EINLADUNG zum Betriebswirtschaftlichen Forschungsseminar

des Instituts fuer Betriebswirtschaftslehre der Universitaet Wien Bruenner Strasse 72, 1210 Wien

Freitag, 14.06.1996; 15.30 Uhr; HS 8 des BWZ DR. HERIBERT REISINGER (Universitaet Wien)

"DER EINFLUSS DES FORSCHUNGSDESIGNS AUF DIE HOEHE VON BESTIMMTHEITSMASSEN IN LINEAREN REGRESSIONSMODELLEN"

Abstract:

The classical linear regression model is the standard procedure for analyzing dependencies between variables that are measured on a metric scale. In the course of model estimation it is common practice to assess the appropriateness of a single descriptive model for the problem under study with the help of coefficients of determination (R^2 and ADJ. R^2 ). When considering the advantages of calculating these measures in empirical studies the question arises whether it makes sense to evaluate a model by means of a single descriptive measure at all. For example, from a statistical point of view the analyzed data set is irrelevant when deciding on the appropriateness of the model under consideration. However, a market researcher clearly distinguishes whether he studies time series or cross sectional data. A well known fact says that on the average one may expect larger coefficients of determination for time series data than for cross sectional data. Starting from this known phenomenon it is tried to identify various impacts on R^2 and ADJ. R^2 that originate in the research designs of empirical studies rather than in the research subjects within the framework of a meta-analysis. One important result claims a strong negative correlation between the sample size and the values of R^2 and ADJ. R^2 . =========================================================================