Faculty Bibliography

A small Monte Carlo study examined the performance of a form of taxometric analysis (the MAXCOV procedure) with fuzzy data sets. These combine taxonic (categorical) and nontaxonic (continuous) features, containing a subset of casts with intermediate degrees of category membership. Fuzzy data sets tended to yield taxonic findings on plot inspection and two popular consistency tests, even when the degree of fuzziness, i.e., the proportion of intermediate cases, was large. These results suggest that fuzzy categories represent a source of pseudotaxonic inferences, if on is understood in the usual binary, "either-or" fashion. This in turn implies that dichotomous causes cannot be confidently inferred when taxometric analyses yield apparently taxonic findings.

This study presents a review and meta-analyses of research on the recidivism-reducing impact of correctionally based treatment programs in Germany. The data are part of the Correctional Drug Abuse Treatment Effectiveness (CDATE) project meta-analytic database (covering 1968-1996) of evaluation research studies of correctional interventions. Overall, the five studies of educational programs show no practical impact of these programs in reducing recidivism. Four studies of programs to counsel driving-under-the-influence (DUI) offenders fall in an intermediate area (not statistically significant, but promising enough to warrant further research). The eight studies of Social Therapy programs did show, on the average, a statistically significant practical impact in reducing recidivism.

A Monte Carlo evaluation of four procedures for detecting taxonicity was conducted using artificial data sets that were either taxonic or nontaxonic. The data sets were analyzed using two of Meehl's taxometric procedures, MAXCOV and MAMBAC, Ward's method for cluster analysis in concert with the cubic clustering criterion and a latent variable mixture modeling technique. Performance of the taxometric procedures and latent variable mixture modeling were clearly superior to that of cluster analysis in detecting taxonicity. Applied researchers are urged to select from the better procedures and to perform consistency tests.