Social research and statistics Books

Book SynopsisDer soziale Wandel ist allgegenwärtig und wird in der Soziologie unter verschiedenen Schlagwörtern wie Globalisierung, Beschleunigung, Individualisierung oder Entgrenzung diskutiert. Der moderne Alltag birgt deshalb zahlreiche Herausforderungen für biographische Verläufe sowie die Konstruktion von Identität und Zugehörigkeit zu sozialen Gruppen. Wie Individuen damit umgehen, ist eine zentrale Frage für die Sozialpsychologie, der damit eine wichtige Rolle für das Verständnis sozialer Wandlungsprozesse zukommt. Um dieses Potenzial zu nutzen, bedarf es einer zugleich kritischen und dynamischen Perspektive, welche nicht vorrangig auf experimenteller, sondern vor allem auf qualitativer Forschung beruht.Table of ContentsDer spätmoderne Alltag im Fokus einer lebenslauforientierten Sozialpsychologie.- Begründung und Skizzierung einer kritischen am Lebenslauf orientierten Sozialpsychologie.- Kulturelle Rahmung des modernen Alltags aus der Perspektive einer lebenslauforientierten kritischen Sozialpsychologie.- Sozialpsychologische Untersuchung nationaler Identität im Rahmen sozialer Ungleichheitsforschung.- Sozialpsychologie des modernen Alltags aus biographischer Perspektive.- Potenzial und Prozesslogik qualitativer Forschung in der Sozialpsychologie am Beispiel problemzentrierter Befragungen.

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Book SynopsisDie Dokumentarische Methode ist eine Methodologie der qualitativen Sozialforschung, die sich seit langem in der Forschungspraxis bewährt hat. In diesem Buch wird in umfassender Weise theoretisch begründet und forschungspraktisch gezeigt, wie mit der Dokumentarischen Methode Interviews ausgewertet werden. Dabei wird dem narrativen Charakter von Interviews, seien diese leitfadengestützt oder biographisch angelegt, besonders Rechnung getragen. Neben der formulierenden und reflektierenden Interpretation der Interviews geht es um deren Vergleich und um die sinn- wie soziogenetische Typenbildung. Das Buch zeigt in seiner fünften Auflage zudem wichtige Weiterführungen der dokumentarischen Interpretation narrativ fundierter Interviews auf, ist mit seinen ausführlichen Forschungsbeispielen vor allem aber eine Anleitung für die Forschungspraxis.Table of ContentsGrundzüge der dokumentarischen Methode.- Narrativ fundierte Interviews.- Die Narrationsstrukturanalyse und ihre Kritik.- Zur dokumentarischen Interpretation von Interviews.- Dokumentarische Interpretation offener Leitfadeninterviews.- Dokumentarische Interpretation biographischer Interviews.

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Book SynopsisIm vorliegenden Sammelband wird untersucht, wie die Potenziale innovativer Formen nachhaltigen Konsums – wie kollaborativer Konsums und Gemeingüter-Ökonomie, Prosuming und Produsing, Upcycling und ReUse – zur Förderung einer nachhaltigen Entwicklung entfaltet werden können. Neben Vorschlägen zur Systematisierung der Vielzahl relevanter Phänomene finden sich empirische Einblicke in die Entwicklung und Verbreitung verschiedener Typen von Innovationen sowie ihrer sozial-ökologischen Wirkungen. Multiplikatoren und Förderer aus Politik, Zivilgesellschaft und Finanzwesen sowie die Akteure selbst kommen zu Wort und geben einen Einblick in die gelebte Praxis ganz unterschiedlicher innovativer Ansätze für nachhaltigen Konsum.Trade Review“... Das Buch kann uneingeschränkt allen empfohlen werden, die an einem Überblick inaktuelle Entwicklungen für nachhaltigen Konsum interssiert sind.” (Contraste, Juli-August 2017)Table of ContentsPerspektiven von Förderern und Multiplikatoren.- Gelebte Praxis sozialer Innovationen nachhaltigen Konsums.

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Book Synopsis​Die Wissenschaftssoziologie Pierre Bourdieus steht im Zentrum seines soziologischen Gesamtwerkes. Die vorliegende Einführung stellt erstmalig Bourdieus wissenschaftssoziologisches Werk in systematischer Form dar und verdeutlicht den zentralen Mehrwert seiner Sozialtheorie für die moderne Wissenschaftsforschung. Es wird gezeigt, wie sich Bourdieus Wissenschaftstheorie und erkenntnistheoretische Kritik mit seiner soziologischen Analyse des wissenschaftlichen Feldes verbinden. Diese Synthese liefert die Begründung für die öffentliche und politische Intervention von Soziolog*innen und Intellektuellen.Der Inhalt• Bourdieus Erkenntnis- und Wissenschaftstheorie• Das wissenschaftliche Feld• Vom Gebrauch der Wissenschaft• Bourdieu und die moderne Wissenschaftssoziologie• Bourdieu in der WissenschaftsforschungDie Zielgruppen• Studierende und Lehrende der Soziologie, Philosophie, Kultur- und Sozialwissenschaften• Studierende und Lehrende der Wissenschaftssoziologie und Science and Technology Studies• Studierende und Lehrende der Wissenschafts- und HochschulforschungDie AutorenDr. Alexander Lenger vertritt seit März 2018 die Professur für Wirtschaftswissenschaften und Didaktik der Wirtschaftslehre an der Universität Siegen. Zuvor hat er von April 2016 bis März 2017 die Professur für Soziologie des Wissens am Karlsruher Institut für Technologie und von April bis August 2015 die Professur für Soziologie an der Universität Freiburg vertreten.Philipp Rhein ist Lehrbeauftragter am Institut für Soziologie an der Ludwig-Maximilians-Universität München und arbeitet am Institut für Sozialwissenschaftliche Forschung – ISF München. Table of ContentsEinleitung.- Bourdieus Erkenntnis- und Wissenschaftstheorie.- Das wissenschaftliche Feld.- Vom Gebrauch der Wissenschaft.- Bourdieu und die moderne Wissenschaftssoziologie.- Bourdieu in der Wissenschaftsforschung.

