Interaction Effects in Multiple Regression


Author: James Jaccard,Robert Turrisi
Publisher: SAGE Publications
ISBN: 1544332572
Category: Social Science
Page: 104
View: 8541

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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

Interaction Effects in Logistic Regression


Author: James Jaccard,Jim Jaccard
Publisher: SAGE
ISBN: 9780761922070
Category: Mathematics
Page: 70
View: 5497

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This book provides an introduction to the analysis of interaction effects in logistic regression by focusing on the interpretation of the coefficients of interactive logistic models for a wide range of situations encountered in the research literature. The volume is oriented toward the applied researcher with a rudimentary background in multiple regression and logistic regression and does not include complex formulas that could be intimidating to the applied researcher.

Interaction Effects in Linear and Generalized Linear Models

Examples and Applications Using Stata
Author: Robert L. Kaufman
Publisher: SAGE Publications
ISBN: 1506365396
Category: Social Science
Page: 608
View: 3518

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Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects. The book develops the statistical basis for the general principles of interpretive tools and applies them to a variety of examples, introduces the ICALC Toolkit for Stata (downloadable from the Robert L. Kaufman’s website), and offers a series of start-to-finish application examples to show students how to interpret interaction effects for a variety of different techniques of analysis, beginning with OLS regression. The data sets and the Stata code to reproduce the results of the application examples are available online.

Regression with Dummy Variables


Author: Melissa A. Hardy
Publisher: SAGE
ISBN: 9780803951280
Category: Social Science
Page: 90
View: 9628

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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.

Applied Statistics: From Bivariate Through Multivariate Techniques

From Bivariate Through Multivariate Techniques
Author: Rebecca M. Warner
Publisher: SAGE
ISBN: 141299134X
Category: Mathematics
Page: 1172
View: 9683

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Rebecca M. Warner's Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked to think about the meaning of equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method. Although SPSS examples are used throughout the book, the conceptual material will be helpful for users of different programs. Each chapter has a glossary and comprehension questions.

LISREL Approaches to Interaction Effects in Multiple Regression


Author: James Jaccard,Choi K Wan,Jim Jaccard
Publisher: SAGE
ISBN: 9780803971790
Category: Social Science
Page: 98
View: 5100

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With detailed examples, this book demonstrates the use of the computer program LISREL and how it can be applied to the analysis of interactions in regression frameworks. The authors consider a wide range of applications including: qualitative moderator variables; longitudinal designs; and product term analysis. They describe different types of measurement error and then present a discussion of latent variable representations of measurement error which serves as the foundation for the analyses described in later chapters. Finally they offer a brief introduction to LISREL and show how it can be used to execute the analyses. Readers can use this book without any prior training in LISREL and will find it an excellent introduction to analytic methods that deal with the problem of measurement error in the analysis of interactions.

Argumente, Bildung und Moral

Eine empirische Untersuchung zu Kohlbergs Theorie des moralischen Urteils
Author: Hermann Dülmer
Publisher: VS Verlag für Sozialwissenschaften
ISBN: N.A
Category: Social Science
Page: 295
View: 9250

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Vor dem Hintergrund von Kohlbergs Theorie des moralischen Urteils wird anhand zweier exemplarisch ausgewählter Konfliktbereiche empirisch untersucht, welchen Einfluss Argumente auf das moralische Urteil ausüben und ob dieser Einfluss vom erreichten Bildungsniveau abhängt.

Quantitative Methods in Social Work

State of the Art
Author: David F. Gillespie,Charles Glisson
Publisher: Psychology Press
ISBN: 9781560242741
Category: Social Science
Page: 228
View: 2505

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Representing some of the best research efforts currently found among social workers, Quantitative Methods in Social Work serves as both a guide and a challenge to social work researchers interested in the application of quantitative methods to social work problem solving. This application of research methods has not been described or discussed adequately in any formal way until now. In a comprehensive manner, this book documents the most advanced quantitative methodologies currently applied by social work researchers and describes issues and techniques that accompany specific applications. It increases social workers'understanding of state-of-the-art applied statistical analysis, enabling them to become more competent and competitive in research and the teaching of research strategies. Quantitative Methods in Social Work addresses three types of methodological issues: measurement, the incorporation of nonquantitative variables in quantitative data analysis, and the use of quantitative analytic techniques to model and assess complex social phenomena. Chapters cover the use of computers for content analysis, structural equation modeling in measurement, logistic regression, loglinear analysis, event history analysis, social network analysis, and discussions of moderator variables and interaction effects in multiple regression. Social work faculty and doctoral students, along with other human service professionals who want to increase their understanding of applied statistical analysis in social and behavioral research, will find the information they need in this informative book.

