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| data analysis for social science: Data Analysis for Social Science Elena Llaudet, Kosuke Imai, 2022-11-29 Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors-- |
| data analysis for social science: Quantitative Social Science Kosuke Imai, Lori D. Bougher, 2021-03-16 Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a translation of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place-- |
| data analysis for social science: Data Analysis for Social Science Elena Llaudet, Kosuke Imai, 2022-11-29 Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors-- |
| data analysis for social science: Data Analytics for the Social Sciences G. David Garson, 2021-11-30 Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the caret package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two Quick Start exercises designed to allow quick immersion in chapter topics, followed by In Depth coverage. Data are available for all examples and runnable R code is provided in a Command Summary. An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, books within the book on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis. |
| data analysis for social science: Data Analysis for the Social Sciences Douglas Bors, 2018-01-08 ′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 social science: Theory-Based Data Analysis for the Social Sciences Carol S. Aneshensel, 2013 This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of third variables to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions. |
| data analysis for social science: Text Mining for Qualitative Data Analysis in the Social Sciences Gregor Wiedemann, 2016-08-23 Gregor Wiedemann evaluates text mining applications for social science studies with respect to conceptual integration of consciously selected methods, systematic optimization of algorithms and workflows, and methodological reflections relating to empirical research. In an exemplary study, he introduces workflows to analyze a corpus of around 600,000 newspaper articles on the subject of “democratic demarcation” in Germany. He provides a valuable resource for innovative measures to social scientists and computer scientists in the field of applied natural language processing. |
| data analysis for social science: Analyzing Social Science Data D. A. De Vaus, 2002-09-17 Abridged Contents PART ONE: HOW TO PREPARE DATA FOR ANALYSIS\PART TWO: HOW TO PREPARE VARIABLE FOR ANALYSIS\PART THREE: HOW TO REDUCE THE AMOUNT OF DATA TO ANALYZE\PART FOUR: HOW AND WHEN TO GENERALIZE\PART FIVE: HOW TO ANALYZE A SINGLE VARIABLE\PART SIX: HOW TO ANALYZE TWO VARIABLES\PART SEVEN: HOW TO CARRY OUT MULTIVARIATE ANALYSIS |
| data analysis for social science: Statistics and Data Analysis for Social Science Eric J. Krieg, 2019-07-11 Apply statistics to your everyday life. Statistics and Data Analysis for Social Science helps students to build a strong foundational understanding of statistics by providing clarity around when and why statistics useful. Rather than focusing on the how to of statistics, author Eric J. Krieg simplifies the complexity of statistical calculations by introducing only what is necessary to understanding each concept. Every chapter is written around and applied to a different social problem or issues—enabling students to broaden their imagination about the statistical tools that can be used to make sense of our world and, maybe, to make the world a better place. In addition to updating all the tables and examples with new data, the Second Edition has replaced the section on SPSS with three new sets of exercises at the end of each chapter: Chapter Exercises for students complete during their reading and bring questions to class, In-Class Exercises that focus on the areas that students struggled with during their reading, and Homework Exercises that can be assigned if students need extra practice with the concepts. |
| data analysis for social science: Using R for Data Analysis in Social Sciences Quan Li, 2018-05-09 Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research. |
| data analysis for social science: Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences Michael J. Albers, 2017-04-03 Guides readers through the quantitative data analysis process including contextualizing data within a research situation, connecting data to the appropriate statistical tests, and drawing valid conclusions Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how to use statistical tests as a key tool for analyzing research data. The book presents the entire data analysis process as a cyclical, multiphase process and addresses the processes of exploratory analysis, decision-making for performing parametric or nonparametric analysis, and practical significance determination. In addition, the author details how data analysis is used to reveal the underlying patterns and relationships between the variables and connects those trends to the data’s contextual situation. Filling the gap in quantitative data analysis literature, this book teaches the methods and thought processes behind data analysis, rather than how to perform the study itself or how to perform individual statistical tests. With a clear and conversational style, readers are provided with a better understanding of the overall structure and methodology behind performing a data analysis as well as the needed techniques to make informed, meaningful decisions during data analysis. The book features numerous data analysis examples in order to emphasize the decision and thought processes that are best followed, and self-contained sections throughout separate the statistical data analysis from the detailed discussion of the concepts allowing readers to reference a specific section of the book for immediate solutions to problems and/or applications. Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences also features coverage of the following: • The overall methodology and research mind-set for how to approach quantitative data analysis and how to use statistics tests as part of research data analysis • A comprehensive understanding of the data, its connection to a research situation, and the most appropriate statistical tests for the data • Numerous data analysis problems and worked-out examples to illustrate the decision and thought processes that reveal underlying patterns and trends • Detailed examples of the main concepts to aid readers in gaining the needed skills to perform a full analysis of research problems • A conversational tone to effectively introduce readers to the basics of how to perform data analysis as well as make meaningful decisions during data analysis Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences is an ideal textbook for upper-undergraduate and graduate-level research method courses in the behavioral and social sciences, statistics, and engineering. This book is also an appropriate reference for practitioners who require a review of quantitative research methods. Michael J. Albers, Ph.D., is Professor in the Department of English at East Carolina University. His research interests include information design with a focus on answering real-world questions, the presentation of complex information, and human–information interaction. Dr. Albers received his Ph.D. in Technical Communication and Rhetoric from Texas Tech University. |
| data analysis for social science: Quantitative Data Analysis for Social Scientists Alan Bryman, Duncan Cramer, 1990-01-01 |
| data analysis for social science: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2016-08-10 Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website. |
| data analysis for social science: Dyadic Data Analysis David A. Kenny, Deborah A. Kashy, William L. Cook, 2020-11-26 Interpersonal phenomena such as attachment, conflict, person perception, learning, and influence have traditionally been studied by examining individuals in isolation, which falls short of capturing their truly interpersonal nature. This book offers state-of-the-art solutions to this age-old problem by presenting methodological and data-analytic approaches useful in investigating processes that take place among dyads: couples, coworkers, parent and child, teacher and student, or doctor and patient, to name just a few. Rich examples from psychology and across the behavioral and social sciences help build the researcher's ability to conceptualize relationship processes; model and test for actor effects, partner effects, and relationship effects; and model and control for the statistical interdependence that can exist between partners. The companion website provides clarifications, elaborations, corrections, and data and files for each chapter. |
| data analysis for social science: Categorical Data Analysis for the Behavioral and Social Sciences Razia Azen, Cindy M. Walker, 2021-05-26 Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analysis methods and emphasize specific research questions that can be addressed by each analytic procedure, including how to obtain results using SPSS, SAS, and R, so that readers are able to address the research questions they wish to answer. Each chapter begins with a Look Ahead section to highlight key content. This is followed by an in-depth focus and explanation of the relationship between the initial research question, the use of software to perform the analyses, and how to interpret the output substantively. Included at the end of each chapter are a range of software examples and questions to test knowledge. New to the second edition: The addition of R syntax for all analyses and an update of SPSS and SAS syntax. The addition of a new chapter on GLMMs. Clarification of concepts and ideas that graduate students found confusing, including revised problems at the end of the chapters. Written for those without an extensive mathematical background, this book is ideal for a graduate course in categorical data analysis taught in departments of psychology, educational psychology, human development and family studies, sociology, public health, and business. Researchers in these disciplines interested in applying these procedures will also appreciate this book’s accessible approach. |
| data analysis for social science: Bayesian Analysis for the Social Sciences Simon Jackman, 2009-10-27 Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets. |
| data analysis for social science: Data Science and Social Research N. Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino, 2017-11-17 This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices. |