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Book SynopsisDie Unternehmerfamilie ist bisher kaum ein Thema der Familiensoziologie. Gerade weil mehr als Zweidrittel aller Unternehmen familiengeführt sind, erstaunt es, dass die Soziologie die Unternehmerfamilie bisher nicht als Forschungsfeld entdeckt hat. Mit diesem Buch wird sie zum Inhalt systematischer familiensoziologischer Reflexionen gemacht. Ein zentraler Ausgangspunkt der Theoriebildung und Forschung zu Unternehmerfamilien ist, dass es diese Familienform mit besonderen sozialen Herausforderungen zu tun hat, weil sich in ihr zwei soziale Sphären strukturell verbinden, vermischen und verkoppeln, die im Zuge der Modernisierung der Gesellschaft gemeinhin auseinandergezogen werden, und zwar Familien als Teil der privaten Lebenswelt und Unternehmen als formale Organisation des Wirtschaftssystems.Trade Review“... In seiner Form bietet der Sammelband sowohl Beiträge aus der grundbegrifflichen Arbeit, aus Forschungsprojekten, die weitere Desiderata explizieren, als auch aus Beratungskontexten. Er stellt damit einen profunden Einführungsband für Mitglieder von Unternehmerfamilien und Personen dar, die einen Einstieg in dieses Forschungsfeld suchen. Blickt man auf die skizzierten Beiträge, so tritt eine Vielzahl nicht nur thematischer, sondern auch theoretischer Überschneidungen auf ...” (Patrick Kahle, in: Kölner Zeitschrift für Soziologie und Sozialpsychologie, Jg. 72, 2020)Table of ContentsAuf dem Wege zu einer Soziologie der Unternehmerfamilie.- Familiensoziologische Grundlagen.- Organisationssoziologische Grundlagen.- Entwicklungslinien.- Perspektiven in Forschung, Theorieentwicklung und Praxisberatung.

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Book SynopsisListening, experiencing, drawing or interpreting spaces: narratives, experiences, visualizations and discourses can be helpful for the empirical investigation of spaces. This interdisciplinary handbook presents a broad spectrum of established methods and innovative method development to capture and understand different facets of spaces. Instructive explanations and concrete examples make the varied qualitative methods of spatial research understandable and applicable across disciplines. The theoretical and methodological aspects of qualitative spatial research form the framework of this handbook.

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Book SynopsisThe book is about a new paradigm in social sciences, contemplative inquiry. It concerns theory, methods of research,and practical applications. Deep contemplation of a situation could be an epistemological choice for social scientists. It is a fundamentally different approach to research, whereby the investigator is researching not only the object, but also the situation of research and herself/himself in it. Contemplating is a dual way of approaching the truth. It does not mean that the researcher is mentally divided; on the contrary she/he wants to be complete and achieve the unity of being as a researcher-human being and as an element of the situation (psychosocial and historical). Contemplating enables researchers to see how the mind works and create images and reports from the field: What is available for the mind and what is silent or repressed because of the dominating rhetoric of description and rhetoric of feeling? Contemplation is not only analysis, it is also an ethical choice of stopping here and now to see the situation clearly and reporting it together with all th reservations that could result from the mindfully observed interaction of mind, self, and situation at one historical moment.Trade ReviewThis book is unique and audacious, and fits very well with current tendencies within qualitative research. It introduces new, fresh threads to questions and concerns that are highly significant for contemporary social science. The formulated conclusions go beyond directly methodological issues, posing key philosophical questions to the whole project of western sociology. The critical potential of the adopted perspective would allow the author to throw the question of reflexive scientific examination in social sciences into doubt to the extent comparable with that of the deconstruction of sociology once performed by Baudrillard. The author does not choose to fulfill this potential. Rather, he pursues the reconciliation of the nondualistic contemplative approach with the discursiveness of western science. He also strongly emphasizes the consistency between the ethical and the cognitive, which is characteristic of the Zen perspective; in our academic reality these two components have been separated and any attempts at their reconciliation result in numerous and rather unmovable difficulties with regard to logic. All this makes the book in question deeply inspiring on various levels of the organization of social science within the contemporary institutional and cultural state of affairs. Without a doubt, this book is worth publishing as it contributes new quality to academic reflection on the social world and the ways of cognising it. -- Grażyna Romańczuk-Woroniecka, University of Warsaw

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Book SynopsisUsing smartphones and apps are innovative ways of collecting data among the public. The Netherlands Institute for Social Research SCP was involved in one of the first experiments to implement a smartphone application for collecting time use. This report describes the pilot study conducted by SCP and CentERdata (University of Tilburg) in 2011-2012 in the Netherlands to test use of smartphones as a data collection method.

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Book Synopsis1. Einführung.- 2. schlanke, grüne und saubere Qualitätskonzepte.- 3. schlanke, grüne und saubere Wissenschaften und Technologien.- 4. Modelle für schlanke, grüne und saubere Qualitätsverbesserung.- 5. Modelle zur Bewertung schlanker, grüner und sauberer Qualität.- 6. Grüne Statistik: Essenz schlanker, grüner und sauberer Wissenschaften.- 7. Erreichen und Fördern von schlanken, grünen und sauberen Qualitätszielen.- 8. Kontrolle, Überwachung und Einsatz von schlanken, grünen und sauberen Aktivitäten.- 9. Schlank, grün und sauber: Einige Fallstudien.- 10. Auf dem Weg zu einer nachhaltigen Lebensqualität.