Logit Modeling

Practical Applications
Author: Alfred DeMaris
Publisher: SAGE
ISBN: 9780803943773
Category: Business & Economics
Page: 87
View: 7435

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Logit Modeling represents a breakthrough for researchers because it offers ways for more efficient estimation of models with multiple categorical variables, particularly whenever the measurement assumptions for classical multiple regression fail to be met. Taking an applied approach, DeMaris begins by describing the logit model in the context of the general loglinear model, moving its application from two-way to multidimensional tables. He then divides the rest of the book between an examination of the varieties of logit models for contingency tables and logistic regression. Throughout his coverage of both these major areas, DeMaris emphasizes interpretation of results. The book concludes with an extension of logistic regression to dependent variables with more than two categories.

Understanding Regression Assumptions


Author: William D. Berry
Publisher: SAGE
ISBN: 9780803942639
Category: Mathematics
Page: 91
View: 2238

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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.

Applied Regression Analysis and Generalized Linear Models


Author: John Fox
Publisher: SAGE Publications
ISBN: 1483321312
Category: Social Science
Page: 816
View: 3099

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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.

Logistic Regression Models for Ordinal Response Variables


Author: Ann A. O'Connell
Publisher: SAGE
ISBN: 9780761929895
Category: Mathematics
Page: 107
View: 556

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Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioral sciences with an accessible and comprehensive coverage of analyses for ordinal outcomes. The content builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal data. Description and examples of partial proportional odds models are also provided. This book is highly readable, with lots of examples and in-depth explanations and interpretations of model characteristics.

Applied logistic regression analysis


Author: Scott W. Menard
Publisher: Sage Publications, Inc
ISBN: N.A
Category: Mathematics
Page: 98
View: 7581

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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.

Interaction Effects in Factorial Analysis of Variance


Author: James Jaccard,Jim Jaccard
Publisher: SAGE
ISBN: 9780761912217
Category: Mathematics
Page: 103
View: 2137

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Although factorial analysis is widely used in the social sciences, there is some confusion as to how to use the technique's most powerful feature - the evaluation of interaction effects. Written to remedy this situation, this book explores the issues underlying the effective analysis of interaction in factorial designs. It includes discussion of: different ways of characterizing interactions in ANOVA; interaction effects using traditional hypothesis testing approaches; and alternative analytic frameworks that focus on effect size methodology and interval estimation.

Spatial Regression Models


Author: Michael D. Ward,Kristian Skrede Gleditsch
Publisher: SAGE
ISBN: 1412954150
Category: Mathematics
Page: 99
View: 3397

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Assuming no prior knowledge this book is geared toward social science readers, unlike other volumes on this topic. The text illustrates concepts using well known international, comparative, and national examples of spatial regression analysis. Each example is presented alongside relevant data and code, which is also available on a Web site maintained by the authors.

The Association Graph and the Multigraph for Loglinear Models


Author: Harry J. Khamis
Publisher: SAGE
ISBN: 1452238952
Category: Mathematics
Page: 136
View: 7593

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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.

Fixed Effects Regression Models


Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 1483389278
Category: Social Science
Page: 136
View: 2148

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This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book is appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. Learn more about "The Little Green Book" - QASS Series! Click Here

Correlation and Regression Analysis

A Historian's Guide
Author: Thomas J. Archdeacon
Publisher: Univ of Wisconsin Press
ISBN: 9780299136543
Category: History
Page: 352
View: 7174

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In Correlation and Regression Analysis: A Historian's Guide Thomas J. Archdeacon provides historians with a practical introduction to the use of correlation and regression analysis. The book concentrates on the kinds of analysis that form the broad range of statistical methods used in the social sciences. It enables historians to understand and to evaluate critically the quantitative analyses that they are likely to encounter in journal literature and monographs reporting research findings in the social sciences. Without attempting to be a text in basic statistics, the book provides enough background information to allow readers to grasp the essentials of correlation and regression. Correlation analysis refers to the measurement of association between or among variables, and regression analysis focuses primarily on the use of linear models to predict changes in the value taken by one variable in terms of changes in the values of a set of explanatory variables. The book also discusses diagnostic methods for identifying shortcomings in regression models, the use of regression to analyze causation, and the application of regression and related procedures to the study of problems containing categorical as well as numerical data. Archdeacon asserts that knowing how statistical procedures are computed can clarify the theoretical structures underlying them and is essential for recognizing the conditions under which their use is appropriate. The book does not shy away from the mathematics of statistical analysis; but Archdeacon presents concepts carefully and explains the operation of equations step by step. Unlike many works in the field, the book does not assume that readers have mathematical training beyond basic algebra and geometry. In the hope of promoting the role of quantitative analysis in his discipline, Archdeacon discusses the theory and methods behind the most important interpretive paradigm for quantitative research in the social sciences. Correlation and Regression Analysis introduces statistical techniques that are indispensable to historians and enhances the presentation of them with practical examples from scholarly works.