Correlation and Regression Analysis

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Edited by: W. Paul Vogt & R. Burke Johnson

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    • Publisher: SAGE Publications Ltd |
    • Publication Year: 2012 |
    • Online Publication Date: February 05, 2015 |
    • DOI: 10.4135/9781446286104 |
    • Print ISBN: 9781848601703 |
    • Online ISBN: 9781446286104 |
    • Series: SAGE Benchmarks in Social Research Methods |
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Abstract

It is no exaggeration to say that virtually all quantitative research in the social sciences is done with correlation and regression analysis (CRA) and their siblings and offspring. CRA are fundamental analytic tools in fields like sociology, economics and political science as well as applied disciplines such as marketing, nursing, education and social work. The subject is of great substantive importance; therefore, distinguished editors, W. Paul Vogt and R. Burke Johnson, have ordered the growing research literature on the use of CRA according to its natural steps. Each step in this logical progression constitutes a part in this collection:

Part I. Regression and Its Correlational Foundations and Concomitants

Part II. Linear Regression Designs and Model Building

Part III. Inherently Nonlinear Models: Log-Linear Models And Probit And Logistic Regression

Part ...

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  • Editors' Introduction: Correlation and Regression Analysis
    W.PaulVogt and R.BurkeJohnson
    Introduction

    Correlation and regression analysis (CRA) are fundamental analytic tools for quantitative work in sociology, economics, and political science as well as applied disciplines such as marketing, nursing, education, and social work. It is no exaggeration to say that virtually all quantitative research in the social sciences is done with correlation and regression analysis and their siblings and offspring. The subject is clearly of great substantive importance.

    As has been repeatedly demonstrated, virtually all analytic methods are at base correlational.1 All are based on variances and covariances, and most multivariate techniques are erected upon analysis of correlation and/or covariance matrices. These statistics are all versions of the same basic algorithm, the same general linear model. While many choices must ...

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