A Mathematical Primer for Social Statistics

Author: John Fox

Publisher: SAGE

ISBN: 1412960800

Category: Mathematics

Page: 170

View: 4319

Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. This handy volume introduces the areas of mathematics that are most important to applied social statistics.
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A Mathematics Course for Political and Social Research

Author: Will H. Moore,David A. Siegel

Publisher: Princeton University Press

ISBN: 140084861X

Category: Political Science

Page: 456

View: 2862

Political science and sociology increasingly rely on mathematical modeling and sophisticated data analysis, and many graduate programs in these fields now require students to take a "math camp" or a semester-long or yearlong course to acquire the necessary skills. Available textbooks are written for mathematics or economics majors, and fail to convey to students of political science and sociology the reasons for learning often-abstract mathematical concepts. A Mathematics Course for Political and Social Research fills this gap, providing both a primer for math novices in the social sciences and a handy reference for seasoned researchers. The book begins with the fundamental building blocks of mathematics and basic algebra, then goes on to cover essential subjects such as calculus in one and more than one variable, including optimization, constrained optimization, and implicit functions; linear algebra, including Markov chains and eigenvectors; and probability. It describes the intermediate steps most other textbooks leave out, features numerous exercises throughout, and grounds all concepts by illustrating their use and importance in political science and sociology. Uniquely designed and ideal for students and researchers in political science and sociology Uses practical examples from political science and sociology Features "Why Do I Care?" sections that explain why concepts are useful Includes numerous exercises Complete online solutions manual (available only to professors, email david.siegel at duke.edu, subject line "Solution Set") Selected solutions available online to students
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Statistics without Mathematics

Author: David J. Bartholomew

Publisher: SAGE

ISBN: 1473934338

Category: Social Science

Page: 192

View: 814

This is a book about the ideas that drive statistics. It is an ideal primer for students who need an introduction to the concepts of statistics without the added confusion of technical jargon and mathematical language. It introduces the intuitive thinking behind standard procedures, explores the process of informal reasoning, and uses conceptual frameworks to provide a foundation for students new to statistics. It showcases the expertise we have all developed from living in a data saturated society, increases our statistical literacy and gives us the tools needed to approach statistical mathematics with confidence. Key topics include: Variability Standard Distributions Correlation Relationship Sampling Inference An engaging, informal introduction this book sets out the conceptual tools required by anyone undertaking statistical procedures for the first time or for anyone needing a fresh perspective whilst studying the work of others.
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Log-Linear Models

Author: David Knoke,Peter J. Burke,Peter Burke

Publisher: SAGE

ISBN: 9780803914926

Category: Mathematics

Page: 80

View: 6687

Discusses the innovative log-linear model of statistical analysis. This model makes no distinction between independent and dependent variables, but is used to examine relationships among categoric variables by analyzing expected cell frequencies.
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Statistics Without Tears

An Introduction for Non-Mathematicians

Author: Derek Rowntree

Publisher: Penguin UK

ISBN: 9780141987491

Category:

Page: 208

View: 8645

This book is written for anyone who needs or wants to know how statistics work. It assumes no expert knowledge, and teaches through words and diagrams rather than through figures, formulae and equations - providing the perfect approach for the non-mathematical reader. Written as a "tutorial in print", Derek Rowntree includes questions in his argument; readers can answer them as they go, enabling them to measure their performance and judge how far they have mastered the subject.
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Heteroskedasticity in Regression

