Basic Math for Social Scientists

Author: Timothy Hagle
Publisher: SAGE
ISBN: 9780803958753
Format: PDF, Docs
Download Now
A review of the basic mathematical concepts that underlie most quantitative analysis in the social sciences is presented in this volume. The author begins with an algebra review featuring sets and combinations and then discusses limits and continuity. Calculus is presented next, with an introduction to differential calculus, multivariate functions, partial derivatives, and integral calculus. Finally the book deals with matrix algebra. Packed with helpful definitions, equations, examples and alternative notations, the book also includes a useful appendix of common mathematical symbols and Greek letters.

Fuzzy Set Theory

Author: Michael Smithson
Publisher: SAGE
ISBN: 9780761929864
Format: PDF, Kindle
Download Now
Fuzzy set theory deals with sets or categories whose boundaries are blurry or, in other words, "fuzzy." This book presents an accessible introduction to fuzzy set theory, focusing on its applicability to the social sciences. Unlike most books on this topic, Fuzzy Set Theory: Applications in the Social Sciences provides a systematic, yet practical guide for researchers wishing to combine fuzzy set theory with standard statistical techniques and model-testing.

The SAGE Encyclopedia of Social Science Research Methods

Author: Michael Lewis-Beck
Publisher: SAGE
ISBN: 9780761923633
Format: PDF, Mobi
Download Now
"The first encyclopedia to cover inclusively both quantitative and qualitative research approaches, this set provides clear explanations of 1,000 methodologies, avoiding mathematical equations when possible with liberal cross-referencing and bibliographies. Each volume includes a list of works cited, and the third contains a comprehensive index and lists of person names, organizations, books, tests, software, major concepts, surveys, and methodologies."--"Reference that rocks," American Libraries, May 2005.

Internet Data Collection

Author: Samuel J. Best
Publisher: SAGE
ISBN: 9780761927105
Format: PDF, Mobi
Download Now
Designed for researchers and students alike, the volume describes how to perform each stage of the data collection process on the Internet, including sampling, instrument design, and administration. Through the use of non-technical prose and illustrations, it details the options available, describes potential dangers in choosing them, and provides guidelines for sidestepping them. In doing so, though, it does not simply reiterate the practices of traditional communication modes, but approaches the Internet as a unique medium that necessitates its own conventions.

Differential Equations

Author: Courtney Brown
Publisher: SAGE
ISBN: 9781412941082
Format: PDF, Mobi
Download Now
Differential Equations: A Modeling Approach introduces differential equations and differential equation modeling to students and researchers in the social sciences. Key Features: - The text is accessibly written, so that students with minimal mathematical training can understand all of the basic concepts and techniques presented. - The author uses social sciences examples to illustrate the relevance of differential equation modeling to readers. - Readers can use graphical methods to produce penetrating analysis of differential equation systems. - Linear and nonlinear model specifications are explained from a social science perspective. Most interesting differential equation models are nonlinear, and readers need to know how to specify and work with such models in the social sciences.

Matrix Algebra

Author: Krishnan Namboodiri
Publisher: Chronicle Books
ISBN: 9780803920521
Format: PDF, ePub, Docs
Download Now
Defines basic terms and elementary matrices manipulation, introduces the concept of linear dependence, and explains eigenvalues and eigenvectors with illustrated examples. Learn more about "The Little Green Book" - QASS Series! Click Here

The Association Graph and the Multigraph for Loglinear Models

Author: Harry J. Khamis
Publisher: SAGE
ISBN: 1452238952
Format: PDF, Mobi
Download Now
The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.

Statistics for the Social Sciences

Author: R. Mark Sirkin
Publisher: SAGE
ISBN: 9781412905466
Format: PDF, ePub, Docs
Download Now
Popular in previous editions, this Third Edition continues to help build students' confidence and ability in doing statistical analysis by slowly moving from concepts that require little computational work to those that require more. Author R. Mark Sirkin once again demonstrates how statistics can be used so that students come to appreciate their usefulness rather than fear them. Statistics for the Social Sciences emphasizes the analysis and interpretation of data to give students a feel for how data interpretation is related to the methods by which the information was obtained.

Spatial Regression Models

Author: Michael D. Ward
Publisher: SAGE Publications
ISBN: 1544328818
Format: PDF, ePub
Download Now
Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including: mapping data on spatial units, exploratory spatial data analysis, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. Using social sciences examples based on real data, Michael D. Ward and Kristian Skrede Gleditsch illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.

Nonlinear Difference Equations

Author: H. Sedaghat
Publisher: Springer Science & Business Media
ISBN: 9401704171
Format: PDF, Kindle
Download Now
It is generally acknowledged that deterministic formulations of dy namical phenomena in the social sciences need to be treated differently from similar formulations in the natural sciences. Social science phe nomena typically defy precise measurements or data collection that are comparable in accuracy and detail to those in the natural sciences. Con sequently, a deterministic model is rarely expected to yield a precise description of the actual phenomenon being modelled. Nevertheless, as may be inferred from a study of the models discussed in this book, the qualitative analysis of deterministic models has an important role to play in understanding the fundamental mechanisms behind social sci ence phenomena. The reach of such analysis extends far beyond tech nical clarifications of classical theories that were generally expressed in imprecise literary prose. The inherent lack of precise knowledge in the social sciences is a fun damental trait that must be distinguished from "uncertainty. " For in stance, in mathematically modelling the stock market, uncertainty is a prime and indispensable component of a model. Indeed, in the stock market, the rules are specifically designed to make prediction impossible or at least very difficult. On the other hand, understanding concepts such as the "business cycle" involves economic and social mechanisms that are very different from the rules of the stock market. Here, far from seeking unpredictability, the intention of the modeller is a scientific one, i. e.