A procedure for analyzing unbalanced datasets

This paper describes a procedure for analyzing unbalanced datasets that include many nominal-and ordinal-scale factors. Such datasets are often found in company datasets used for benchmarking and productivity assessment. The two major problems caused by lack of balance are that the impact of factors can be concealed and that spurious impacts can be observed. These effects are examined with the help of two small artificial datasets. The paper proposes a method of forward pass residual analysis to analyze such datasets. The analysis procedure is demonstrated on the artificial datasets and then applied to the COCOMO dataset. The paper ends with a discussion of the advantages and limitations of the analysis procedure.