Risk behavior data analysis: ordinal or dichotomous the choice is yours.
OBJECTIVE
To demonstrate the differences of 2 approaches to data analysis.


METHODS
Using the South Carolina YRBS data, study focused on contingency tables and ANOVA. Additive chi squares are utilized to illustrate information loss when collapsing a contingency table. Odds ratios are derived from contingency tables or logistic regression. Means are utilized in ANOVA. Five measures of life satisfaction were summed to create a pseudo-continuous response variable that was subsequently trichotomized. All predictors are dichotomized risk variables.


RESULTS
Chi squares from subtables added exactly to that of the original table measuring lost information. ANOVA conveyed the same clinical message.


CONCLUSIONS
Clinically relevant conclusions might be the same even when drawn from any of several different analyses of the same risk-behavior data.
Drane
J Wanzer
JW
Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia 29208-0001, USA. WDrane@gwm.sc.edu
Valois
Robert F
RF
D000293:Adolescent; D000294:Adolescent Behavior / Q000523:psychology*; D000704:Analysis of Variance; D016009:Chi-Square Distribution; D016000:Cluster Analysis; D006801:Humans; D016017:Odds Ratio; D010549:Personal Satisfaction; D011788:Quality of Life; D012309:Risk-Taking; D013022:South Carolina; D013223:Statistics as Topic / Q000379:methods*