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A Mathematical Primer For Social Statistics

Author: John Fox
Publisher: SAGE
ISBN: 1412960800
Size: 71.19 MB
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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.

Interpreting Probability Models

Author: Tim Futing Liao
Publisher: SAGE
ISBN: 9780803949997
Size: 73.17 MB
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What is the probability that something will occur, and how is that probability altered by a change in an independent variable? To answer these questions, Tim Futing Liao introduces a systematic way of interpreting commonly used probability models. Since much of what social scientists study is measured in noncontinuous ways and, therefore, cannot be analyzed using a classical regression model, it becomes necessary to model the likelihood that an event will occur. This book explores these models first by reviewing each probability model and then by presenting a systematic way for interpreting the results from each.

Log Linear Models

Author: David Knoke
Publisher: SAGE
ISBN: 9780803914926
Size: 17.22 MB
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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.

Model Based Inference In The Life Sciences

Author: David R. Anderson
Publisher: Springer Science & Business Media
ISBN: 9780387740751
Size: 41.68 MB
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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.

Heteroskedasticity In Regression

Author: Robert L. Kaufman
Publisher: SAGE Publications
ISBN: 1483322513
Size: 14.72 MB
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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.

Mathematics For Social Scientists

Author: Jonathan Kropko
Publisher: SAGE Publications
ISBN: 1506304222
Size: 50.82 MB
Format: PDF, Kindle
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Written for social science students who will be working with or conducting research, Mathematics for Social Scientists offers a non-intimidating approach to learning or reviewing math skills essential in quantitative research methods. The text is designed to build students’ confidence by presenting material in a conversational tone and using a wealth of clear and applied examples. Author Jonathan Kropko argues that mastering these concepts will break students’ reliance on using basic models in statistical software, allowing them to engage with research data beyond simple software calculations.

Event History Analysis

Author: Paul D. Allison
Publisher: SAGE
ISBN: 9780803920552
Size: 14.46 MB
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Drawing on recent "event history" analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events. Allison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem. He then discusses the particular new methods that social scientists should find useful.

Applied Regression Analysis And Generalized Linear Models

Author: John Fox
Publisher: SAGE Publications
ISBN: 1483321312
Size: 49.35 MB
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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.

Simulation For The Social Scientist

Author: Gilbert, Nigel
Publisher: McGraw-Hill Education (UK)
ISBN: 9780335216000
Size: 80.35 MB
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Social sciences -- Simulation methods. Social interaction -- Computer simulation. Social sciences -- Mathematical models. (publisher)

Basic Content Analysis

Author: Robert Philip Weber
Publisher: SAGE
ISBN: 9780803938632
Size: 47.23 MB
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This second edition of Basic Content Analysis is completely updated and offers a concise introduction to content analysis methods from a social science perspective. It includes new computer applications, new studies and an additional chapter on problems and issues that can arise when carrying out content analysis in four major areas: measurement, indication, representation and interpretation.