| data analysis for social science: Statistics and Data Analysis for Social Science Eric J. Krieg, 2013-11-01 For a one-semester, undergraduate introductory course in social statistics. Statistical tools for understanding the social world. This first edition text seeks to answer the question, universally asked by the social science student, Why statistics? The author introduces only those statistical concepts that are necessary to understand, interpret, and present social science research. All concepts are introduced in the context of a social science application, and strong emphasis is placed on demonstrating what data looks like, as opposed to giving theoretical explanations. Complexity of calculations is reduced to those elements necessary for understanding the statistical concept. Optional technology use is paired with the core elements of the course, making this text a pragmatic and engaging introduction to the practice of social statistics. |
| data analysis for social science: The Behavioral and Social Sciences National Research Council, Division of Behavioral and Social Sciences and Education, Commission on Behavioral and Social Sciences and Education, Committee on Basic Research in the Behavioral and Social Sciences, 1988-02-01 This volume explores the scientific frontiers and leading edges of research across the fields of anthropology, economics, political science, psychology, sociology, history, business, education, geography, law, and psychiatry, as well as the newer, more specialized areas of artificial intelligence, child development, cognitive science, communications, demography, linguistics, and management and decision science. It includes recommendations concerning new resources, facilities, and programs that may be needed over the next several years to ensure rapid progress and provide a high level of returns to basic research. |
| data analysis for social science: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2020-11-17 Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. Features: Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHub New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner. |
| data analysis for social science: Qualitative Data Analysis Ian Dey, 2003-09-02 Qualitative Data Analysis shows that learning how to analyse qualitative data by computer can be fun. Written in a stimulating style, with examples drawn mainly from every day life and contemporary humour, it should appeal to a wide audience. |
| data analysis for social science: Just Plain Data Analysis Gary M. Klass, 2012-04-13 Just Plain Data Analysis teaches students statistical literacy skills that they can use to evaluate and construct arguments about public affairs issues grounded in numerical evidence. The book addresses skills that are often not taught in introductory social science research methods courses and that are often covered sketchily in the research methods textbooks: where to find commonly used measures of political and social conditions; how to assess the reliability and validity of specific indicators; how to present data efficiently in charts and tables; how to avoid common misinterpretations and misrepresentations of data; and how to evaluate causal arguments based on numerical data. With a new chapter on statistical fallacies and updates throughout the text, the new edition teaches students how to find, interpret, and present commonly used social indicators in an even clearer and more practical way. |
| data analysis for social science: Dependent Data in Social Sciences Research Mark Stemmler, |
| data analysis for social science: Longitudinal Data Analysis Jason Newsom, Richard N. Jones, Scott M. Hofer, 2013-06-19 This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields. |
| data analysis for social science: Data Analysis with Mplus Christian Geiser, 2012-11-14 A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips and solution strategies are presented in boxes in each chapter. The companion website (http://crmda.ku.edu/guilford/geiser) features data sets, annotated syntax files, and output for all of the examples. Of special utility to instructors and students, many of the examples can be run with the free demo version of Mplus. |
| data analysis for social science: Social Phenomena Bruno Gonçalves, Nicola Perra, 2015-08-14 This book focuses on the new possibilities and approaches to social modeling currently being made possible by an unprecedented variety of datasets generated by our interactions with modern technologies. This area has witnessed a veritable explosion of activity over the last few years, yielding many interesting and useful results. Our aim is to provide an overview of the state of the art in this area of research, merging an extremely heterogeneous array of datasets and models. Social Phenomena: From Data Analysis to Models is divided into two parts. Part I deals with modeling social behavior under normal conditions: How we live, travel, collaborate and interact with each other in our daily lives. Part II deals with societal behavior under exceptional conditions: Protests, armed insurgencies, terrorist attacks, and reactions to infectious diseases. This book offers an overview of one of the most fertile emerging fields bringing together practitioners from scientific communities as diverse as social sciences, physics and computer science. We hope to not only provide an unifying framework to understand and characterize social phenomena, but also to help foster the dialogue between researchers working on similar problems from different fields and perspectives. |
