The model-selection is an essential problem in the loglinear model approach to the analysis of multidimensional contingency tables. Although some stepwise model-selection procedures have been proposed, these procedures are not very efficient because all the internal goodness-of-fit information is ignored. The primary objective of this proposed research is to develop some new stepwise model-selection procedures in which the internal goodness-of-fit information (especially the estimated interaction parameters in loglinear models) is utilized. Throughout the development of the new procedures both the efficiency in computation and the ability in reaching to the "best" model will be emphasized. Some Monte Carlo methods will be used to assess the computational efficiency and the ability in reaching to the "best" model.