Robert P. Abelson – författare

In this illuminating volume, Robert P. Abelson delves into the too-often dismissed problems of interpreting quantitative data and then presenting them in the context of a coherent story about one's research. Unlike too many books on statistics, this is a remarkably engaging read, filled with fascinating real-life (and real-research) examples rather than with recipes for analysis. It will be of true interest and lasting value to beginning graduate students and seasoned researchers alike. The focus of the book is that the purpose of statistics is to organize a useful argument from quantitative evidence, using a form of principled rhetoric. Five criteria, described by the acronym MAGIC (magnitude, articulation, generality, interestingness, and credibility) are proposed as crucial features of a persuasive, principled argument. Particular statistical methods are discussed, with minimum use of formulas and heavy data sets. The ideas throughout the book revolve around elementary probability theory, t tests, and simple issues of research design. It is therefore assumed that the reader has already had some access to elementary statistics. Many examples are included to explain the connection of statistics to substantive claims about real phenomena.

First Published in 1977. In the summer of 1971, there was a workshop in an ill-defined field at the intersection of psychology, artificial intelligence, and linguistics. The fifteen participants were in various ways interested in the representation of large systems of knowledge (or beliefs) based upon an understanding process operating upon information expressed in natural language. This book reflects a convergence of interests at the intersection of psychology and artificial intelligence. What is the nature of knowledge and how is this knowledge used? These questions lie at the core of both psychology and artificial intelligence.

First Published in 1986. This book marks a watershed in cognitive science activity at Yale University. Over the past decade, the cognitive science orientation has become more and more integrated into the mainstream of cognitive psychology, and artificial intelligence workers now feel comfortable thinking about psychological experimentation. This book collects in one place the research work which concentrates on covering topics in the representation, processing, and recall of meaningful verbal .materials. Several of the chapters are first reports of research; others are specially prepared reviews and elaborations of research reported previously. Here it is all together: Studies of scripts, plans, and higher-level knowledge structures; analyses of knowledge structure activation, of autobiographical memory, of the phenomenon of reminding, of the summarization of text, of explanations for events, and more.

In Statistics as Principled Argument, first published in 1995, Robert P. Abelson exposes the often-overlooked problems in quantitative data interpretation, and the challenge of presenting the data in terms of a coherent narrative about one's research. This classic edition offers a new introduction which views Abelson's work through a contemporary lens, highlighting how his teachings remain vitally relevant today.The focus of the book is that the purpose of statistics is to organize a useful argument from quantitative evidence, using a form of principled rhetoric. Five criteria, described by the acronym MAGIC (magnitude, articulation, generality, interestingness, and credibility) are proposed as crucial features of a persuasive, principled argument. Statistical methods are discussed, but with minimum use of formulas and heavy data sets. The ideas throughout the book revolve around elementary probability theory, t-tests, and simple issues of research design. Many examples are included to explain the connection of statistics to substantive claims about real phenomena.This remarkably engaging read is filled with fascinating real-life (and real research) examples rather than simply recipes for analysis. Abelson’s insights generate true interest for beginning graduate students and seasoned researchers alike, and will be invaluable to students, scholars, researchers, and practitioners who use statistics within their research.