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Das politische Fußballstadion: Identitätsdiskurse und Machtkämpfe
by Albrecht Sonntag Başak Alpan Katarzyna HerdDieses Buch konzentriert sich auf das Fußballstadion als politischen Raum und untersucht, wie Stadien als Objekte und Katalysatoren des politischen Wandels betrachtet werden können. Stadien sind nicht nur funktionale Konstruktionen, die für die Austragung von Fußballspielen konzipiert wurden, sondern sie sind auch Wahrzeichen der Stadt, die als Versammlungsorte für große Gemeinschaften dienen.Die Manifestation des Politischen in Fußballstadien zeigt sich in der Unzufriedenheit, die durch Fan-Aktivismus zum Ausdruck kommt, in der Nutzung von Stadien für nationale und lokale Identitätspolitik, in Versuchen der Instrumentalisierung von Emotionen sowohl durch totalitäre als auch durch demokratische Regime, in Fangruppen bei politischen Aufständen und in der Überwachung von Fans durch E-Tickets und Sitzplatzvergabe.Dieser Sammelband vereint eine Vielzahl von Fallstudien aus den unterschiedlichsten Kontexten. Die Beiträge stammen aus den Bereichen Politikwissenschaft, Soziologie, Geschichte, Anthropologie, Humangeographie und Urbanistik. Das Buch definiert und erweitert das Verständnis der politischen Dimension des Fußballstadions neu.
Das politische System der Bundesrepublik Deutschland
by Wolfgang Rudzio Tom MannewitzDas Lehrbuch führt ein in Selbstverständnis, institutionellen Aufbau und Praxis des politischen Systems der Bundesrepublik Deutschland. Das Werk gibt einen problemorientierten Überblick über die verfassungs- und außenpolitischen Grundentscheidungen, die die deutsche Demokratie konstituiert haben; das politische Kräftefeld, das durch Interessengruppen, Bürgerinitiativen, Parteien und Massenmedien gebildet wird; die politischen Institutionen in Bund, Ländern und Kommunen; die gesellschaftliche Reichweite und administrative Durchsetzung politischer Entscheidungen; die politische Kultur einschließlich der Struktur der politischen Führungsschicht. Die Neuauflage thematisiert u.a. auch die Folgen der Corona-Krise für das politische System, das sich wandelnde Parteiensystem und die Krise der EU nach der Bundestagswahl 2021.
Das sowjetische Fieber: Fußballfans im poststalinistischen Vielvölkerreich (Soviet and Post-Soviet Politics and Society #136)
by Manfred ZellerWhat did the citizens of the Soviet Union identify with? Where did the societal faultlines lie? Did mass demonstrations destabilize Soviet order? How did informal groups come into being within a society based on uniformity? What impact did media and new forms of connection have on the development of a multinational Soviet society? What remained after the end of the Soviet Union? Using Soviet soccer teams from Moscow (Spartak, Dynamo, ZSKA) and Kiev (Dynamo) as examples, Manfred Zeller tells a story of community and enmity in the post-Stalinist empire. He analyzes the complex loyalties that governed group identities and explains phenomena like the love-hate relationship between Kiev and Moscow. 'Moscow against Kiev' in Soviet times was not a question of war and peace, but in soccer there was already a feeling of 'us against them.' Zeller's book is an important contribution to research on Soviet culture after Stalin as well as to contemporary debates on antagonism in the post-Soviet world.
