RT Article
T1 Analyzing Multiple-Item Measures of Crime and Deviance II: Tobit Regression Analysis of Transformed Scores
JF Journal of quantitative criminology
VO 18
IS 4
SP 319
OP 347
A1 Osgood, D. Wayne
A2 Finken, Laura Lei
A2 McMorris, Barbara J.
LA English
YR 2002
UL https://krimdok.uni-tuebingen.de/Record/1764278585
AB 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.
K1 limited dependent variables
K1 Regression Analysis
K1 Self-report measures
K1 tobit regression
DO 10.1023/A:1021198509929