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Book Synopsis

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Book SynopsisTrade Review"Strikes a good balance between teaching computer science fundamentals and putting data science techniques into practice. Designed to help students not only learn programming fundamentals but also leverage the large number of existing libraries to start accomplishing tasks with minimal code. Concepts are accompanied by rich Python examples that students can adapt to implement their own solutions to data science problems. I like that cloud services are used." —David Koop, Assistant Professor, U-Mass Dartmouth "Fun, engaging real-world examples and exercises will encourage students to conduct meaningful data analyses. This book provides many of the best explanations of data science concepts I’ve encountered. Introduces the most useful starter machine learning models—does a good job explaining how to choose the best model and what “the best” means. Great overview of all the big data technologies with relevant examples." —Jamie Whitacre, Data Science Consultant "Great introduction to Python! This book has my strongest recommendation both as an introduction to Python as well as Data Science. A great introduction to IBM Watson and the services it provides!" —Shyamal Mitra, Senior Lecturer, University of Texas "The best designed Intro to Data Science/Python book I have seen." —Roland DePratti, Central Connecticut State University "You’ll develop applications using industry standard libraries and cloud computing services." —Daniel Chen, Data Scientist, Lander Analytics "The book’s applied approach should engage students. The examples involving the top-down, stepwise refinement of programs illustrate how programs are really developed. A fantastic job providing background on various machine learning concepts without burdening the users with too many mathematical details." —Garrett Dancik, Associate Professor of Computer Science/Bioinformatics, Eastern Connecticut State University "Wonderful for first-time Python learners from all educational backgrounds and majors. My business analytics students had little to no coding experience when they began the course. In addition to loving the material, it was easy for them to follow along with the example exercises and by the end of the course were able to mine and analyze Twitter data using techniques learned from the book. The chapters are clearly written with detailed explanations of the example code, which makes it easy for students without a computer science background to understand. The modular structure, wide range of contemporary data science topics, and companion Jupyter notebooks make this a fantastic resource for instructors and students of a variety of Data Science, Business Analytics, and Computer Science courses. The “Self Checks” are great for students. Fabulous Big Data chapter–it covers all of the relevant programs and platforms. Great Watson chapter! This is the type of material that I look for as someone who teaches Business Analytics. The chapter provided a great overview of the Watson applications. Also, your translation examples are great for students because they provide an “instant reward”–it’s very satisfying for students to implement a task and receive results so quickly. Machine Learning is a huge topic and this chapter serves as a great introduction. I loved the housing data example–very relevant for business analytics students. The chapter was visually stunning." –Alison Sanchez, Assistant Professor in Economics, University of San Diego "I like the new combination of topics from computer science, data science, and stats. A compelling feature is the integration of content that is typically considered in separate courses. This is important for building data science programs that are more than just cobbling together math and computer science courses. A book like this may help facilitate expanding our offerings and using Python as a bridge for computer and data science topics. For a data science program that focuses on a single language (mostly), I think Python is probably the way to go." –Lance Bryant, Shippensburg University "The end-of-the-chapter problems are a real strength of this book (and of Deitel & Deitel books in general). I would likely use this book. The most compelling feature is that it could, theoretically, be used for both computer science and data science programs." –Dr. Mark Pauley, University of Nebraska at Omaha "I agree with the authors that CS curricula should include data science–the authors do an excellent job of combining programming and data science topics into an introductory text. The material is presented in digestible sections accompanied by engaging interactive examples. This book should appeal to both computer science students interested in high-level Python programming topics and data science applications, and to data science students who have little or no prior programming experience. Nearly all concepts are accompanied by a worked-out example. A comprehensive overview of object-oriented programming in Python–the use of graphics is sure to engage the reader. A great introduction to Big Data concepts, notably Hadoop, Spark, and IoT. The examples are extremely realistic and practical." –Garrett Dancik, Eastern Connecticut State University "I can see students feeling really excited about playing with the animations. Covers some of the most modern Python syntax approaches and introduces community standards for style and documentation. The breadth of each chapter and modular design of this book ensure that instructors can select sections tailored to a variety of programming skill levels and domain knowledge. The sorting visualization program is neat. The machine learning chapter does a great job of walking people through the boilerplate code needed for ML in Python. The case studies accomplish this really well. The later examples are so visual. Many of the model evaluation tasks make for really good programming practice." –Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign "An engaging, highly-accessible book that will foster curiosity and motivate beginning data scientists to develop essential foundations in Python programming, statistics, data manipulation, working with APIs, data visualization, machine learning, cloud computing, and more. Great walkthrough of the Twitter APIs–sentiment analysis piece is very useful. I’ve taken several classes that cover natural language processing and this is the first time the tools and concepts have been explained so clearly. I appreciate the discussion of serialization with JSON and pickling and when to use one or the other–with an emphasis