Das transformative Potenzial von Konsum zwischen Nachhaltigkeit und Digitalisierung: Chancen und Risiken (Kritische Verbraucherforschung)
by Renate Hübner Barbara SchmonWelche Bedeutung hat Konsum in der gesellschaftlichen Umbruchphase zwischen Klimawandel und Digitalisierungsprozessen? Konsum im Sinne einer nachhaltigen Entwicklung zu verändern und dabei Digitalisierung für eine gesellschaftliche Transformation aktiv zu nützen, ist eng verknüpft sowohl mit einem veränderten Konsumverständnis, als auch mit teilweise neuen Rollen der Akteure. Der vorliegende Band bietet Zugänge, wie durch nachhaltigkeitsorientierte Forschung und Bildung diese neuen Rollen und das damit verbundene transformative Potenzial von Konsum sichtbar und nutzbar gemacht werden können. Diese Zielsetzung ist verbunden mit dem Anliegen, VerbraucherInnen zu unterstützen, Konsumformen im Sinne einer Circular Economy zu praktizieren und Konsumhandlungen auch abseits marktvermittelter Lösungen stärker zu berücksichtigen.Der Inhalt• Nachhaltiger Konsum als Utopie? Wie kann Konsum transformative Kraft entwickeln? Soziale Praktiken ändern, nicht die Individuen! Über die transformativen Potenziale des „Scheiterns“• „Smarte“ Konsumwende und „Smarte“ Technologien? Chancen und Grenzen der Digitalisierung für klimafreundlichen Konsum• on/offline: Digitalisierung zwischen diskursivem politischen Konsum und dem pathogenen Potenzial von Konsum• Verbraucherbildung im und für Wandel: Normative, konzeptionelle und curriculare Transformationen im Spiegel konsumgesellschaftlicher Entwicklungen und das Problem der Grenzziehungen in heterogenen Klassen• Wie können Konferenzen durch ihre Gestaltung transformativ wirken?Die Herausgeberinnen Dr. Renate Hübner ist Nachhaltigkeitsforscherin und leitet den Studienbereich Nachhaltige Entwicklung an der Alpen-Adria-Universität Klagenfurt.Dr. Barbara Schmon ist Expertin für „Nachhaltigen Konsum“ im österreichischen Bundesministerium für Nachhaltigkeit und Tourismus (BMNT) in Wien.
Das vergessene Subjekt: Subjektkonstitutionen in mediatisierten Alltagswelten (Medien • Kultur • Kommunikation)
by Jeffrey Wimmer Friedrich Krotz Rainer Winter Peter GentzelDer Band liefert eine kritische Bestandsaufnahme bestehender Subjektkonzeptionen der kommunikationswissenschaftlichen Forschung. Zudem werden Konzepte entwickelt um Subjektivität im Kontext aktueller theoretischer Debatten (u.a. Mediensoziologie, Cultural Studies, Psychoanalyse, Praxistheorie, Science and Technology Studies) sowie sozialer, kultureller und technischer Entwicklungen (u.a. Digitalisierung, Mediatisierung, Mobilität und Vernetzung) analysieren zu können. Da Subjektkonzeptionen für jegliche Kommunikations- und Medienanalysen von zentraler Bedeutung sind, schließt der Band eine zentrale Leerstelle der Kommunikations- und Medienwissenschaft.
Daseinsvorsorge und Gemeinwesen im ländlichen Raum
by Frieder Dünkel Michael Herbst Benjamin StahlIn entlegenen, ländlichen Räumen ergeben sich komplexe Problemlagen, die eine interdisziplinäre Erforschung notwendig machen. Der vorliegende Band beleuchtet mit Hilfe unterschiedlichster Fachdisziplinen neue Ansätze für die Daseinsvorsorge und das Gemeinwesen. Beteiligt sind Sozial-/Wirtschaftsgeografie und Ökologie, Soziologie, Politikwissenschaft, Agrarwissenschaften, Gesundheitswissenschaften, Psychiatrie, Theologie, Kriminologie und Präventionswissenschaft.
Data Analysis Using SAS Enterprise Guide
by Lawrence S. Meyers Glenn Gamst A. J. GuarinoThis book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.
Data Analysis Using SAS Enterprise Guide
by Lawrence S. Meyers Glenn Gamst A. J. Guarino Lawrence S. Meyers Glenn GamstThis book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.
Data Analysis and Classification: Methods and Applications (Studies in Classification, Data Analysis, and Knowledge Organization)
by Krzysztof Jajuga Marek Walesiak Krzysztof NajmanThis volume gathers peer-reviewed contributions that address a wide range of recent developments in the methodology and applications of data analysis and classification tools in micro and macroeconomic problems. The papers were originally presented at the 29th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2020, held in Sopot, Poland, September 7–9, 2020. Providing a balance between methodological contributions and empirical papers, the book is divided into five parts focusing on methodology, finance, economics, social issues and applications dealing with COVID-19 data. It is aimed at a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.
