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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Proportionality (mathematics), Summary statistics, Accuracy and precision, Bernoulli distribution, Randomization, Error term, Dummy variable (statistics), Test statistic, Standard error, Variable (computer science), Regression discontinuity design, Z-test, Average treatment effect, Central limit theorem, Confidence interval, Calculation, One-Tailed Test, Regression toward the mean, Fisher's exact test, Standard deviation, Randomized experiment, Normal distribution, Variance, Stata, Joint probability distribution, Law of large numbers, Error, Summation, Least squares, Probability distribution, Random variable, Estimator, Minimum wage, P-value, Student's t-test, Variable (mathematics), Histogram, False discovery rate, Bias of an estimator, Betweenness, Cross-validation (statistics), Linear regression, Weighted arithmetic mean, Monte Carlo method, Causal inference, Estimation, Population proportion, Observational study, Quantile, Correlation and dependence, Interquartile range, Probability, Binomial distribution, Chi-squared test, Margin of error, Fair coin, Null hypothesis, False positive rate, Data set, Standard score, World population estimates, Prediction, Statistical hypothesis testing, Root-mean-square deviation, Sampling (statistics), Result, Inference, Alternative hypothesis, Empirical distribution function, Law of total variance