Leseprobe
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Quantitative Social Science
Kosuke Imai, Lori D. Bougher
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Sozialwissenschaften, Recht, Wirtschaft / Methoden der empirischen und qualitativen Sozialforschung
Beschreibung
The Stata edition of the groundbreaking textbook on data analysis and statistics for the social sciences and allied fields
Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as business, economics, education, political science, psychology, sociology, public policy, and data science.
Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the Stata statistical software and interpret the results—it emphasizes hands-on learning, not paper-and-pencil statistics. More than fifty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior.
Proven in classrooms around the world, this one-of-a-kind textbook features numerous additional data analysis exercises, and also comes with supplementary teaching materials for instructors.
- Written especially for students in the social sciences and allied fields, including business, economics, education, psychology, political science, sociology, public policy, and data science
- Provides hands-on instruction using Stata, not paper-and-pencil statistics
- Includes more than fifty data sets from actual research for students to test their skills on
- Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools
- Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises
- Offers a solid foundation for further study
- Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides
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Confidence interval, Regression toward the mean, Central limit theorem, Estimator, Sampling (statistics), Standard error, Joint probability distribution, Randomized experiment, Probability, Observational study, Bernoulli distribution, Population proportion, Result, One-Tailed Test, Calculation, Prediction, Fisher's exact test, Summation, Statistical hypothesis testing, World population estimates, Variance, Law of total variance, Cross-validation (statistics), Data set, Probability distribution, Chi-squared test, Error, Histogram, Quantile, Standard score, Interquartile range, Correlation and dependence, Null hypothesis, False positive rate, Binomial distribution, Minimum wage, Root-mean-square deviation, False discovery rate, Monte Carlo method, Z-test, Alternative hypothesis, Random variable, Regression discontinuity design, Law of large numbers, Stata, Margin of error, Average treatment effect, Normal distribution, Empirical distribution function, Standard deviation, P-value, Inference, Fair coin, Variable (computer science), Dummy variable (statistics), Proportionality (mathematics), Variable (mathematics), Accuracy and precision, Bias of an estimator, Test statistic, Weighted arithmetic mean, Betweenness, Linear regression, Estimation, Summary statistics, Least squares, Causal inference, Error term, Randomization, Student's t-test