Data Analysis for Social Science: A Friendly and Practical Introduction
by Kosuke Imai Elena LlaudetAn ideal textbook for an introductory course on quantitative methods for social scientists—assumes no prior knowledge of statistics or coding <p><p>Data Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Using plain language and assuming no prior knowledge of statistics and coding, the book provides a step-by-step guide to analyzing real-world data with the statistical program R for the purpose of answering a wide range of substantive social science questions. It teaches not only how to perform the analyses but also how to interpret results and identify strengths and limitations. This one-of-a-kind textbook includes supplemental materials to accommodate students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose. <p><p>Analyzes real-world data using the powerful, open-sourced statistical program R, which is free for everyone to use <p><p>Teaches how to measure, predict, and explain quantities of interest based on data <p><p>Shows how to infer population characteristics using survey research, predict outcomes using linear models, and estimate causal effects with and without randomized experiments <p><p>Assumes no prior knowledge of statistics or coding <p><p>Specifically designed to accommodate students with a variety of math backgrounds <p><p>Provides cheatsheets of statistical concepts and R code <p><p>Supporting materials available online, including real-world datasets and the code to analyze them, plus—for instructor use—sample syllabi, sample lecture slides, additional datasets, and additional exercises with solutions <p><p>Looking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.
Data Analysis for Social Science: A Friendly and Practical Introduction
by Kosuke Imai Elena LlaudetAn ideal textbook for complete beginners—teaches from scratch R, statistics, and the fundamentals of quantitative social scienceData Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Assuming no prior knowledge of statistics and coding and only minimal knowledge of math, the book teaches the fundamentals of survey research, predictive models, and causal inference while analyzing data from published studies with the statistical program R. It teaches not only how to perform the data analyses but also how to interpret the results and identify the analyses&’ strengths and limitations.Progresses by teaching how to solve one kind of problem after another, bringing in methods as needed. It teaches, in this order, how to (1) estimate causal effects with randomized experiments, (2) visualize and summarize data, (3) infer population characteristics, (4) predict outcomes, (5) estimate causal effects with observational data, and (6) generalize from sample to population.Flips the script of traditional statistics textbooks. It starts by estimating causal effects with randomized experiments and postpones any discussion of probability and statistical inference until the final chapters. This unconventional order engages students by demonstrating from the very beginning how data analysis can be used to answer interesting questions, while reserving more abstract, complex concepts for later chapters.Provides a step-by-step guide to analyzing real-world data using the powerful, open-source statistical program R, which is free for everyone to use. The datasets are provided on the book&’s website so that readers can learn how to analyze data by following along with the exercises in the book on their own computer.Assumes no prior knowledge of statistics or coding.Specifically designed to accommodate students with a variety of math backgrounds. It includes supplemental materials for students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.Provides cheatsheets of statistical concepts and R code.Comes with instructor materials (upon request), including sample syllabi, lecture slides, and additional replication-style exercises with solutions and with the real-world datasets analyzed. Looking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.
Data Analysis for the Social Sciences: Integrating Theory and Practice
by Professor Douglas Bors'This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.' —Ruth Horry, Psychology, Swansea University 'This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.' —Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbers Calculate probability and value distributions Test a priori and post hoc hypotheses Conduct Chi-squared tests and observational studies Structure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.
Data Analysis for the Social Sciences: Integrating Theory and Practice
by Professor Douglas Bors'This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.' —Ruth Horry, Psychology, Swansea University 'This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.' —Barbra Teater, Social Work, College of Staten Island, City University of New York Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability. It shows you how to: Describe data with graphs, tables, and numbers Calculate probability and value distributions Test a priori and post hoc hypotheses Conduct Chi-squared tests and observational studies Structure ANOVA, ANCOVA, and factorial designs Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.
Data Analysis in Business Research: A Step-By-Step Nonparametric Approach (Response Books)
by D IsraelWhile there are books focusing on parametric tests, the domain of nonparametric tests is mostly unexplored. Data Analysis in Business Research: A Step by Step Nonparametric Approach brings under one umbrella all the major nonparametric statistical tools that can be used by undergraduate and postgraduate students of all disciplines, especially students of Research Methods in Social Sciences and Management Studies, in their dissertation work. Students face difficulty in analyzing data collected from small samples; they end up reporting mere percentage analysis which results in the loss of information collected. Hence there is a need to create awareness among students and researchers about the application of major nonparametric tools that can be applied confidently without worrying about sample size, scale of measurement, normality assumptions or other parameters of that nature. The lucid presentation of the step-by-step procedures, explaining in simple English how to perform each of the major nonparametric tests, is a major attraction of the book. The book, which also has a comprehensive question bank, assumes minimal or little knowledge of statistics on the part of the reader. This book will also be informative for Marketing Research professionals and organisations, consultancies and organisations of economic research.
Data Analysis in Management with SPSS Software
by J. P. VermaThis book provides readers with a greater understanding of a variety of statistical techniques along with the procedure to use the most popular statistical software package SPSS. It strengthens the intuitive understanding of the material, thereby increasing the ability to successfully analyze data in the future. The book provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of research problems. This book focuses on providing readers with the knowledge and skills needed to carry out research in management, humanities, social and behavioural sciences by using SPSS.