on using JSON over pickle–good to know there’s a better, safer way! Very clear and engaging coverage of recursion, searching, sorting, and especially Big O–several “Aha” moments. The sorting animation is illustrative, useful, and fun. I look forward to seeing the textbook in use by instructors, students, and aspiring data scientists very soon." –Jamie Whitacre, Data Science Consultant "For a while, I have been looking for a book in Data Science using Python that would cover the most relevant technologies. Well, my search is over. A must-have book for any practitioner of this field. The machine learning chapter is a real winner!! The dynamic visualization is fantastic." —Ramon Mata-Toledo, Professor, James Madison University "IBM Watson is an exciting chapter. I enjoyed running the code and using the Watson service. The code examples put together a lot of Watson services in a really nifty example. I enjoyed the OOP chapter—doctest unit testing is nice because you can have the test in the actual docstring so things are traveling together. The line-by-line explanations of the static and dynamic visualizations of the die rolling are just great." —Daniel Chen, Data Scientist, Lander Analytics "A lucid exposition of the fundamentals of Python and Data Science. Excellent section on problem decomposition. Thanks for pointing out seeding the random number generator for reproducibility. I like the use of dictionary and set comprehensions for succinct programming. “List vs. Array Performance: Introducing %timeit” is convincing on why one should use ndarrays. Good defensive programming. Great section on Pandas Series and DataFrames—one of the clearest expositions that I have seen. The section on data wrangling is excellent. Natural Language Processing is an excellent chapter! I learned a tremendous amount going through it. Great exercises." —Shyamal Mitra, Senior Lecturer, University of Texas "My game programming students would appreciate these exercises." —Pranshu Gupta, Assistant Professor, DeSales U. "I like the discussion of exceptions and tracebacks. I really liked the Data Mining Twitter chapter; it focused on a real data source, and brought in a lot of techniques for analysis (e.g., visualization, NLP). I like that the Python modules helped hide some of the complexity. Word clouds look cool." —David Koop, Assistant Professor, U-Mass Dartmouth "I love the text! The right level for IT students. The examples are definitely a high point to this text. I love the quantity and quality of exercises. Avoiding heavy mathematics fits an IT program well." —Dr. Irene Bruno, George Mason University "A great introduction to deep learning." —Alison Sanchez, University of San Diego "I was very excited to see this textbook. I like its focus on data science and a general purpose language for writing useful data science programs. The data science portion distinguishes this book from most other introductory Python books." —Dr. Harvey Siy, University of Nebraska at Omaha "The collection of exercises is simply amazing. I’ve learned a lot in this review process, discovering the exciting field of AI. I liked the Deep Learning chapter, which left me amazed with the things that have already been achieved in this field. Many of the projects are really interesting." —José Antonio González Seco, Consultant "An impressive hands-on approach to programming meant for exploration and experimentation." —Elizabeth Wickes, Lecturer, School of Information Sciences, University of Illinois at Urbana-Champaign "I was impressed at how easy it was to get started with NLP using Python. A meaningful overview of deep learning concepts, using Keras. I like the streaming example." —David Koop, Assistant Professor, U-Mass Dartmouth "Really like the use of f-strings, instead of the older string-formatting methods. Seeing how easy TextBlob is compared to base NLTK was great. I never made word clouds with shapes before, but I can see this being a motivating example for people getting started with NLP. I’m enjoying the chapters in the latter parts of the book. They are really practical. I really enjoyed working through all the Big Data examples, especially the IoT ones." —Daniel Chen, Data Scientist, Lander Analytics "A good overview of various neural networks with coding examples for classification problems for which neural networks are commonly used. The exercises in this chapter will give students insight into how changing the structure of neural networks and the amount of training/testing data affect performance. The Twitter examples covering trending topics, creating word clouds, and mapping the location of users are instructive and engaging. I like the real-world examples of data munging. Reviewing this book was enjoyable and even though I was fairly familiar with Python, I ended up learning a lot." —Garrett Dancik, Associate Professor of Computer Science/Bioinformatics, Eastern Connecticut State University "I really liked the live input-output. The thing that I like most about this product is that it is a Deitel & Deitel book (I’m a big fan) that covers Python." —Dr. Mark Pauley, University of Nebraska at OmahaTable of ContentsPART 1 CS: Python Fundamentals Quickstart CS 1. Introduction to Computers and Python DS Intro: AI–at the Intersection of CS and DS CS 2. Introduction to Python Programming DS Intro: Basic Descriptive Stats CS 3. Control Statements and Program Development DS Intro: Measures of Central Tendency—Mean, Median, Mode CS 4. Functions DS Intro: Basic Statistics— Measures of Dispersion CS 5. Lists and Tuples DS Intro: Simulation and Static Visualization PART 2 CS: Python Data Structures, Strings and Files CS 6. Dictionaries and Sets DS Intro: Simulation and Dynamic Visualization CS 7. Array-Oriented Programming with NumPy, High-Performance NumPy Arrays DS Intro: Pandas Series and DataFrames CS 8. Strings: A Deeper Look Includes Regular Expressions DS Intro: Pandas, Regular Expressions and Data Wrangling CS 9. Files and Exceptions DS Intro: Loading Datasets from CSV Files into Pandas DataFrames PART 3 CS: Python High-End Topics CS 10. Object-Oriented Programming DS Intro: Time Series and Simple Linear Regression CS 11. Computer Science Thinking: Recursion, Searching, Sorting and Big O CS and DS Other Topics Blog PART 4 AI, Big Data and Cloud Case Studies DS 12. Natural Language Processing (NLP), Web Scraping in the Exercises DS 13. Data Mining Twitter®: Sentiment Analysis, JSON and Web Services DS 14. IBM Watson® and Cognitive Computing DS 15. Machine Learning: Classification, Regression and Clustering DS 16. Deep Learning Convolutional and Recurrent Neural Networks; Reinforcement Learning in the Exercises DS 17. Big Data: Hadoop®, Spark™, NoSQL and IoT

