Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

CiteULike is a free service for managing and discovering scholarly references - click here to get started.

Sign In to gain access to subscriptions and/or personal tools.
Sociological Methods & Research
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Levine, J. H.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Extended Correlation

Not Necessarily Quadratic or Quantitative

Joel H. Levine

Dartmouth College

What is the correlation between two variables? Traditional answers offer summary assessments such as Pearson’s r and regression coefficients. But new computing techniques make it possible to construct conceptually simple hypotheses that describe the full joint distribution of two variables, making it possible to "mine" the correlation for information that was previously unused. This article begins with evidence of systematic anomalies in the empirical joint distribution of height-weight data and follows with a hypothesis that explains these anomalies in terms of a theoretical joint distribution relative to a linear equation. The hypothesis has serious consequences because even in traditional examples, while it offers an improved fit to the data, its estimates of the linear center do not correspond to traditional least squares estimates of the linear relation for the same two variables.

Key Words: correlation • association • regression • least squares • categorical data

Sociological Methods & Research, Vol. 34, No. 1, 31-75 (2005)
DOI: 10.1177/0049124104267344


Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?