Data Analysis for Social Science (eBook)

A Friendly and Practical Introduction

Kosuke Imai, Elena Llaudet (Autoren)

eBook Download: PDF

2022
256 Seiten
Princeton University Press (Verlag)
978-0-691-22934-8 (ISBN)

Infos

An ideal textbook for complete beginnersteaches from scratch R, statistics, and the fundamentals of quantitative social scienceData Analysis for Social Science provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Assuming no prior knowledge of statistics and coding and only minimal knowledge of math, the book teaches the fundamentals of survey research, predictive models, and causal inference while analyzing data from published studies with the statistical program R. It teaches not only how to perform the data analyses but also how to interpret the results and identify the analyses' strengths and limitations.Progresses by teaching how to solve one kind of problem after another, bringing in methods as needed. It teaches, in this order, how to (1) estimate causal effects with randomized experiments, (2) visualize and summarize data, (3) infer population characteristics, (4) predict outcomes, (5) estimate causal effects with observational data, and (6) generalize from sample to population.Flips the script of traditional statistics textbooks. It starts by estimating causal effects with randomized experiments and postpones any discussion of probability and statistical inference until the final chapters. This unconventional order engages students by demonstrating from the very beginning how data analysis can be used to answer interesting questions, while reserving more abstract, complex concepts for later chapters.Provides a step-by-step guide to analyzing real-world data using the powerful, open-source statistical program R, which is free for everyone to use. The datasets are provided on the book's website so that readers can learn how to analyze data by following along with the exercises in the book on their own computer.Assumes no prior knowledge of statistics or coding.Specifically designed to accommodate students with a variety of math backgrounds. It includes supplemental materials for students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose.Provides cheatsheets of statistical concepts and R code.Comes with instructor materials (upon request), including sample syllabi, lecture slides, and additional replication-style exercises with solutions and with the real-world datasets analyzed. Looking for a more advanced introduction? Consider Quantitative Social Science by Kosuke Imai. In addition to covering the material in Data Analysis for Social Science, it teaches diffs-in-diffs models, heterogeneous effects, text analysis, and regression discontinuity designs, among other things.

Produkt-Details

• Erscheint lt. Verlag: 20.12.2022
• Zusatzinfo: 57 color + 101 b/w illus. 33 tables.
• Sprache: englisch
• Themenwelt: Sachbuch/Ratgeber ► Natur / Technik ► Natur / Ökologie
• Themenwelt: Schulbuch / Wörterbuch ► Lexikon / Chroniken
• Themenwelt: Informatik ► Datenbanken ► Data Warehouse / Data Mining
• Themenwelt: Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung
• Schlagworte: Alternative hypothesis • average treatment effect • Bias of an estimator • Case sensitivity • causal inference • Causality • class size • Coding (social sciences) • Comma-separated values • Computer performance • Confidence interval • Control variable • Cosmic Evolution (book) • Cumulative distribution function • Data Analysis • Data Science • data set • Demography • descriptive statistics • Diagram (category theory) • Dummy variable (statistics) • Education Policy • Empirical distribution function • Energy development • epidemiology • estimation • Estimator • Experiment • Explanation • Floor and ceiling functions • Gaussian function • General Certificate of Secondary Education • Gravity Recovery and Interior Laboratory • Gross Domestic Product • histogram • hypothesis • Inequality (mathematics) • inference • Instance (computer science) • Instruction set • Intellectual Property • language interpretation • Likelihood Function • linear regression • Mathematical Notation • mathematician • Mathematics • Methodology • Normal distribution • Notation • null hypothesis • observational study • Parameter • Parameter (computer programming) • percentage • Percentage point • population characteristics • Prediction • Predictive Modelling • private sector • Probability • Programming language • Proportionality (mathematics) • p-value • quantitative research • Quantity • Radio Wave • Randomized experiment • Realization (probability) • Relational operator • result • RStudio • sampling distribution • Sampling (Statistics) • scientific notation • Social Science • Social science research • Socioeconomics • spreadsheet • Statistic • Statistical hypothesis testing • statistical significance • Statistics • Summation • Supply (economics) • survey methodology • Test score • test statistic • theory • Treatment and control groups • Uncertainty • Unit of observation • Units of measurement • Utilization • Variable (computer science) • Variable (mathematics) • Voting • Voting Behavior • Working directory • workspace
• ISBN-10: 0-691-22934-1 / 0691229341
• ISBN-13: 978-0-691-22934-8 / 9780691229348