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Regression Analysis For The Social Sciences

Author: Rachel A. Gordon
Publisher: Routledge
ISBN: 1317607112
Size: 68.10 MB
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Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. 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 Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.

Regression Analysis For Social Sciences

Author: Alexander von Eye
Publisher: Elsevier
ISBN: 0080550827
Size: 68.37 MB
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Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the social and behavioral sciences as well as biology, making the book useful for readers with biological and biometrical backgrounds. Sample command and result files for SYSTAT are included in the text. Presents accessible methods of regression analysis Includes a broad spectrum of methods Techniques are explained step-by-step Provides sample command and result files for SYSTAT

Multiple Regression Analysis

Author: Kunal Gaurav
Publisher: GRIN Verlag
ISBN: 3656081654
Size: 52.24 MB
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Seminar paper from the year 2010 in the subject Economics - Statistics and Methods, grade: A, - (All India Management Association, New Dehli, India), course: PhD, language: English, abstract: This is well known fact that the success of social science research heavily depends upon the selection of research tools and its effective utilization. Researchers often come across the situations where they want to study the impact of one variable on the other variable viz. impact of income on expenditure. Although we have freedom to select research tools for multivariate analysis as wide range of research tools are available, multiple regression analysis allows us to determine the effect of more than one independent variable on dependent variable. This term paper talks about the concept of multiple regression analysis, its assumptions, application, and its limitations to the social science research. The paper also briefs about various statistics associated with multiple regression analysis.

Applied Logistic Regression Analysis

Author: Scott Menard
Publisher: SAGE
ISBN: 9780761922087
Size: 21.63 MB
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The focus in this Second Edition is on logistic regression models for individual level (but aggregate or grouped) data. Multiple cases for each possible combination of values of the predictors are considered in detail and examples using SAS and SPSS included. New to this edition: · More detailed consideration of grouped as opposed to casewise data throughout the book · Updated discussion of the properties and appropriate use of goodness of fit measures, R2 analogues, and indices of predictive efficiency · Discussion of the misuse of odds ratios to represent risk ratios, and of overdispersion and underdispersion for grouped data · Updated coverage of unordered and ordered polytomous logistic regression models.

Understanding Regression Analysis

Author: Larry D. Schroeder
Publisher: SAGE
ISBN: 9780803927582
Size: 62.86 MB
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The authors have provided beginners with a background to the frequently-used technique of linear regression. It is not intended to be a substitute for a course or textbook in statistics, but rather a stop-gap for students who encounter empirical work before undertaking a statistics course. It provides a heuristic explanation of the procedures and terms used in regression analysis and has been written at the most elementary level.

Spatial Regression Models

Author: Michael D. Ward
Publisher: SAGE Publications
ISBN: 1544328818
Size: 50.92 MB
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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.

Regression Analysis

Author: Richard A. Berk
Publisher: SAGE
ISBN: 0761929045
Size: 57.88 MB
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Regression Analysis: A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved. Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. The emphasis on description provides readers with an insightful rethinking from the ground up of what regression analysis can do, so that readers can better match regression analysis with useful empirical questions and improved policy-related research. "An interesting and lively text, rich in practical wisdom, written for people who do empirical work in the social sciences and their graduate students." --David A. Freedman, Professor of Statistics, University of California, Berkeley

Logistic Regression

Author: Fred C. Pampel
Publisher: SAGE Publications
ISBN: 1452207615
Size: 57.44 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.

Regression Analysis For Categorical Moderators

Author: Herman Aguinis
Publisher: Guilford Press
ISBN: 9781572309692
Size: 26.75 MB
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Does the stability of personality vary by gender or ethnicity? Does a particular therapy work better to treat clients with one type of personality disorder than those with another? Providing a solution to thorny problems such as these, Aguinis shows readers how to better assess whether the relationship between two variables is moderated by group membership through the use of a statistical technique, moderated multiple regression (MMR). Clearly written, the book requires only basic knowledge of inferential statistics. It helps students, researchers, and practitioners determine whether a particular intervention is likely to yield dissimilar outcomes for members of various groups. Associated computer programs and data sets are available at the author's website (http: // haguinis/mmr).

Fixed Effects Regression Models

Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 1483389278
Size: 21.85 MB
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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