Detection and Correction

Author: Robert L. Kaufman

Publisher: SAGE Publications

ISBN: 1483322513

Category: Social Science

Page: 112

View: 1489

Heteroskedasticity in Regression: Detection and Correction, by Robert Kaufman, covers the commonly ignored topic of heteroskedasticity (unequal error variances) in regression analyses and provides a practical guide for how to proceed in terms of testing and correction. Emphasizing how to apply diagnostic tests and corrections for heteroskedasticity in actual data analyses, the monograph offers three approaches for dealing with heteroskedasticity: (1) variance-stabilizing transformations of the dependent variable; (2) calculating robust standard errors, or heteroskedasticity-consistent standard errors; and (3) generalized least squares estimation coefficients and standard errors. The detection and correction of heteroskedasticity is illustrated with three examples that vary in terms of sample size and the types of units analyzed (individuals, households, U.S. states). Intended as a supplementary text for graduate-level courses and a primer for quantitative researchers, the book fills the gap between the limited coverage of heteroskedasticity provided in applied regression textbooks and the more theoretical statistical treatment in advanced econometrics textbooks.
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Linear Regression

A Mathematical Introduction

Author: Damodar N. Gujarati

Publisher: SAGE Publications

ISBN: 154433656X

Category: Social Science

Page: 272

View: 1390

Damodar N. Gujarati’s Linear Regression: A Mathematical Introduction presents linear regression theory in a rigorous, but approachable manner that is accessible to students in all social sciences. This concise title goes step-by-step through the intricacies, and theory and practice of regression analysis. The technical discussion is provided in a clear style that doesn’t overwhelm the reader with abstract mathematics. End-of-chapter exercises test mastery of the content and advanced discussion of some of the topics is offered in the appendices.
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Integrative Statistics for the Social and Behavioral Sciences

Author: Renee R. Ha,James C. Ha

Publisher: SAGE

ISBN: 1452237298

Category: Mathematics

Page: 432

View: 7794

Designed to engage students and lower their "fear factor", Integrative Statistics for the Social and Behavioral Sciences is a concise, user-friendly text that prepares students to use statistics in the real world. Providing depth and breadth of statistical tests, the text focuses on choosing the appropriate statistical analysis, and shows how to interpret the output and present the results. Basic descriptive statistics, hypothesis testing, and basic inferential statistics are covered along with more advanced topics such as correlation, regression, non-parametric statistics, multivariate statistics, and general linear modeling. The authors emphasize choosing the appropriate statistical test through conceptual material, assumptions, homework exercises, and a helpful "choose-the-appropriate-test" flowchart. They focus on the interpretation of results from both Excel and SPSS outputs, and also demonstrate how to do important calculations by hand to help students grasp the underlying concepts. The book includes end-of-chapter exercises that help students fully grasp the content of each chapter.
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Modern methods of data analysis

Author: John Fox,J. Scott Long

Publisher: Sage Publications, Inc

ISBN: N.A

Category: Mathematics

Page: 446

View: 7723

This volume seeks to move the standard of statistical analysis and presentation in the social sciences towards an accurate and sensitive representation of data. This is accomplished by focusing on four themes: an emphasis on graphical data analysis; the use of computers for doing intensive computations; regression analysis; and sampling characteristics of data.
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Applied Statistics for the Social and Health Sciences

Author: Rachel A. Gordon

Publisher: Routledge

ISBN: 0415875366

Category: Mathematics

Page: 742

View: 7457

Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with the basic skills that they need to estimate, interpret, present, and publish statistical models using contemporary standards. The book targets the social and health science branches such as human development, public health, sociology, psychology, education, and social work in which students bring a wide range of mathematical skills and have a wide range of methodological affinities. For these students, a successful course in statistics will not only offer statistical content but will also help them develop an appreciation for how statistical techniques might answer some of the research questions of interest to them. This book is for use in a two-semester graduate course sequence covering basic univariate and bivariate statistics and regression models for nominal and ordinal outcomes, in addition to covering ordinary least squares regression. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature thorough integration of teaching statistical theory with teaching data processing and analysis teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set. This book is for a two-semester course. For a one-semester course, see http://www.routledge.com/9780415991544/
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Quantitative Narrative Analysis

Author: Roberto Franzosi

Publisher: SAGE

ISBN: 1412925258

Category: Language Arts & Disciplines

Page: 175

View: 6084

Researchers and students in the social sciences.
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Understanding Multivariate Research

