05129cam a2200505 i 4500 22739175 BJBSDDR 20260518173415.0 a|||||r|||| 00| 0 ta 220805s2023 njua rb 001 0 eng 2022030106 9780691199436 (paperback ; alk. paper) 9780691199429 (hardback ; alk. paper) 9780691229348 (ebook) 22739175 DLC eng rda DLC DLC eng pcc HA29 .L791d 2023 519.5 23/eng/20220805 SOC027000 COM021030 bisacsh Llaudet, Elena, 1978- 48509 Data analysis for social science : a friendly and practical introduction / Elena Llaudet and Kosuke Imai. Princeton ; Oxford : Princeton University Press, [2023] xii, 238 pages : color illustrations ; 26 cm text txt rdacontent unmediated n rdamedia volume nc rdacarrier Includes bibliographical references and index. Contenidos : Introduction Estimating causal effects with randomized experiments Inferring population characteristics via survey research Predicting outcomes using linear regression Estimating casual effects with observational data Probability Quantifying uncertainty "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"-- Provided by publisher. "An ideal textbook for an introductory course on quantitative methods for social scientistsData Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Using plain language and assuming no prior knowledge of statistics and coding, the book provides a step-by-step guide to analyzing real-world data with the statistical program R for the purpose of answering a wide range of substantive social science questions. It teaches not only how to perform the analyses but also how to interpret results and identify strengths and limitations. This one-of-a-kind textbook includes supplemental materials to accommodate students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.Analyzes real-world data using the powerful, open-sourced statistical program R, which is free for everyone to useTeaches how to measure, predict, and explain quantities of interest based on dataShows how to infer population characteristics using survey research, predict outcomes using linear models, and estimate causal effects with and without randomized experimentsAssumes no prior knowledge of statistics or codingSpecifically designed to accommodate students with a variety of math backgroundsProvides cheatsheets of statistical concepts and R codeSupporting materials available online, including real-world datasets and the code to analyze them, plus-for instructor use-sample syllabi, sample lecture slides, additional datasets, and additional exercises with solutions"-- Provided by publisher. Social sciences Statistical methods. Social sciences Data processing. Social sciences Methodology. SOCIAL SCIENCE / Statistics bisacsh COMPUTERS / Data Science / Data Analytics bisacsh Imai, Kosuke author. 48508 Online version: Llaudet, Elena, 1978- Data analysis for social science Princeton : Princeton University Press, [2023] 9780691229348 (DLC) 2022030107 7 cbc orignew 1 ecip 20 y-gencatlg lcc 0 BK llh 127133 127133 0 0 lcc 0 0 APT BJB BJB APT 2026-05-18 0 HA29 .L791d 2023 00000200112 2026-05-18 17:34:31 1 2026-05-18 BK