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Understanding Context
To make sense of the world, we’re always trying to place things in context, whether our environment is physical, cultural, or something else altogether. Now that we live among digital, always-networked products, apps, and places, context is more complicated than ever—starting with "where" and "who" we are. This practical, insightful book provides a powerful toolset to help information architects, UX professionals, and web and app designers understand and solve the many challenges of contextual ambiguity in the products and services they create. You’ll discover not only how to design for a given context, but also how design participates in making context. Learn how people perceive context when touching and navigating digital environments See how labels, relationships, and rules work as building blocks for context Find out how to make better sense of cross-channel, multi-device products or services Discover how language creates infrastructure in organizations, software, and the Internet of Things Learn models for figuring out the contextual angles of any user experience
A First Course in Causal Inference
The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. This textbook, based on the author's course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference, and reviews basic probability and statistics in the appendix. It covers causal inference from a statistical perspective and includes examples and applications from biostatistics and econometrics. Key Features: All R code and data sets available at Harvard Dataverse. Solutions manual available for instructors. Includes over 100 exercises. This book is suitable for an advanced undergraduate or graduate-level course on causal inference, or postgraduate and PhD-level course in statistics and biostatistics departments.
Transforming Criminal Justice
"America's criminal justice system requires reform, but those efforts too often rest on anecdotes or assumptions. Drawing on the contributions of America's top justice researchers, this compendium provides an evidence-based blueprint to guide the movement toward criminal justice reform"--
Causality in a Social World
Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with conventional analysis methods. Whilst highlighting the crucial role of good research design and the evaluation of assumptions required for identifying causal effects in the context of each application, the author demonstrates that improved statistical procedures will greatly enhance the empirical study of causal relationship theory. Applications focus on interventions designed to improve outcomes for participants who are embedded in social settings, including families, classrooms, schools, neighbourhoods, and workplaces.
Experimental Thinking
Novel collection of essays addressing contemporary trends in political science from a broad spectrum of interdisciplinary scholars.
The Routledge Companion to Intersectionalities
The Routledge Companion to Intersectionalities is a dynamic reference source to the key contemporary analytic in feminist thought: intersectionality. Comprising over 50 chapters by a diverse, international, and interdisciplinary team of contributors, the Companion is divided into nine parts: Retracing intersectional genealogies Intersectional methods and (inter)disciplinarity Intersectionality’s travels Intersectional borderwork Trans* intersectionalities Disability and intersectional embodiment Intersectional science and data studies Popular culture at the intersections Rethinking intersectional justice This accessibly written collection is essential reading for students, teachers, and researchers working in women’s and gender studies, sexuality studies, African American studies, sociology, politics, and other related subjects from across the humanities and social sciences.
Quantifying Aesthetics of Visual Design Applied to Automatic Design
In this thesis, the author makes several contributions to the study of design of graphical materials. The thesis begins with a review of the relationship between design and aesthetics, and the use of mathematical models to capture this relationship. Then, a novel method for linking linguistic concepts to colors using the Latent Dirichlet Allocation Dual Topic Model is proposed. Next, the thesis studies the relationship between aesthetics and spatial layout by formalizing the notion of visual balance. Applying principles of salience and Gaussian mixture models over a body of about 120,000 aesthetically rated professional photographs, the author provides confirmation of Arnhem's theory about spatial layout. The thesis concludes with a description of tools to support automatically generating personalized design.
Veridical Data Science
Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to...
Handbook of Matching and Weighting Adjustments for Causal Inference
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
The Politics of Legislative Debates
Legislative debates make democracy and representation work. Political actors engage in legislative debates to make their voice heard to voters. Parties use debates to shore up their brand. This book makes the most comprehensive study of legislative debates thus far, looking at the politics of legislative debates in 33 liberal democracies in Europe, North America and Latin America, Africa, Asia, and Oceania. The book begins with theoretical chapters focused on the key concepts in the study of legislative debates. Michael Laver, Slapin and Proksch, and Taylor examine the politics of legislative debates in parliamentary and presidential democracies. Subsequently, Goplerud makes a critical revie...
