Computational Social Sciences - Böcker

Two billion people around the world use Instagram, but so far social scientists have done little research on the platform. Despite Instagram's reputation for shallowness, the ongoing self-presentation it demands confronts users with profound dilem...

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Learn how to conduct a robust text analysis project from start to finish--and then do it again. Mining is the dominant metaphor in computational text analysis. When mining texts, the implied assumption is that analysts can find kernels of truth--t...

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Learn how to conduct a robust text analysis project from start to finish--and then do it again. Mining is the dominant metaphor in computational text analysis. When mining texts, the implied assumption is that analysts can find kernels of truth--t...

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This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolve...

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Quantitative social scientists use survival analysis to understand the forces that determine the duration of events. This Element provides a guideline to new techniques and models in survival analysis, particularly in three areas: non-proportional...

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This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill an...

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In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other tha...

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The accumulation of empirical evidence that has been collected in multiple contexts, places, and times requires a more comprehensive understanding of empirical research than is typically required for interpreting the findings from individual studi...

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In this Element, which continues our discussion in Foundations, the authors provide an accessible and practical guide for the analysis and interpretation of Regression Discontinuity (RD) designs that encourages the use of a common set of practices...

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In this Element, which continues our discussion in Foundations, the authors provide an accessible and practical guide for the analysis and interpretation of Regression Discontinuity (RD) designs that encourages the use of a common set of practices...

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The accumulation of empirical evidence that has been collected in multiple contexts, places, and times requires a more comprehensive understanding of empirical research than is typically required for interpreting the findings from individual studi...

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In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other tha...

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This Element introduces the basics of Bayesian regression modeling using modern computational tools. This Element only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill an...

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The rise of the internet and mobile telecommunications has created the possibility of using large datasets to understand behavior at unprecedented levels of temporal and geographic resolution. Online social networks attract the most users, though ...

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In this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, Nicolás¿¿Idrobo, and Roc?ìo Titiunik provide an accessible and practical guide for the analysis and int...

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In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influenc...

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This Element discusses how shiny, an R package, can help instructors teach quantitative methods more effectively by way of interactive web apps. The interactivity increases instructors' effectiveness by making students more active participants in ...

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We elaborate a general workflow of weighting-based survey inference, decomposing it into two main tasks. The first is the estimation of population targets from one or more sources of auxiliary information. The second is the construction of weights...

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Social interactions are rich, complex, and dynamic. One way to understand these is to model interactions that fascinate us. Some of the more realistic and powerful models are computer simulations. Simple, elegant and powerful, tools are available ...

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Building on the Cambridge Element Agent Based Models of Social Life: Fundamentals (Cambridge, 2020), we move on to the next level. We do this by building agent based models of polarization and ethnocentrism. In the process, we develop: stochastic ...

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The goal of this Element is to provide a detailed introduction to adaptive inventories, an approach to making surveys adjust to respondents' answers dynamically. This method can help survey researchers measure important latent traits or attitudes ...

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Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in...

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In discrete choice models the relationships between the independent variables and the choice probabilities are nonlinear, depending on both the value of the particular independent variable being interpreted and the values of the other independent ...

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Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned ...

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Text contains a wealth of information about about a wide variety of sociocultural constructs. Automated prediction methods can infer these quantities (sentiment analysis is probably the most well-known application). However, there is virtually no ...

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Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. ...

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Computational Approaches to Studying the Co-evolution of Networks and Behaviour in Social Dilemmas shows students, researchers, and professionals how to use computation methods, rather than mathematical analysis, to answer research questions for a...

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This book is organized in two parts: the first part introduces the reader to all the concepts, tools and references that are required to start conducting research in behavioral computational social science. The methodological reasons for integrati...

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Political Science has traditionally employed empirical research and analytical resources to understand, explain and predict political phenomena. One of the long-standing criticisms against empirical modeling targets the static perspective provided...

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Geo-mathematical modelling: models from complexity science ¿ Sir Alan Wilson, Centre for Advanced Spatial Analysis, University College London ¿ Mathematical and computer models for a complexity science tool kit ¿ Geographical systems are character...

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