Data Analysis with Machine Learning for Psychologists: Crash Course to Learn Python 3 and Machine Learning in 10 hours
by Chandril GhoshThe power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of research publications using machine learning in some of the top journals in combination with an increasing number of academic recruiters within psychology asking for Python knowledge from applicants indicates a growing demand for these skills in the market. While there are plenty of books covering data science, rarely, if ever, books in the market address the need of social science students with no computer science background. They are typically written by engineers or computer scientists for people of their discipline. As a result, often such books are filled with technical jargon and examples irrelevant to psychological studies or projects. In contrast, this book was written by a psychologist in a simple, easy-to-understand way that is brief and accessible. The aim for this book was to make the learning experience on this topic as smooth as possible for psychology students/researchers with no background in programming or data science. Completing this book will also open up an enormous amount of possibilities for quantitative researchers in psychological science, as it will enable them to explore newer types of research questions.
Data Analysis: A Model Comparison Approach To Regression, ANOVA, and Beyond, Third Edition
by Charles M. Judd Carey S. Ryan Gary H. McClellandData Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model. Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach. Highlights of the third edition include: a new chapter on logistic regression; expanded treatment of mixed models for data with multiple random factors; updated examples; an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.
Data Analysis: A Model Comparison Approach, Second Edition
by Charles M. Judd Carey S. Ryan Gary H. McclellandThis completely rewritten classic text features many new examples, insights and topics including mediational, categorical, and multilevel models. Substantially reorganized, this edition provides a briefer, more streamlined examination of data analysis. Noted for its model-comparison approach and unified framework based on the general linear model, the book provides readers with a greater understanding of a variety of statistical procedures. This consistent framework, including consistent vocabulary and notation, is used throughout to develop fewer but more powerful model building techniques. The authors show how all analysis of variance and multiple regression can be accomplished within this framework. The model-comparison approach provides several benefits: It strengthens the intuitive understanding of the material thereby increasing the ability to successfully analyze data in the future It provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of questions It reduces the number of statistical techniques that must be memorized It teaches readers how to become data analysts instead of statisticians. The book opens with an overview of data analysis. All the necessary concepts for statistical inference used throughout the book are introduced in Chapters 2 through 4. The remainder of the book builds on these models. Chapters 5 - 7 focus on regression analysis, followed by analysis of variance (ANOVA), mediational analyses, non-independent or correlated errors, including multilevel modeling, and outliers and error violations. The book is appreciated by all for its detailed treatment of ANOVA, multiple regression, nonindependent observations, interactive and nonlinear models of data, and its guidance for treating outliers and other problematic aspects of data analysis. Intended for advanced undergraduate or graduate courses on data analysis, statistics, and/or quantitative methods taught in psychology, education, or other behavioral and social science departments, this book also appeals to researchers who analyze data. A protected website featuring additional examples and problems with data sets, lecture notes, PowerPoint presentations, and class-tested exam questions is available to adopters. This material uses SAS but can easily be adapted to other programs. A working knowledge of basic algebra and any multiple regression program is assumed.
Data Analytics and Digital Transformation (Business and Digital Transformation)
by Erik Beulen Marla A. DansUnderstanding the significance of data analytics is paramount for digital transformation but in many organizations they are separate units without fully aligned goals. As organizations are applying digital transformations to be adaptive and agile in a competitive environment, data analytics can play a critical role in their success. This book explores the crossroads between them and how to leverage their connection for improved business outcomes. The need to collaborate and share data is becoming an integral part of digital transformation. This not only creates new opportunities but also requires well-considered and continuously assessed decision-making as competitiveness is at stake. This book details approaches, concepts, and frameworks, as well as actionable insights and good practices, including combined data management and agile concepts. Critical issues are discussed such as data quality and data governance, as well as compliance, privacy, and ethics. It also offers insights into how both private and public organizations can innovate and keep up with growing data volumes and increasing technological developments in the short, mid, and long term. This book will be of direct appeal to global researchers and students across a range of business disciplines, including technology and innovation management, organizational studies, and strategic management. It is also relevant for policy makers, regulators, and executives of private and public organizations looking to implement successful transformation policies.
Data Augmentation, Labelling, and Imperfections: Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings (Lecture Notes in Computer Science #13567)
by Hien V. Nguyen Yuan Xue Sharon X. HuangThis book constitutes the refereed proceedings of the Second MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022.DALI 2022 accepted 12 papers from the 22 submissions that were reviewed. The papers focus on rigorous study of medical data related to machine learning systems.