A Primer For Beginning Social Scientists

Author: William Berry,Mitchell Sanders

Publisher: Westview Press

ISBN: 9780813346281

Category: Political Science

Page: 104

View: 3681

Although nearly all major social science departments offer graduate students training in quantitative methods, the typical sequencing of topics generally delays training in regression analysis and other multivariate techniques until a student's second year. William Berry and Mitchell Sanders's Understanding Multivariate Research fills this gap with a concise introduction to regression analysis and other multivariate techniques. Their book is designed to give new graduate students a grasp of multivariate analysis sufficient to understand the basic elements of research relying on such analysis that they must read prior to their formal training in quantitative methods. Berry and Sanders effectively cover the techniques seen most commonly in social science journals--regression (including nonlinear and interactive models), logit, probit, and causal models/path analysis. The authors draw on illustrations from across the social sciences, including political science, sociology, marketing and higher education. All topics are developed without relying on the mathematical language of probability theory and statistical inference. Readers are assumed to have no background in descriptive or inferential statistics, and this makes the book highly accessible to students with no prior graduate course work.
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Model Based Inference in the Life Sciences

A Primer on Evidence

Author: David R. Anderson

Publisher: Springer Science & Business Media

ISBN: 9780387740751

Category: Science

Page: 184

View: 2481

This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.
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Applied Regression Analysis and Generalized Linear Models

Author: John Fox

Publisher: SAGE Publications

ISBN: 1483321312

Category: Social Science

Page: 816

View: 9806

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.
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Calculus

Author: Gudmund R. Iversen

Publisher: SAGE

ISBN: 9780803971103

Category: Mathematics

Page: 82

View: 1866

This book offers an overview of the central ideas in calculus and gives examples of how calculus is used to translate many real-world phenomena into mathematical functions. Beginning with an explanation of the two major parts of calculus - differentiation and integration - Gudmund R Iversen illustrates how calculus is used in statistics: to distinguish between the mean and the median; to derive the least squares formulas for regression co-efficients; to find values of parameters from theoretical distributions; and to find a statistical p-value when using one of the continuous test variables such as the t-variable.
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Applied Regression Analysis and Generalized Linear Models

Author: John Fox

Publisher: SAGE Publications

ISBN: 1483321312

Category: Social Science

Page: 816

View: 4484

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.
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The Association Graph and the Multigraph for Loglinear Models

Author: Harry J. Khamis

Publisher: SAGE

ISBN: 1452238952

Category: Mathematics

Page: 136

View: 8361

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.
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Statistical Methods for Categorical Data Analysis

Author: Daniel Powers,Yu Xie

Publisher: Emerald Group Publishing

ISBN: 9781781906590

Category: Psychology

Page: 296

View: 1559

This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. Companion website also available, at https://webspace.utexas.edu/dpowers/www/
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A Research Primer for the Social and Behavioral Sciences

Author: Miriam Schapiro Grosof,Hyman Sardy

Publisher: Academic Press

ISBN: 1483258017

Category: Social Science

Page: 450

View: 656

A Research Primer for the Social and Behavioral Sciences provides an introductory but comprehensive overview of the research process that primarily concerns human subjects. This book discusses the methods of acquiring knowledge, importance of a well-chosen problem, review of the literature, and relationship between theory-building and hypothesis-testing. The common sources of invalidity in practice, non-experimental research types, Stevens' classification of scales, and estimation based on probabilistic sampling are also elaborated. This text likewise covers the role of computer in research, techniques for analysis of data, univariate and bivariate statistics, and assumptions underlying analysis of variance. Other topics include the canonical correlation analysis, non-parametric analysis of variance, deterministic problem analysis techniques, and common errors in presentation of findings. This publication is intended for novice investigators in the broad category of social and behavioral sciences.
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