A systematic research approach is necessary to investigate complex systems. This approach should provide a tool for examining multifactors in an efficient manner since a large number of factors is usually involved in the design and evaluation of complex systems. This study is used to develop empirical models which describe the functional relationships of many independent variables in the design of a telephone information system. Such a development is based on integrating several data sets using sequential experimentation. Reanalyses of previous experiments were conducted to examine necessary and sufficient conditions for integrating data sets resulting from previous studies. As a result. an experiment was conducted to investigate the effects of independent variables which were not manipulated in the previous experiments. An additional experiment was conducted to provide a bridge among several data sets. The integrated data set was then used to build second-order empirical models using polynomial regression. Determinant values of X'X matrices served as a statistical criterion for achieving minimum variances of coefficients and prediction variances of the models. Based upon the empirical models developed, optimum configurations of the telephone information system were obtained using a nonlinear programming technique. A separate optimization method was used since the empirical models included both continuous variables and discrete variables. Specific procedures and guidelines are suggested in planning and conducting sequential research which deals with a large number of independent variables in an efficient and systematic manner. The procedures and guidelines are summarized based upon the lessons learned from the dissertation research. These include administrative requirements, alternative experimental designs, methodological considerations on conducting sequential experiments, and other necessary rules and decision criteria for bridging data sets and optimizing empirical models. This approach is expected to provide a tool for obtaining generalizable results in human factors research. / Ph. D.