000			04884cam a2200457 i 4500
001			22739175
003			OSt
005			20250423070653.0
008			220805s2023 njua b 001 0 eng
010			_a 2022030106
020			_a9780691199436 _q(paperback ; alk. paper)
020			_a9780691199429 _q(hardback ; alk. paper)
020			_z9780691229348 _q(ebook)
040			_aDLC _beng _erda _cAmiu _dDLC
042			_apcc
050	0	0	_aHA29 _b.L835339 2023
082	0	0	_a519.5 _223/eng/20220805
084			_aSOC027000 _aCOM021030 _2bisacsh
100	1		_aLlaudet, Elena, _d1978- _eauthor.
245	1	0	_aData analysis for social science : _ba friendly and practical introduction / _cElena Llaudet and Kosuke Imai.
264		1	_aPrinceton ; _aOxford : _bPrinceton University Press, _cc2022.
300			_a1 online Resource xii, 238 pages : _bcolor illustrations ; _c26 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
520			_a"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"-- _cProvided by publisher.
520			_a"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"-- _cProvided by publisher.
583			_aCataloging Notes: _c20250423 _kSTAMIU-0199STAMIU-0199
650		0	_aSocial sciences _xStatistical methods.
650		0	_aSocial sciences _xData processing.
650		0	_aSocial sciences _xMethodology.
650		7	_aSOCIAL SCIENCE / Statistics _2bisacsh
650		7	_aCOMPUTERS / Data Science / Data Analytics _2bisacsh
700	1		_aImai, Kosuke, _eauthor.
776	0	8	_iOnline version: _aLlaudet, Elena, 1978- _tData analysis for social science _dPrinceton : Princeton University Press, [2023] _z9780691229348 _w(DLC) 2022030107
856			_uhttps://www.perlego.com/book/3563384 _zAccess online at Perlego
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cEBK _n0
999			_c21305 _d21305