At last--a social scientist's guide through the pitfalls of modern statistical computing
Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical guidebook to the numerical methods underlying computerized statistical calculations specific to these fields. The authors demonstrate that knowledge of these numerical methods and how they are used in statistical packages is essential for making accurate inferences. With the aid of key contributors from both the social and behavioral sciences, the authors have assembled a rich set of interrelated chapters designed to guide empirical social scientists through the potential minefield of modern statistical computing.
Uniquely accessible and abounding in modern-day tools, tricks, and advice, the text successfully bridges the gap between the current level of social science methodology and the more sophisticated technical coverage usually associated with the statistical field.
Highlights include:
Designed as an independent research tool, a professional reference, or a classroom supplement, the book presents a well-thought-out treatment of a complex and multifaceted field.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
MICAH ALTMAN is Associate Director of the Harvard-MIT Data Center in Cambridge, Massachusetts.
JEFF GILL is Associate Professor of Political Science at the University of California, Davis.
MICHAEL P. McDONALD is Assistant Professor of Government and Politics at George Mason University in Fairfax, Virginia.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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