| data analysis for social science: Applied Panel Data Analysis for Economic and Social Surveys Hans-Jürgen Andreß, Katrin Golsch, Alexander W. Schmidt, 2013-01-24 Many economic and social surveys are designed as panel studies, which provide important data for describing social changes and testing causal relations between social phenomena. This textbook shows how to manage, describe, and model these kinds of data. It presents models for continuous and categorical dependent variables, focusing either on the level of these variables at different points in time or on their change over time. It covers fixed and random effects models, models for change scores and event history models. All statistical methods are explained in an application-centered style using research examples from scholarly journals, which can be replicated by the reader through data provided on the accompanying website. As all models are compared to each other, it provides valuable assistance with choosing the right model in applied research. The textbook is directed at master and doctoral students as well as applied researchers in the social sciences, psychology, business administration and economics. Readers should be familiar with linear regression and have a good understanding of ordinary least squares estimation. |
| data analysis for social science: Longitudinal and Panel Data Edward W. Frees, 2004-08-16 An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers. |
| data analysis for social science: Using Statistical Methods in Social Science Research Soleman H. Abu-Bader, 2011-07-01 In Using Statistical Methods, Soleman Abu-Bader detects and addresses the gaps between the research and data analysis of the classroom environment and the practitioner's office. This book not only guides social scientists through different tests, but also provides students and researchers alike with information that will help them in their own practice. With focus on the purpose, rationale, and assumptions made by each statistical test, and a plethora of research examples that clearly display their applicability and function in real-world practice, Professor Abu-Bader creates a step-by-step description of the process needed to clearly organize, choose a test or statistical technique, analyze, interpret, and report research findings. |
| data analysis for social science: Analysis of Multivariate Social Science Data David J. Bartholomew, Fiona Steele, Jane Galbraith, Irini Moustaki, 2008-06-04 Drawing on the authors' varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to include regression analysis, con |
| data analysis for social science: Introduction to Data Science for Social and Policy Research Jose Manuel Magallanes Reyes, 2017-09-21 This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R. |
| data analysis for social science: Theory-building and Data Analysis in the Social Sciences Herbert B. Asher, 1984 |
| data analysis for social science: Handbook of Data Analysis Melissa A Hardy, Alan Bryman, 2009-06-17 ′This book provides an excellent reference guide to basic theoretical arguments, practical quantitative techniques and the methodologies that the majority of social science researchers are likely to require for postgraduate study and beyond′ - Environment and Planning ′The book provides researchers with guidance in, and examples of, both quantitative and qualitative modes of analysis, written by leading practitioners in the field. The editors give a persuasive account of the commonalities of purpose that exist across both modes, as well as demonstrating a keen awareness of the different things that each offers the practising researcher′ - Clive Seale, Brunel University ′With the appearance of this handbook, data analysts no longer have to consult dozens of disparate publications to carry out their work. The essential tools for an intelligent telling of the data story are offered here, in thirty chapters written by recognized experts. ′ - Michael Lewis-Beck, F Wendell Miller Distinguished Professor of Political Science, University of Iowa ′This is an excellent guide to current issues in the analysis of social science data. I recommend it to anyone who is looking for authoritative introductions to the state of the art. Each chapter offers a comprehensive review and an extensive bibliography and will be invaluable to researchers wanting to update themselves about modern developments′ - Professor Nigel Gilbert, Pro Vice-Chancellor and Professor of Sociology, University of Surrey This is a book that will rapidly be recognized as the bible for social researchers. It provides a first-class, reliable guide to the basic issues in data analysis, such as the construction of variables, the characterization of distributions and the notions of inference. Scholars and students can turn to it for teaching and applied needs with confidence. The book also seeks to enhance debate in the field by tackling more advanced topics such as models of change, causality, panel models and network analysis. Specialists will find much food for thought in these chapters. A distinctive feature of the book is the breadth of coverage. No other book provides a better one-stop survey of the field of data analysis. In 30 specially commissioned chapters the editors aim to encourage readers to develop an appreciation of the range of analytic options available, so they can choose a research problem and then develop a suitable approach to data analysis. |
