Carta PAYBACK di Mondadori
Su Mondadori Store, con la tua carta PAYBACK ti premi ad ogni acquisto.
William M. Holmes
pubblicato da
SAGE Publications
Using an accessible approach perfect for social and behavioral science students (requiring minimal use of matrix and vector algebra), Holmes examines how propensity scores can be used to both reduce bias with different kinds of quasi-experimental designs and fix or improve broken experiments. This unique book covers the causal assumptions of propensity score estimates and their many uses, linking these uses with analysis appropriate for different designs. Thorough coverage of bias assessment, propensity score estimation, and estimate improvement is provided, along with graphical and statistical methods for this process. Applications are included for analysis of variance and covariance, maximum likelihood and logistic regression, two-stage least squares, generalized linear regression, and general estimation equations. The examples use public data sets that have policy and programmatic relevance across a variety of social and behavioral science disciplines.
Scrivi una recensione per "Using Propensity Scores in Quasi-Experimental Designs"
Torna in cima
Leggi un estratto
dai un voto