Data Baby: My Life in a Psychological Experiment
by Susannah BreslinA Belletrist Book Pick for December 2023Lab Girl meets Brain on Fire in this provocative and poignant memoir delving into a woman's formative experiences as a veritable "lab rat" in a lifelong psychological study, and her pursuit to reclaim autonomy and her identity as a adult. What if your parents turn you into a human lab rat when you&’re a child? Will that change the story of your life? Will that change who you are? When Susannah Breslin is a toddler, her parents enroll her in an exclusive laboratory preschool at the University of California, Berkeley, where she becomes one of over a hundred children who are research subjects in an unprecedented thirty-year study of personality development that predicts who she and her cohort will grow up to be. Decades later, trapped in what she feels is an abusive marriage and battling breast cancer, she starts to wonder how growing up under a microscope shaped her identity and life choices. Already a successful journalist, she makes her own curious history the subject of her next investigation. From experiment rooms with one-way mirrors, to children&’s puzzles with no solutions, to condemned basement laboratories, her life-changing journey uncovers the long-buried secrets hidden behind the renowned study. The question at the gnarled heart of her quest: Did the study know her better than she knew herself? At once bravely honest and sharply witty, Data Baby is a compelling and provocative account of a woman&’s quest to find her true self, and an unblinking exploration of why we turn out as we do. Few people in all of history have been studied from such a young age and for as long as this author, but the message of her book is universal. In an era when so many of us are looking to technology to tell us who to be, it&’s up to us to discover who we actually are.
Data Collection in Fragile States: Innovations from Africa and Beyond
by Johannes Hoogeveen Utz Pape‘This open access book addresses an urgent issue on which little organized information exists. It reflects experience in Africa but is highly relevant to other fragile states as well.’ —Constantine Michalopoulos, John Hopkins University, USA and former Director of Economic Policy and Co-ordination at the World BankFragile countries face a triple data challenge. Up-to-date information is needed to deal with rapidly changing circumstances and to design adequate responses. Yet, fragile countries are among the most data deprived, while collecting new information in such circumstances is very challenging. This open access book presents innovations in data collection developed with decision makers in fragile countries in mind. Looking at innovations in Africa from mobile phone surveys monitoring the Ebola crisis, to tracking displaced people in Mali, this collection highlights the challenges in data collection researchers face and how they can be overcome.
Data Collection in Sociolinguistics: Methods and Applications
by Gerard Van Herk Christine Mallinson Becky ChildsThis edited volume provides up-to-date, succinct, relevant, and informative discussion about methods of data collection in sociolinguistic research. It covers the main areas of research design, conducting research, and sharing data findings with longer chapters and shorter vignettes written by a range of top sociolinguists, both veteran and emerging scholars. Here is the one-stop, go-to guide for the numerous quantitative, qualitative, and mixed methods that are used in sociolinguistic research, ensuring that Data Collection in Sociolinguistics will be not only useful in the classroom but also as a reference tool for active researchers. For more information, visit sociolinguisticdatacollection.com.
Data Conscience: Algorithmic Siege on our Humanity
by Brandeis Hill MarshallDATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of “move fast and break things” is, itself, broken, and requires change. You’ll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression A can’t-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with: Discussions of the importance of transparency Explorations of computational thinking in practice Strategies for encouraging accountability in tech Ways to avoid double-edged data visualization Schemes for governing data structures with law and algorithms
Data Culture: Develop An Effective Data-Driven Organization
by Dr Shorful IslamOrganizations often start their data journey by either procuring the technology or hiring the people. However, without an effective data-driven culture in place, they can struggle to derive value from their investments.Data Culture explores how data leaders can develop and nurture a data-driven culture tailored to their organization's needs. It outlines the types of data leadership and teams needed and the key building blocks for success, such as team recruitment, building and training, leadership, process, behavioural change management, developing, sustaining and measuring a data culture, company values and everyday decision making. It also explores the nuances of how different types of data cultures work with different types of companies, what to avoid and the differences between building a data culture from scratch and changing an existing data culture from within.With this hands-on guide, senior data leader Shorful Islam takes readers through how to successfully establish or change a data culture, sharing his expertise in behavioural change psychology and two decades of experience in fostering data culture in organizations. Supported throughout by real-world examples and cases, this will be an essential read for all data leaders and anyone involved in developing a data-driven organizational culture.