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Book SynopsisThe $12 billion self-help industry is under constant attack for pedaling false miracles to duped believers. But sociologist Albert Amao demonstrates that Americans eagerly support self-help books, seminars, and programs because, under the right conditions, these things work.Sociologist Albert Amao analyzes the accuracy of self-help and positive-thinking claims in this groundbreaking--and wholly unexpected--exploration of what works, what doesn't, and why. Regarding my personal experience, Amao writes, I can testify that positive thinking and positive action have worked wonderfully for me. Born in a poor Latin-American country into a very impoverished family with both parents practically illiterate, I was the oldest of five children. I started working when I was six years old, shining shoes and selling newspapers to help my family. Nobody then would have believed that I would be able to finish high school. Nevertheless, I was able to do it going to night

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Book SynopsisA roadmap to guide individuals on the ever-changing path of public scholarshipThe academic landscape is shifting greatly in the 21st century, and modern researchers must be able to navigate this sphere. With increased communication via the Internet and social media, researchers have developed new ways of conducting and representing research. Popularizing Scholarly Research: The Academic Landscape, Representation, and Professional Identity in the 21st Century explains how research has turned from disciplinary to transdisciplinary, the new structures research may take, as well as what a scholar''s professional life may look like. An impressive list of contributors cover transdisciplinary research, public intellectuals, audience and voice, creative nonfiction, writing collaboratively, visual images, writing for broad audiences, academic blogs, publicity, funding, and public policy. Additionally, Patricia Leavy includes supplemental resources to augment the information presented by contributors. Taking influence from Oxford Handbook of Methods for Public Scholarship, this book is required for anyone who wants to understand and keep up with modern research practices and build a career in this shifting arena.Table of ContentsPreface 1. Introduction to The Academic Landscape, Representation, and Professional Identity Patricia Leavy 2. The 21st Century Academic Landscape: From a Disciplinary to a Transdisciplinary Model Patricia Leavy 3. Public Scholarship, Public Intellectuals, and the Role of Higher Education in a Time of Crisis Henry Armand Giroux 4. Audience and Voice (and Sometimes Reflexivity) Yvonna S. Lincoln, Vassa Grichko, and Glenn Allen Phillips 5. Creative Nonfiction in Qualitative Inquiry Jessica Smartt Gullion and Jessica Spears Williams 6. Writing Collaboratively JeffriAnne Wilder 7. Visualizing Scholarship: Teaching, Research, and Representation in an Age of Images J. Wheeldon 8. Academics Writing for Broader Public Audience Phillip Vannini and Sarah Abbott 9. Academic Blogs Jimmie Manning 10. Generating Publicity and Engaging with the Media to Promote Academic Research Mark David Ryan 11. Funding: Writing Grant Proposals Ellen Gorsevski, Kate Magsamen-Conrad, and Lisa Hanasono 12. Public Policy and a Community-Based Research Approach 13. Beyond Popular Scholarship: SExT Youth Engaging in the World's Renewal Shira Taylor, Isfandyar Virani, Michelle Nyamekye, Hamza Shahid, Lauren Chang and Lynn Fels 14. Growing the Revolutionary Intellectual, Creating the Counterpublic Sphere Peter McLaren and Lilia D. Monzó

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Book SynopsisIn Social Networks of Meaning and Communication, Jan A. Fuhse offers a coherent theory of social structures as networks of relations interwoven with meaning. Drawing upon and extending the relational sociology of Harrison White and Charles Tilly, Fuhse seeks to establish a theory of social networks. Using a broad range of classic and contemporary social theory, he reconceptualizes social networks as constituted in patterns of expectations that form, reproduce, and change over the course of communicative events. These events, he argues, are the basic building blocks of the social world. They lead to expectations about the behavior of actors and their interaction with others the meaning structure making for observable regularities of communication in social networks.Social Networks of Meaning and Communication lays out a relational and constructivist perspective of social networks, highlighting a number of implications for social relationships, groups, and collective actors, as well as Trade ReviewJan Fuhse, the leading network theorist in Germany, lays out a theory of social networks that does justice to its cultural and communicative basis. With clarity and a remarkable breadth of view, he unites different forms of networks under a general theory. This masterful book brings insights from systems theory, culture, and philosophical anthropology to the study of social networks. This long-awaited statement will be the starting point for the next generation of network researchers. * John Levi Martin, University of Chicago, and author of Social Structures *Arguing against the conception of networks as stable and unproblematic patterns of relations, Jan Fuhse roots social networks in communication processes and dynamically evolving bundles of interpersonal expectations. This important work, indispensable for advancing network theory, provides deep reflection and a powerful lens for examining contexts ranging from migrant cultures to love as a relationship frame. * Ronald Breiger, Regents Professor, University of Arizona *Social Networks of Meaning and Communication offers readers a theory of meaningful, informal social networks, enacted through communication. In this book, scholars of culture will find the truly relational account of meaning they've wanted for decades. Fuhse has written a brilliant reference work, theoretical treatise, and measurement manual—all while engaging interesting and clear empirical data to illuminate the work. * Jennifer C. Lena, Columbia University, and co-author of Measuring Culture *The key arguments of the book are Fuhse's...and they are important and well-made arguments that everybody working in relational sociology and/or SNA should take note of and can learn from. The book is a rich source of insights that deserves to be widely read. This is a book that needed writing. Fuhse was the perfect person to write it, and he has made a great job of it. It should make a significant impact. * Nick Crossley, American Journal of Sociology *The idea that societies are vast networks of relations between social actors has a long history in sociology and is currently enjoying something of a renaissance in relational sociology...the central argument of Social Networks of Meaning and Communication by Jan Fuhse is that they comprise "observable regularities in communication" and a "meaning structure" that specifies the identities of participants and related expectations regarding interaction between them. * Nick Crossley, University of Manchester, American Journal of Sociology *Table of ContentsPreface 1. Networks with Theory 2. Networks, Relationships, and Meaning 3. Groups and Social Boundaries 4. Ethnic Categories and Cultural Differences 5. Roles and Institutions 6. Love and Gender 7. Events in Networks 8. Networks from Communication 9. Summary and Discussion References Index

