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19 - MULTIVARIATE ANALYSIS AND STATISTICAL SIGNIFICANCE

Published online by Cambridge University Press:  05 June 2012

Paul S. Gray
Affiliation:
Boston College, Massachusetts
John B. Williamson
Affiliation:
Boston College, Massachusetts
David A. Karp
Affiliation:
Boston College, Massachusetts
John R. Dalphin
Affiliation:
Merrimack College, Massachusetts
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Summary

INTRODUCTION

The first part of this chapter introduces regression analysis, one of the most widely used multivariate statistical techniques. Although a comprehensive treatment of this topic is beyond the scope of a first course in research methods, a brief introduction to regression analysis is essential because it appears so frequently in quantitative social research literature. The aim of the present discussion is to give the reader a basic overview and some suggestions for further reading. One reason that regression analysis is so widely used is that it lends itself to causal modeling. Although there are many types of causal modeling, we discuss path analysis here because it is one of the most commonly used types.

The second section of this chapter covers statistical inference. Social researchers use tests of significance to make inferences about a population (or universe) based on the evidence obtained from a sample selected from that population. Many social researchers put a great deal of confidence in these tests of significance, and it is important that both researchers and consumers of social research understand their uses and misuses. Although they can help researchers to answer legitimately a wide range of questions, tests of significance simply cannot answer some questions.

MULTIVARIATE ANALYSIS

Regression analysis

linear regression is a statistical procedure used to estimate the amount of change in a dependent variable that can be expected for a given change in an independent variable.

Type
Chapter
Information
The Research Imagination
An Introduction to Qualitative and Quantitative Methods
, pp. 421 - 432
Publisher: Cambridge University Press
Print publication year: 2007

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References

Kuh, George D. 2006. The National Survey of Student Engagement: Conceptual Framework and Overview of Psychometric Properties. Indiana University.Google Scholar
Levin, Jack, and Fox, James Alan. 2006. Elementary Statistics in Social Research. Boston: Allyn & Bacon.Google Scholar
Mertler, Craig A., and Rachel, A. Vannatta. 2001. Advanced and Multivariate Statistical Methods: Practical Application and Interpretation. Los Angeles: Pryrczak.Google Scholar
Ritchey, Ferris. 2000. The Statistical Imagination: Elementary Statistics for the Social Sciences. Boston: McGraw-Hill.Google Scholar
Spicer, John. 2004. Making Sense of Multivariate Data Analysis. Thousand Oaks, CA: Sage.Google Scholar
Tabachnick, Barbara G., and Linda, S. Fidell. 2001. Using Multivariate Statistics. 4th ed.Boston: Allyn & Bacon.Google Scholar
Mertler, Craig A., and Rachel, A. Vannatta. 2001. Advanced and Multivariate Statistical Methods: Practical Application and Interpretation. Los Angeles: Pryrczak.Google Scholar
Ritchey, Ferris. 2000. The Statistical Imagination: Elementary Statistics for the Social Sciences. Boston: McGraw-Hill.Google Scholar
Ullman, Jodie B. 2001. “Structural Equation Modeling.” In Using Multivariate Statistics, 653–771. 4th ed. Barbara, G. Tabachnick and Linda, S. Fidell, eds. Boston: Allyn & Bacon.Google Scholar

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