| data analysis for social science: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data analysis for social science: Qualitative Analysis for Social Scientists Anselm L. Strauss, 1987-06-26 The teaching of qualitative analysis in the social sciences is rarely undertaken in a structured way. This handbook is designed to remedy that and to present students and researchers with a systematic method for interpreting qualitative data', whether derived from interviews, field notes, or documentary materials. The special emphasis of the book is on how to develop theory through qualitative analysis. The reader is provided with the tools for doing qualitative analysis, such as codes, memos, memo sequences, theoretical sampling and comparative analysis, and diagrams, all of which are abundantly illustrated by actual examples drawn from the author's own varied qualitative research and research consultations, as well as from his research seminars. Many of the procedural discussions are concluded with rules of thumb that can usefully guide the researchers' analytic operations. The difficulties that beginners encounter when doing qualitative analysis and the kinds of persistent questions they raise are also discussed, as is the problem of how to integrate analyses. In addition, there is a chapter on the teaching of qualitative analysis and the giving of useful advice during research consultations, and there is a discussion of the preparation of material for publication. The book has been written not only for sociologists but for all researchers in the social sciences and in such fields as education, public health, nursing, and administration who employ qualitative methods in their work. |
| data analysis for social science: Applied Quantitative Analysis in Education and the Social Sciences Yaacov Petscher, Christopher Schatschneider, Donald L. Compton, 2013-03-05 To say that complex data analyses are ubiquitous in the education and social sciences might be an understatement. Funding agencies and peer-review journals alike require that researchers use the most appropriate models and methods for explaining phenomena. Univariate and multivariate data structures often require the application of more rigorous methods than basic correlational or analysis of variance models. Additionally, though a vast set of resources may exist on how to run analysis, difficulties may be encountered when explicit direction is not provided as to how one should run a model and interpret results. The mission of this book is to expose the reader to advanced quantitative methods as it pertains to individual level analysis, multilevel analysis, item-level analysis, and covariance structure analysis. Each chapter is self-contained and follows a common format so that readers can run the analysis and correctly interpret the output for reporting. |
| data analysis for social science: Quantitative Data Analysis with Minitab Alan Bryman, Duncan Cramer, 2003-09-02 Quantitative data analysis is now a compulsory component of most degree courses in the social sciences and students are increasingly reliant on computers for the analysis of data. Quantitative Data Analysis with Minitab explains statistical tests for Minitab users using the same formulae free, non technical approach, as the very successful SPPS version. Students will learn a wide range of quantitative data analysis techniques and become familiar with how these techniques can be implemented through the latest version of Minitab. Techniques covered include univariate analysis (with frequency table, dispersion and histograms), bivariate (with contingency tables correlation, analysis of varience and non-parametric tests) and multivariate analysis (with multiple regression, path analysis, covarience and factor analysis). In addition the book covers issues such as sampling, statistical significance, conceptualisation and measurement and the selection of appropriate tests. Each chapter concludes with a set of exercises. Social science students will welcome this integrated, non mathematical introduction to quantitative data anlysis and the minitab package. |
| data analysis for social science: Social Science Research Anol Bhattacherjee, 2012-04-01 This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages. |
| data analysis for social science: Regression Analysis for the Social Sciences Rachel A. Gordon, 2015-03-17 Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: •interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. •thorough integration of teaching statistical theory with teaching data processing and analysis. •teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition. |