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Book Synopsis.Trade ReviewThis is an accessible and student-friendly text that makes research methods come to life by connecting them to the most important and relevant issues of the day. This book checks every box for something that I would want for my students. * Kathrin Parks, ^lLoras College *Social Research Methods uses clear examples from current studies and prioritizes issues of diversity. It provides a fresh perspective, uses accessible language, and employs logic to explain complex concepts to students. * Monica Solinas-Saunders, ^lIndiana University Northwest *This is by far the most helpful sociology methods textbook I've come across. It sets itself apart from other social science textbooks by paying less attention to experiments and more to survey and in-depth interview skills. * Hien Park, ^lVanguard University *As a textbook for undergrads being introduced to research methods, it is just right. It is straightforward, concise, and conversational. I love the interactive classroom exercises at the end of each chapter. * Karen Jeong Robinson, ^lCSU-San Bernardino *

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Book SynopsisThe primary goal of science is to get it right, meaning that scientists seek to accurately document the world as it is. While erroneous conclusions and flawed theories can and do occur, they can only be tolerated as long as reliable mechanisms of self-correction exist, but an array of recent evidence suggests that this is not always the case. This book offers a behavioral science perspective on how scientific practice becomes compromised and provides recommendations for improvement. Broadening the discussion of research integrity beyond replication, publication biases, statistics, and methods, this book addresses the full complexity of the issue and serves academics and policy makers concerned with the reliability and validity of scientific findings across the social sciences. It tackles challenges presented by published reports and textbooks, addresses the ways that institutional review boards (IRBs) can influence the course of research, and discusses the weaknesses of meta-analysis, which is often recommended as a possible corrective measure for suboptimal scientific practice. The book concludes with an organizing framework to investigate how scientists'' behaviors can impact the reliability and validity of scientific research.Table of ContentsChapter 1: Science Reform Annabell Suh, Jon A. Krosnick, Lee J. Jussim, Steven T. Stevens, Stephanie Anglin Chapter 2: Improving Research Transparency in the Social Sciences: Registration, Pre-Registration, and Multiple Testing Adjustments Garret Christensen, Edward Miguel Chapter 3: What Can We Do About Our (Untrustworthy) Literature? Harold Pashler, Christine R. Harris Chapter 4: Is Science in Crisis? Danielle Fanelli Chapter 5: Accuracy and Completeness in the Dissemination of Classic Findings in Social Psychology David A. Wilder, Thomas E. Cuthbert Chapter 6: Strengths and Weaknesses of Meta-Analyses Katie Corker Chapter 7: Statistical Inference in Behavioral Research: Traditional and Bayesian Approaches Alexander Etz, Steven N. Goodman, Joachim Vandekerckhove Chapter 8: Stimulus Sampling and Research Integrity Byron Reeves, James J. Cummings Chapter 9: Questionable Interpretive Practices: Data (Mis)Interpretation and (Mis)Representation as Threats to Scientific Validity Lee Jussim, Sean T. Stevens, Stephanie Anglin Chapter 10: Questionable Research Practices Ernest H. O'Boyle Chapter 11: p-Hacking: A Strategic Analysis Robert J. MacCoun, Martin Götz Chapter 12:The Importance of Type III and Type IV Epistemic Errors for Improving Empirical Science Charlotte Ursula Tate Chapter 13: Publication Bias in the Social Sciences: A Threat to Scientific Integrity Neil Malhotra, L. J. Zigerell Chapter 14: Let's Peer Review Peer Review Simine Vazire Chapter 15: Impact and Influence of the Institutional Review Board: Protecting the Rights of Human Subjects in Scientific Experiments Alison Dundes Renteln Chapter 16: Viral Science and The Tragedy of the Scientific Commons Tanya Menon, Christopher Winship

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Book SynopsisBy most accounts, forced labour, human trafficking, and modern slavery are thriving in the global economy. Recent media reports -- including the discovery of widespread trafficking in Thailand''s shrimp industry, forced labour in global tea and cocoa supply chains, and the devastating deaths of workers constructing stadiums for Qatar''s World Cup-- have brought once hidden exploitation into the mainstream spotlight. As public concern about forced labour has escalated, governments around the world have begun to enact legislation to combat it in global production.Yet, in spite of soaring media and policy attention, reliable research on the business of forced labour remains difficult to come by. Forced labour is notoriously challenging to investigate, given that it is illegal, and powerful corporations and governments are reluctant to grant academics access to their workers and supply chains. Given the risk associated with researching the business of forced labour, until very recently, feTable of ContentsNotes on contributors Acknowledgements 1: Genevieve LeBaron: Introduction Part I: Surveying the Gaps 2: Andrew Crane & Genevieve LeBaron: Methodological Challenges in the Business of Forced Labour 3: Nicola Phillips: The Politics of Numbers: Beyond Methodological Challenges in Research on Forced Labour 4: Joel Quirk: The Politics of Forced Labour Research: NGOs, Activists, and States 5: Jean Allain: What Is Forced Labour? A Practical Guide for Humanities and Social Science Research 6: Sam Okyere: Confronting Bias in Ngo Research on Modern Slavery Part II: Frontiers of Forced Labour Research and Methods 7: Neil Howard: Why (and How) We Need To Talk To 'The Victims' 8: Jenny Chan: Researching Unfree Student Labour in Apple's Supply Chain 9: Andreas Rühmkorf: Transparent Companies? Legal Research Strategies to Understand Forced Labour in Global Supply Chains 10: Robert Caruana: The Role of Discourse Analysis in Researching Severe Labour Exploitation 11: Jessica Pliley: Archival Trouble: Researching Sex Trafficking In Early Twentieth-Century America

