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Regression Analysis For The Social Sciences

Author: Rachel A. Gordon
Publisher: Routledge
ISBN: 1317607112
Size: 65.74 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: 76.20 MB
Format: PDF
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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: 17.85 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.

Correlation And Regression Analysis

Author: Thomas J. Archdeacon
Publisher: Univ of Wisconsin Press
ISBN: 9780299136543
Size: 68.39 MB
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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.

Time Series Analysis For The Social Sciences

Author: Janet M. Box-Steffensmeier
Publisher: Cambridge University Press
ISBN: 0521871166
Size: 76.33 MB
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This book provides instruction and examples of the core methods in time series econometrics, drawing from several main fields of the social sciences.

Applied Regression Analysis And Generalized Linear Models

Author: John Fox
Publisher: SAGE Publications
ISBN: 1483321312
Size: 36.40 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.

Multivariate Analysis Techniques In Social Science Research

Author: J. J. A. Tacq
Publisher: SAGE
ISBN: 9781446237410
Size: 50.34 MB
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Unlike most statistical texts, this book breathes real life into multivariate analysis. Starting with a range of actual research examples in the social sciences, it demonstrates how to make the most appropriate choice of technique. The examples are drawn from a broad spectrum of disciplines including: sociology, psychology, economics, political science and international comparative research.

Applied Logistic Regression Analysis

Author: Scott Menard
Publisher: SAGE
ISBN: 9780761922087
Size: 55.49 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.

Applied Regression

Author: Colin Lewis-Beck
Publisher: SAGE Publications
ISBN: 1483381498
Size: 75.10 MB
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Known for its readability and clarity, this Second Edition of the best-selling Applied Regression provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. Authors Colin Lewis-Beck and Michael Lewis-Beck then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.