crisis. People have become less willing to respond to polls and recent misses in critical elections have undermined the field's credibility. Pollsters have developed many tools for dealing with the new environment, an increasing number of which rely on risky opt-in samples. Virtually all of these tools require that respondents in each demographic category are a representative sample of all people in each demographic category, something that is unlikely to be reliably true. Polling at a Crossroads moves beyond such strong limitations, providing tools that work even when survey respondents are unrepresentative in complex ways. This book provides case studies that show how to avoid underestimating Trump support and how conventional polls exaggerate partisan differences. This book also helps us think in clear and sometimes counterintuitive ways and points toward simple, low-cost changes that can better address contemporary polling challenges.

A vast literature exists on theories of public opinion - how to measure, analyze, predict, and influence it; however, there is no synthesis of best practices for interpreting public opinion: existing knowledge is disparate and spread across many disciplines. Polls, Pollsters, and Public Opinion presents a systematic analytical approach for understanding, predicting, and engaging public opinion. It tells the story through the eyes of the pollster and draws an analytical road map for examining public opinion, both conceptually and practically. Providing a theoretical and conceptual foundation, as well as debunking popular myths, this book delves into the science of polling, offering tools analysts can use to assess the quality of polls. It also introduces methods that can be used to predict elections and other socio-political outcomes while understanding the nuances of messaging, engaging, and moving public opinion.

The 'data revolution' offers many new opportunities for research in the social sciences. Increasingly, social and political interactions can be recorded digitally, leading to vast amounts of new data available for research. This poses new challenges for organizing and processing research data. This comprehensive introduction covers the entire range of data management techniques, from flat files to database management systems. It demonstrates how established techniques and technologies from computer science can be applied in social science projects, drawing on a wide range of different applied examples. This book covers simple tools such as spreadsheets and file-based data storage and processing, as well as more powerful data management software like relational databases. It goes on to address advanced topics such as spatial data, text as data, and network data. This book is one of the first to discuss questions of practical data management specifically for social science projects. This title is also available as Open Access on Cambridge Core.

The 'data revolution' offers many new opportunities for research in the social sciences. Increasingly, social and political interactions can be recorded digitally, leading to vast amounts of new data available for research. This poses new challenges for organizing and processing research data. This comprehensive introduction covers the entire range of data management techniques, from flat files to database management systems. It demonstrates how established techniques and technologies from computer science can be applied in social science projects, drawing on a wide range of different applied examples. This book covers simple tools such as spreadsheets and file-based data storage and processing, as well as more powerful data management software like relational databases. It goes on to address advanced topics such as spatial data, text as data, and network data. This book is one of the first to discuss questions of practical data management specifically for social science projects. This title is also available as Open Access on Cambridge Core.