Analyzing multiple-item measures of crime and deviance II: Tobit regression analysis of transformed scores

D. Wayne Osgood, Laura L. Finken, Barbara J. McMorris

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147 Scopus citations


The purpose of this article is to inform criminological researchers about tobit regression, an alternative regression model that deserves more attention in this field. Tobit regression is intended for continuous data that are censored, or bounded at a limiting value. The tobit model may be a particularly good match to measures of self-reported offending, provided they have been transformed to reduce skewness. We present empirical analyses that evaluate the match of self-report measures to the assumptions of ordinary least square (OLS) and tobit regression models and that assess the consequences of any violations of assumptions. The analyses use a fourteen-item, self-report measure of delinquency from the Monitoring the Future study, a national survey of high school seniors. These analyses provide clear evidence that (1) transformations to reduce skewness improve the match of OLS to the data but still leave considerable discrepancies, and (2) the tobit model is well suited to the transformed measure. We conclude by assessing the purposes for which tobit offers greater and smaller advantages over OLS regression.

Original languageEnglish (US)
Pages (from-to)319-347
Number of pages29
JournalJournal of Quantitative Criminology
Issue number4
StatePublished - 2002

Bibliographical note

Funding Information:
This research was conducted with the support of a Gallup Research Professorship for the first author, through the Gallup Survey Research Center of the University of Nebraska—Lincoln.


  • Limited dependent variables
  • Regression analysis
  • Self-report measures
  • Tobit regression


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