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Applied Regression

Author: Michael S. Lewis-Beck
Publisher: Sage Publications, Inc
ISBN:
Size: 29.93 MB
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Applied regression allows social scientists who are not specialists in quantitative techniques to arrive at clear verbal explanations of their numerical results. Provides a lucid discussion of more specialized subjects: analysis of residuals, interaction effects, specification error, multicollinearity, standardized coefficients, and dummy variables.

Regression Diagnostics

Author: John Fox
Publisher: SAGE
ISBN: 9780803939714
Size: 42.79 MB
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With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that compromise a regression analysis and for determining whether certain assumptions appear reasonable. The book covers such topics as the problem of collinearity in multiple regression, dealing with outlying and influential data, non-normality of errors, non-constant error variance and the problems and opportunities presented by discrete data. In addition, sophisticated diagnostics based on maximum-likelihood methods, scores tests, and constructed variables are introduced.

Logistic Regression

Author: Fred C. Pampel
Publisher: SAGE Publications
ISBN: 1452207615
Size: 35.77 MB
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Pampel's book offers readers the first "nuts and bolts" approach to doing logistic regression through the use of careful explanations and worked-out examples. This book will enable readers to use and understand logistic regression techniques and will serve as a foundation for more advanced treatments of the topic.

Applied Logistic Regression Analysis

Author: Scott W. Menard
Publisher: Sage Publications, Inc
ISBN:
Size: 32.20 MB
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Emphasizing the parallels between linear and logistic regression, Scott Menard explores logistic regression analysis and demonstrates its usefulness in analyzing dichotomous, polytomous nominal, and polytomous ordinal dependent variables. The book is aimed at readers with a background in bivariate and multiple linear regression.

Understanding Regression Assumptions

Author: William D. Berry
Publisher: SAGE Publications
ISBN: 1506315828
Size: 26.91 MB
Format: PDF, ePub
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Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the substantive meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation.

Interpreting And Using Regression

Author: Christopher H. Achen
Publisher: SAGE Publications
ISBN: 145221011X
Size: 49.95 MB
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Interpreting and Using Regression sets out the actual procedures researchers employ, places them in the framework of statistical theory, and shows how good research takes account both of statistical theory and real world demands. Achen builds a working philosophy of regression that goes well beyond the abstract, unrealistic treatment given in previous texts.

Interaction Effects In Multiple Regression

Author: James Jaccard
Publisher: SAGE Publications
ISBN: 1544332572
Size: 75.88 MB
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Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis. Learn more about "The Little Green Book" - QASS Series! Click Here

A Mathematical Primer For Social Statistics

Author: John Fox
Publisher: SAGE
ISBN: 1412960800
Size: 27.15 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.

Regression With Dummy Variables

Author: Melissa A. Hardy
Publisher: SAGE
ISBN: 9780803951280
Size: 66.70 MB
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It is often necessary for social scientists to study differences in groups, such as gender or race differences in attitudes, buying behavior, or socioeconomic characteristics. When the researcher seeks to estimate group differences through the use of independent variables that are qualitative, dummy variables allow the researcher to represent information about group membership in quantitative terms without imposing unrealistic measurement assumptions on the categorical variables. Beginning with the simplest model, Hardy probes the use of dummy variable regression in increasingly complex specifications, exploring issues such as: interaction, heteroscedasticity, multiple comparisons and significance testing, the use of effects or contrast coding, testing for curvilinearity, and estimating a piecewise linear regression.

Reliability And Validity Assessment

Author: Edward G. Carmines
Publisher: SAGE Publications
ISBN: 1452207712
Size: 71.19 MB
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This guide explains how social scientists can evaluate the reliability and validity of empirical measurements, discussing the three basic types of validity: criterion related, content, and construct. In addition, the paper shows how reliability is assessed by the retest method, alternative-forms procedure, split-halves approach, and internal consistency method.