| data analysis for social science: Secondary Qualitative Data Analysis in the Health and Social Sciences Cheryl Tatano Beck, 2019-01-15 Despite a long history in quantitative research, it is only recently that enthusiasm for secondary analysis of qualitative data has gained momentum across health and social science disciplines. Given that researchers have long known the inordinate amount of time and energy invested in conducting qualitative research, the appeal of secondary analysis of qualitative data is clear. Involving the use of an existing dataset to answer research questions that are different from those asked in the original study, this method allows researchers to once again make use of their hard-earned qualitative dataset and to listen to their participants’ voices to the best of their ability in order to improve care and promote understanding. As secondary qualitative data analysis continues to evolve, more methodological guidance is needed. This book outlines three approaches to secondary data analysis and addresses the key issues that researchers need to wrestle with, such as ethical considerations, voice, and representation. Intellectual and interpretive hazards that can jeopardize the outcome of these analyses are highlighted and discussed, as are the criteria for assessing their quality and trustworthiness. Written as a thought-provoking guide for qualitative researchers from across the health and social sciences, this text includes a review of the state of the science in nursing and a number of in-depth illustrative case studies. |
14.310x: Data Analysis for Social Scientists - Amazon Web …
We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, …
Code and Data for the Social Sciences: A Practitioner’s Guide
We write code to clean data, to transform data, to scrape data, and to merge data. We write code to execute statistical analyses, to simulate models, to format results, to produce plots. We stare …
DATA ANALYSIS FOR THE SOCIAL SCIENCES - us.sagepub.com
Once researchers have collected their data, the first step in the analysis process is to summarize the observations, both pictorially and numerically. The purpose of this chapter is to examine …
Theory-Based Data Analysis for the Social Sciences
Theory-Based Data Analysis for the Social Sciences applies the reasoning of the elaboration model to multivariate statistical techniques for observational data, making the model applicable …
DATA ANALYSIS IN SOCIAL SCIENCE RESEARCH - ijnrd.org
Data analysis provides social science researchers with the tools to unlock insights and understand complex social phenomena. Researchers can interpret the data and uncover relationships and …
Statistical Tools for Social Sciences: A Comprehensive Review …
Evaluate the effectiveness of different statistical tools in analyzing social science data. Examine how statistical tools contribute to the interpretation of social phenomena. Assess the impact of …
A First Course in Quantitative Social Science - Harvard University
Our vision for A First Course in Quantitative Social Science stems from the emerging need for undergraduate and beginning graduate students in social sciences to acquire the data analysis …
SPSS: An Imperative Quantitative Data Analysis Tool for Social …
SPSS is a powerful and user-friendly software package for all sorts of statistical analysis of data (Levesque, 2007). This program is mostly used by students and researchers from the fields of...
POL 451: Statistical Methods in Political Science - Harvard …
In this course, students will learn basic research design and data analysis methodology in empirical social science research. The main goal is to learn how statistical theory can be used …
DATA ANALYSIS FOR THE SOCIAL SCIENCES - SAGE …
We differentiate the basic forms of research (experimental, quasi-experimental, and observational designs) and review the main characteristics of each.
Introduction to Data Science for Social and Policy Research
Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis.
Quantitative Social Science: An Introduction - Chapter one …
In quantitative social science research, scholars analyze data to understand and solve problems about society and human behavior. For example, researchers examine racial discrimination in …
Using Topological Data Analysis in Social Science Research: …
In this paper, we discuss the considerations that researchers must understand in conducting TDA with engineering education data. In the data analysis, a researcher must choose a filtering …
QUANTITATIVE DATA ANALYSIS USING SPSS - Archive.org
practical introduction to quantitative data analysis using the most widely available statistical software – SPSS (Statistical Package for the Social Sciences). The aim is to get students and …
Quantitative Data Analysis: Choosing Between SPSS, PLS and …
methodology employs numerical data to quantify the social phenomenon, choosing the right techniques enable social scientists to analyse the findings of the study accurately.
Statistical Analysis of Social Data: Regression Analysis
We will cover correlation and bivariate regression, multiple ordinary least squares regression, regression assumptions, regression diagnostics, indicator variables and interactions, data …
Introduction to Quantitative Data Analysis in the Behavioral …
It looks at the data analysis from different viewpoints and using different tests to enable a student to learn how and when to apply different analysis methods.