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Book SynopsisResearchers finally have a resource that focuses on the research question, providing them with a unique approach that will help in the field. Key Ideas sections presented in each chapter act as an organising mechanism and look at the big picture of that chapter.Table of ContentsChapter 1 Introduction To Research Design. Research What Is It Anyway? Qualitative And Quantitative Design. Types Of Research. Case Study. Introduction To Armchair Moments . Chapter 2 Scholar Before Researcher. Reading Why Do It? What Is A Literature Review? Research Idea Locations. Finding Literature. Background Descriptive Statistics. Case Study. Armchair Moment. Chapter 3 Problem Areas And Research Questions. The Need To Organize. Practical Tactics For Organizing Your Information. Transitioning From Organizing To Writing. Your Research Interests. The Road You Are On. Case Study. Armchair Moment. Chapter 4 Participant Sampling And Selection. How Do We Get Who We Want? Population, Sample, And Participants. Quantitative Single-Stage Sampling Procedures. Qualitative Sampling Process. Potential Harm To Participants. Case Study. Armchair Moment. Chapter 5 Believability In Observation And Measurement. You Must Know So They Can Believe! The Ways We Measure Quantitatively. Qualitative Data. “Score” Reliablity And Validity. Aera/Apa Guidelines. Developed Instruments. Reliability And Validity Of Qualitative Research. Case Study. Armchair Moment. Chapter 6 Instruments And Collection. Surveys, Questionnaires, Interviews, And Field Notes. How Should I Collect Data? Types Of Content. Basics Of Instrument Construction. Instrument Design. Scaling. Pretesting. Interviews. Methods Of Data Collection. Paper-And-Pencil Tests And Inventories. Cognitive Tests. Affective Tests. Language And Bias. Operationalizing Latent Constructs. Data Collection Patterns. Case Study. Armchair Moment. Chapter 7 Experimental And Nonexperimental. Research Design. Design For Strength And Elegance. Study Design Review. Designs And Their Limitations. Experimental Designs. Nonexperimental And Descriptive Designs. Brief Discussion Of Advanced Designs/Statistical Techniques. Types Of Data. Justifying Your Research Design. Case Study. Armchair Moment. Chapter 8 Qualitative Design. Introduction To Qualitative Design. Interactive Participation. Noninteractive Participation. Case Study. Armchair Moment. Chapter 9 Analysis Techniques: Descriptive And Inferential Statistics. Why Choose An Analysis Technique Now? Data. Descriptive Statistics. Inferential Statistics. Statistical Significance. Inferential Tests. Chapter 10 Data Analysis: Non-Numeric Data. Pattern Searching And Testing, Qualitative Style. Study Validity In Qualitative Analysis. Data Analysis Techniques, Phases, And Issues. Which Software To Choose, If You Go That Route. Analysis By Method. Case Study. Armchair Moment. Chapter 11 Program Evaluation. What Is Evaluation? Models/Approaches To Evaluation. Our Model And Experience With Evaluations. Additional Topics. Case Study. Armchair Moment. Chapter 12 Writing The Proposal Or Final Report. Research Report Writing. Proposal Report/Organization. Being Your Own Best Editor. Summary. Armchair Moment.

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Book SynopsisThe authors' inclusion of international experiences of election and exit polling expand the book's interest level beyond the domestic border - not to mention its use as a text. There are presently few publications, if any, that address the challenges and complexities of conducting this kind of research either at home or abroad.Trade Review"Elections and Exit Polling is an excellent basic guide to election polling, consisting of introductory material written by the editors and essays or excerpts of essay by various experts in the field." (MAA Reviews, August 2008) "This book is early proof that Warren Mitofsky's legacy will live on after him." (Clyde Tucker of CNN News) “An understanding the relationship between the product and the process in election polling is often lost. This edited volume unites ideas and researchers, with quality playing the central role.” (Michael J. Brick of Westat, Inc.) “The way [the author] conceived and constructed it is … a real joy for the reader.” (Dick Rubin of National Opinion Research Center)Table of ContentsPreface. Acknowledgments. Contributors. 1. Introduction (Fritz Scheuren and Wendy Alvey). 1.1. Introduction. 1.2. Definitions. 1.3. Brief History. 1.4. A Day in the Life of An Exit Poll. 1.5. Theory and Methodology. 1.6. Recent electoral Applications. 1.7. Some Major Limitations. 1.8. Recommendations and a Few Predictions. 1.9. References. 2. The Infamous 2000 Election. 2.1. Introduction. 2.2. Analysis: Looking Back. 2.3. Data Quality. 2.4. Summary Observations. 3. 2004: Fraudulent Election? 3.1. Introduction. 3.2. The Great Election Debate. 3.3. Confidentiality-Another Side of the Controversy. 3.4. Further Research. 3.5. Summary Observations. 4. Mid-Term Elections: 2006. 4.1. Introduction. 4.2. Pre-Election Polls. 4.3. Referenda on the Ballot. 4.4. Summary Observations. 5. Globe-Trotting Consultant. 5.1. Introduction. 5.2. A Historical Look at Exit Polls From a Mexican Perspective. 5.3. Muslim Opinions on Political Reform. 5.4. A Mexican Case Study. 5.5. Pre-Election Polling in Nagano, Japan. 5.6. Swedish Public Opinion Polls. 5.7. Election Polling In Canada. 5.8. Summary Observations. 6. Looking Ahead: Recommendations for 2008 and Beyond. 6.1. Introduction. 6.2. Recommendations for the Future. 6.3. Some Recent Research. 6.4. Guidelines for Quality Polling. 6.5. Next Steps. 6.6. Summary Observations. 7. Technical Appendix. 7.1. Introduction. 7.2. Statistical Methods for Papers in This Volume. 7.3. Appendix: Survey Questions. 7.4. Additional Methodological Papers. 7.5. Summary Observations. Author Index. Index.

