This Element introduces the basics of Bayesian regression modeling using modern computational tools and assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level. The sections cover theoret...Loe edasi...
Sari: Elements in Quantitative and Computational Methods for the Social Sciences
(Ilmumisaeg: 30-Sep-2025, Hardback, Kirjastus: Cambridge University Press, ISBN-13: 9781009598446)
This Element introduces the basics of Bayesian regression modeling using modern computational tools and assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level. The sections cover theoret...Loe edasi...
The accumulation of empirical evidence that has been collected in multiple contexts, places, and times requires a more comprehensive understanding of empirical research than is typically required for interpreting the findings from individual studies....Loe edasi...
Sari: Elements in Quantitative and Computational Methods for the Social Sciences
(Ilmumisaeg: 02-Jan-2025, Hardback, Kirjastus: Cambridge University Press, ISBN-13: 9781009486033)
The accumulation of empirical evidence that has been collected in multiple contexts, places, and times requires a more comprehensive understanding of empirical research than is typically required for interpreting the findings from individual studies....Loe edasi...
Sari: Elements in Quantitative and Computational Methods for the Social Sciences
(Ilmumisaeg: 24-Oct-2024, Hardback, Kirjastus: Cambridge University Press, ISBN-13: 9781009494694)
In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other than t...Loe edasi...
In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other than t...Loe edasi...
This Element discusses covers (i) the local randomization framework for RD analysis, (ii) the fuzzy RD design where compliance with treatment is imperfect, (iii) RD designs with discrete scores, and (iv) and multi-dimensional RD designs. In t...Loe edasi...
Matias D. Cattaneo, Nicolas Idrobo, Rocío Titiunik
Sari: Elements in Quantitative and Computational Methods for the Social Sciences
(Ilmumisaeg: 11-Apr-2024, Hardback, Kirjastus: Cambridge University Press, ISBN-13: 9781009462327)
In this Element, which continues our discussion in Foundations, the authors provide an accessible and practical guide for the analysis and interpretation of Regression Discontinuity (RD) designs that encourages the use of a common set of practices an...Loe edasi...
The goal of this Element is to provide a detailed introduction to adaptive inventories, an approach to making surveys adjust to respondents answers dynamically. This method can help survey researchers measure important latent traits or attitudes acc...Loe edasi...
Quantitative social scientists use survival analysis to understand the forces that determine the duration of events. This Element provides a guideline to new techniques and models in survival analysis, particularly in three areas: non-proportional co...Loe edasi...
In discrete choice models the relationships between the independent variables and the choice probabilities are nonlinear, depending on both the value of the particular independent variable being interpreted and the values of the other independent var...Loe edasi...
This Element provides the working social scientist with an overview of the most common methods for text classification, an intuition of their applicability, and Python code to execute them. It covers both the ethical foundations of such work as well...Loe edasi...
Dimension reduction offers researchers and scholars the ability to make complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques to efficiently repre...Loe edasi...
This Element discusses how shiny, an R package, can help instructors teach quantitative methods more effectively by way of interactive web apps. The interactivity increases instructors effectiveness by making students more active participants in the...Loe edasi...
Offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered,...Loe edasi...
Text is a fantastic resource for social scientists, but because it is so abundant, and so variable, it can be difficult to extract the information we want. Many basic text analysis methods are available as Python implementations: this Element will te...Loe edasi...
Nonresponse and other sources of bias are endemic features of public opinion surveys. We elaborate a general workflow of weighting-based survey inference, and describe in detail how this can be applied to the analysis of historical and contemporary o...Loe edasi...
Shows how innovation in computer vision methods can markedly lower the costs of using images as data. Introduces readers to deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. Provides gui...Loe edasi...
In this Element we develop: stochastic models, which add a crucial element of uncertainty to human interaction; models of human interactions structured by social networks; and evolutionary models in which agents using more effective decision rules...Loe edasi...