SPSS as a means for Scientific Analysis in Social Science …
Abstract: SPSS (Statistical Package for the Social Sciences) is an innovative computerized programming for statistical analysis, specially developed for the social sciences research. Now …
Data science for social work practice - SAGE Journals
Data science can improve social work scientific knowl-edge by creating innovative approaches to quantitative data, beyond the traditional statistical analysis. Data science focuses on real …
Msc in Social Scientific Data Analysis - Lu
The Master of Science in Social Scientific Data Analysis will prepare you to become a professional researcher/social scientific data analyst with a specialisation on the research process and …
14.310x: Data Analysis for Social Scientists - Amazon Web …
We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized …
Code and Data for the Social Sciences: A Practitioner’s Guide
We write code to clean data, to transform data, to scrape data, and to merge data. We write code to execute statistical analyses, to simulate models, to format results, to produce plots. We stare at, …
DATA ANALYSIS FOR THE SOCIAL SCIENCES
Once researchers have collected their data, the first step in the analysis process is to summarize the observations, both pictorially and numerically. The purpose of this chapter is to examine the …
Theory-Based Data Analysis for the Social Sciences
Theory-Based Data Analysis for the Social Sciences applies the reasoning of the elaboration model to multivariate statistical techniques for observational data, making the model applicable to …
DATA ANALYSIS IN SOCIAL SCIENCE RESEARCH - ijnrd.org
Data analysis provides social science researchers with the tools to unlock insights and understand complex social phenomena. Researchers can interpret the data and uncover relationships and …
Statistical Tools for Social Sciences: A Comprehensive Review …
Evaluate the effectiveness of different statistical tools in analyzing social science data. Examine how statistical tools contribute to the interpretation of social phenomena. Assess the impact of …
A First Course in Quantitative Social Science - Harvard …
Our vision for A First Course in Quantitative Social Science stems from the emerging need for undergraduate and beginning graduate students in social sciences to acquire the data analysis …
SPSS: An Imperative Quantitative Data Analysis Tool for …
SPSS is a powerful and user-friendly software package for all sorts of statistical analysis of data (Levesque, 2007). This program is mostly used by students and researchers from the fields of...
POL 451: Statistical Methods in Political Science - Harvard …
In this course, students will learn basic research design and data analysis methodology in empirical social science research. The main goal is to learn how statistical theory can be used to make …
DATA ANALYSIS FOR THE SOCIAL SCIENCES - SAGE …
We differentiate the basic forms of research (experimental, quasi-experimental, and observational designs) and review the main characteristics of each.
Introduction to Data Science for Social and Policy Research
Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis.
Quantitative Social Science: An Introduction - Chapter one …
In quantitative social science research, scholars analyze data to understand and solve problems about society and human behavior. For example, researchers examine racial discrimination in the …
Using Topological Data Analysis in Social Science Research: …
In this paper, we discuss the considerations that researchers must understand in conducting TDA with engineering education data. In the data analysis, a researcher must choose a filtering …
QUANTITATIVE DATA ANALYSIS USING SPSS - Archive.org
practical introduction to quantitative data analysis using the most widely available statistical software – SPSS (Statistical Package for the Social Sciences). The aim is to get students and …
Quantitative Data Analysis: Choosing Between SPSS, PLS and …
methodology employs numerical data to quantify the social phenomenon, choosing the right techniques enable social scientists to analyse the findings of the study accurately.
Statistical Analysis of Social Data: Regression Analysis
We will cover correlation and bivariate regression, multiple ordinary least squares regression, regression assumptions, regression diagnostics, indicator variables and interactions, data …
Introduction to Quantitative Data Analysis in the Behavioral …
It looks at the data analysis from different viewpoints and using different tests to enable a student to learn how and when to apply different analysis methods.
SPSS as a means for Scientific Analysis in Social Science …
Abstract: SPSS (Statistical Package for the Social Sciences) is an innovative computerized programming for statistical analysis, specially developed for the social sciences research. Now a …
Data science for social work practice - SAGE Journals
Data science can improve social work scientific knowl-edge by creating innovative approaches to quantitative data, beyond the traditional statistical analysis. Data science focuses on real …
Msc in Social Scientific Data Analysis - Lu
The Master of Science in Social Scientific Data Analysis will prepare you to become a professional researcher/social scientific data analyst with a specialisation on the research process and …