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Book SynopsisBased on the multifaceted Encyclopedia of Statistical Sciences, Second Edition, this concise book outlines the statistical concepts and techniques for application that are essential for understanding data gathered from modern research in the social and behavioral sciences.Table of ContentsPreface v Contributors vii 1. Actuarial Science 1 2. Causation-I 6 3. Causation-II 9 4. Census 25 5. Correlation 32 6. Current Population Survey 46 7. Data Masking for Disclosure Avoidance 56 8. Divisia Indices 66 9. Econometrics 69 10. Educational Statistics 81 11. Electron Forecasting in the United Kingdom 88 12. Electron Projections 93 13. Employment Cost Index 97 14. Evolution of Current Population Survey 105 15. Federal Statistics 119 16. Fertility Measurement 128 17. Graduation 134 18. Human Capital 141 19. Dagum System of Income and Wealth Distributions 156 20. Income Distribution Models 174 21. Income Inequality Measures 186 22. Indexes: Affluence, Poverty, and Quality of Life 210 23. Index of Industrial Production 215 24. Infant Morality 220 25. Labor Statistics 223 26. Life Tables 228 27. Literature and Statistics-I 232 28. Literature and Statistics-II 238 29. Mathematical Theory of Population 242 30. Medico-Actuarial Investigations 249 31. Migration 255 32. Music, Probability, and Statistics 259 33. Numeracy 263 34. Office of Population Censuses and Surveys 271 35. Population Growth Models 277 36. Population Projection 285 37. Probabilistic Voting Models 289 38. Productivity Measurement 296 39. Public Opinion Polls 302 40. Purchasing Power Parity 308 41. Psychological Decision Making 313 42. Sabermetrics 319 43. Social Security Statistics 324 44. Social Statistics 329 45. Sociometry 335 46. Scoring Systems in Sports 339 47. Statistical Aspects of Television Viewing 343 48. Statistical Methods in Psychophysics 348 49. Statistics and Religious Studies 353 50. Statistics in Agriculture Production 357 51. Statistics in Archaeology-I 373 52. Statistics in Archaeology-II 378 53. Statistics in Childhood Obesity 385 54. Statistics in Historical Studies 400 55. Statistics in Gambling 407 56. Statistics in Law 411 57. Statistics in Linguistics 421 58. Statistics in Musicology 429 59. Statistics in Political Science 437 60. Statistics in Psychology 446 61. Statistics in Sociology 453 62. Statistics in Sports 461 63. Stochastic Demography 467 64. The Theory and Practice of Index Numbers 480 65. Transferable Vote System for Elections 505 66. Uncertainty in Economics 508 67. U.S. Bureau of the Census 515 68. Utility Theory 531 69. Utilization Analysis 539 70. Vital Statistics 544 71. Whittaker-Henderson Graduation Formulas 549 Index 553

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Book SynopsisHarm takes shape in and through what is suppressed, left out, or taken for granted.This book is a guide tounderstanding and uncovering what is left unsaidwhether concealed or silenced, presupposed or excluded. Drawing on a variety of real-world examples, narrative criminologist Lois Presser outlines how to determine what or who is excluded from textual materials. With strategies that can be added to the tool kits of social researchers and activists alike, Unsaidprovides a richly layered approach to analyzing and dismantling the power structures that both create and arise from what goes without saying.Trade Review"Presser concedes the impossibility of creating a comprehensive, all-inclusive text in which nothing is left unsaid as she advocates for honest and critical reflection to identify the unspoken assumptions and silenced viewpoints characteristic of all texts." * CHOICE *Table of ContentsContents Preface Acknowledgments 1. Kept Quiet 2. Too Little or Too Much Said 3. Figurative Expression 4. Missing Subjects 5. The Social Construction of Absences 6. Concluding Remarks: Boundless Texts, Better Worlds Appendix: A Word on Sampling Glossary Notes References Index

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Book SynopsisIn this collection of essays, historians discuss the applications of evolutionary theory to cultural, social, economic and political phenomena. Contributors include anthropologist Donald Brown, archaeologist Albert Naccache and demographer Noel Bonneuil.Table of ContentsChapter 1 An Interdisciplinary Paradigm Shift Chapter 2 History and the Scientific World View Chapter 3 A Brief History of Evolution Chapter 4 Evolving toward History Chapter 5 Human Nature and History Chapter 6 Evolutionary Theory and Group Selection: The Question of Warfare Chapter 7 Evolutionary Theory of History Chapter 8 Darwinizing History: The Evolution of Power in Russia Chapter 9 On the Role of Mathematical Biology in Contemporary Historiography Chapter 10 History, Differential Inclusions, and Narrative Chapter 11 Does Culture Evolve? Chapter 12 On the Problem of Laws in Nature and History: A Comparison

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