Large Number Of Explanatory Variables Research Articles

The Differential Impact of CETA Training

This study analyzes the effectiveness of CETA skill training programs in Boston, using three techniques not often employed in such evaluation studies—a comparison group, multiple regressions incorporating an unusually large number of explanatory variables, and an examination of the effects of differences in the type of skill-training provided. The authors find that these programs provided participants with significant wage and employment benefits, particularly when participants obtained jobs on which they could use the skills they learned. The results also showed, however, that the stability of postprogram employment is sensitive to the type of training received and postprogram wages are even more so.

A comparison of the predictive ability of mode choice models with various levels of complexity

Models of mode choice have recently been developed which include a large number of explanatory variables. The inclusion of some of these variables is obviously the result of trial-and-error analysis of various model specifications: the researcher tries various specifications until he obtains a specification which is consistent with a priori beliefs and fits the data fairly well. This method of model specification allows one to “learn” from the data, but is also open to the critism that the resultant model simply reflects relations which happen to exist in the sample, rather than true, behavioral relations. This paper examines this question. A complex model is presented which was developed after attempting a wide variety of specifications. The predictive ability of this model is compared with that of models with fewer variables, each of which could be included on the basis of a priori ideas. It is found that the complex model predicts best, indicating that the behavioral content of the model which was developed through “learning” from the data is greater than that of models which were specified on a priori beliefs.