Statistical Computing Environments For Social Research
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Statistical Computing Environments for Social Research
Author: Robert Stine
language: en
Publisher: SAGE Publications, Incorporated
Release Date: 1997
The nature of statistics has changed from classical notions of hypothesis testing toward graphical and exploratory data analysis that exploits the flexibility of interactive computing and graphical displays. With contributions from some of the leading researchers in the social sciences and statistics, Statistical Computing Environments for Social Research describes seven statistical computing environments--APL2STAT, GAUSS, Lisp-Stat, Mathematica, S, SAS/IML, and Stata--that can be used effectively in graphical and exploratory modeling. These statistical computing environments, in contrast to a standard statistical package, provide programming tools for building other statistical applications. Programmability, flexible data structures, and--in the case of some of the computing environments--graphical interfaces and object-oriented programming permit researchers to take advantage of emerging statistical methodologies. Three additional chapters, describing the Axis, R-code, and ViSta statistical packages, demonstrate how researchers have extended one of the computing environments--Lisp-Stat--to produce significant statistical applications employing graphical interfaces to statistical software. To illustrate the capabilities of the seven statistical computing environments, each contributor uses the same data set to perform three computing tasks: robust regression, bootstrap resampling, and kernel-density estimation. The same data are analyzed in the chapters on Axis, R-code, and ViSta packages. The chapters in Statistical Computing Environments for Social Research illustrate important ideas and techniques in modern data analysis and statistical computing, ideas and techniques that readers will be able to apply in the more effective analysis of their own data.
Doing Computational Social Science
Computational approaches offer exciting opportunities for us to do social science differently. This beginner’s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research. It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach in any discipline. The book also: Considers important principles of social scientific computing, including transparency, accountability and reproducibility. Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases. Empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed. For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work.
Applied Statistics Using R
If you want to learn to use R for data analysis but aren’t sure how to get started, this practical book will help you find the right path through your data. Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research. It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers. The book: Shows you how to use R packages and apply functions, adjusting them to suit different datasets. Gives you the tools to try new statistical techniques and empowers you to become confident using them. Encourages you to learn by doing when running and adapting the authors’ own code. Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect. Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or quantitative research methods courses.