Leseprobe
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Quantitative Social Science
Kosuke Imai
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Sozialwissenschaften, Recht, Wirtschaft / Methoden der empirischen und qualitativen Sozialforschung
Beschreibung
An introductory textbook on data analysis and statistics written especially for students in 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 economics, sociology, public policy, and data science.
Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty 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 the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors.
- Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science
- Provides hands-on instruction using R programming, not paper-and-pencil statistics
- Includes more than forty 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
Looking for a more accessible introduction? Consider Data Analysis for Social Science by Elena Llaudet and Kosuke Imai, which teaches from scratch and step-by-step the fundamentals of survey research, predictive models, and causal inference. It covers descriptive statistics, the difference-in-means estimator, simple linear regression, and multiple linear regression.
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Coefficient, Box plot, Error, Histogram, Quartile, P-value, Binomial distribution, K-means clustering, Expected value, Bias of an estimator, Bayesian, Estimation, One-Tailed Test, Error term, False discovery rate, Inference, Percentage point, Confounding, Monte Carlo method, Minimum wage, Data set, Point estimation, Interquartile range, Equation, Causal inference, Central limit theorem, Law of total variance, Betweenness, Fisher's exact test, Birthday problem, Calculation, Coefficient of determination, Average treatment effect, Estimator, Measurement, Observational study, Internal validity, Bernoulli distribution, Parameter (computer programming), Cross-validation (statistics), PageRank, Population proportion, Multiple comparisons problem, Null hypothesis, Margin of error, Proportionality (mathematics), Confidence interval, Joint probability distribution, Prediction, Probability, Accuracy and precision, Combination, Empirical distribution function, Percentage, Cartesian coordinate system, Probability distribution, Correlation and dependence, Least squares, Linear regression, Law of large numbers, Addition, Normal distribution, Quantile, Fair coin, Random variable, Conditional probability, Permutation, Alternative hypothesis, Mutual exclusivity, Exploratory data analysis