Advances in Social Science Research Using R

ISBN-13: 9781441917638 / Angielski / Miękka / 2010 / 205 str.

Hrishikesh D. Vinod

Opis

Quantitative social science research has been expanding due to the ava- ability of computers and data over the past few decades. Yet the textbooks and supplements for researchers do not adequately highlight the revolution created by the R software 2] and graphics system. R is fast becoming the l- gua franca of quantitative research with some 2000 free specialized packages, where the latest versions can be downloaded in seconds. Many packages such as "car" 1] developed by social scientists are popular among all scientists. An early 2009 article 3] in the New York Times notes that statisticians, engineers and scientists without computer programming skills ?nd R "easy to use." A common language R can readily promote deeper mutual respect and understanding of unique problems facing quantitative work in various social sciences. Often the solutions developed in one ?eld can be extended and used in many ?elds. This book promotes just such exchange of ideas across many social sciences. Since Springer has played a leadership role in promoting R, we are fortunate to have Springer publish this book. A Conference on Quantitative Social Science Research Using R was held in New York City at the Lincoln Center campus of Fordham University, June 18-19, 2009. This book contains selected papers presented at the conference, representing the "Proceedings" of the conference.

Recenzje

In the introduction of Advances in social Science Research Using R, the editor gives valuable short summaries of all chapters to point out that each one is of relevance beyond the scope of the disciplines where the methods or authors originate Several chapters are well suited for social scientists interested in new methodology and how to use it in R. These chapters not only introduce advanced statistical techniques, but also walk the reader through their usage in R in a concise and yet instructive way Other chapters show that complex econometric research questions can be addressed with R and illustrate the variety of advanced methods now available .  (Journal of Statistical Software, Vol. 34, Book Review 2, April 2010)

Spis treści

Econometric Computing with 'R' by B. D. McCullough.- Additive Models for Quantile Regression: An Analysis of Risk Factors for Malnutrition in India by Roger Koenker.- Toward better R defaults for graphics: Example of voter turnouts in US elections by Andrew Gelman.- Superior Estimation and Inference Avoiding Heteroscedasticity and Flawed Pivots: R-example of Inflation Unemployment Trade-Off by H. D. Vinod.- Bubble Plots as a Model-Free Graphical Tool for Continuous Variables by Keith A. Markus and Wen Gu.- Combinatorial Fusion for Improving Portfolio Performance by H. D. Vinod, D. F. Hsu and Y. Tian.- Reference growth charts for Saudi Arabian children and Adolescents by P. J. Foster and T. Kecojevic.- Causal Mediation Analysis Using R by K. Imai, L. Keele, D. Tingley, and T. Yamamoto.- Statistical validation of functional form in multiple regression using R by Harry Haupt, Joachim Schnurbus, and Rolf Tschernig.- Fitting Multinomial Models in R: A program based on Bock's multinomial response relation model by David Rindskopf.- A Bayesian Analysis of Leukemia Incidence Surrounding an Inactive Hazardous Waste Site by Ronald C. Neath.- Stochastic Volatility Model with Jumps in Returns and Volatility: An R-Package Implementation by Adjoa Numatsi and Erick W. Rengifo.

Blurb

This book covers recent advances for quantitative researchers with practical examples from social sciences. The twelve chapters written by distinguished authors cover a wide range of issues--all providing practical tools using the free R software.

McCullough: R can be used for reliable statistical computing, whereas most statistical and econometric software cannot. This is illustrated by the effect of abortion on crime.

Koenker: Additive models provide a clever compromise between parametric and non-parametric components illustrated by risk factors for Indian malnutrition.

Gelman: R graphics in the context of voter participation in US elections.

Vinod: New solutions to the old problem of efficient estimation despite autocorrelation and heteroscedasticity among regression errors are proposed and illustrated by the Phillips curve tradeoff between inflation and unemployment.

Markus and Gu: New R tools for exploratory data analysis including bubble plots.

Vinod, Hsu and Tian: New R tools for portfolio selection borrowed from computer scientists and data-mining experts; relevant to anyone with an investment portfolio.

Foster and Kecojevic: Extends the usual analysis of covariance (ANCOVA) illustrated by growth charts for Saudi children.

Imai, Keele, Tingley, and Yamamoto: New R tools for solving the age-old scientific problem of assessing the direction and strength of causation. Their job search illustration is of interest during current times of high unemployment.

Haupt, Schnurbus, and Tschernig: Consider the choice of functional form for an unknown, potentially nonlinear relationship, explaining a set of new R tools for model visualization and validation.

Rindskopf: R methods to fit a multinomial based multivariate analysis of variance (ANOVA) with examples from psychology, sociology, political science, and medicine. Neath: R tools for Bayesian posterior distributions to study increased disease risk in proximity to a hazardous waste site.

Numatsi and Rengifo: Explain persistent discrete jumps in financial series subject to misspecification.