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Albert L. Lederer, Jayesh Prasad, "A Causal Model for Software Cost Estimating Error," IEEE Transactions on Software Engineering, vol. 24, no. 2, pp. 137148, February, 1998.  
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@article{ 10.1109/32.666827, author = {Albert L. Lederer and Jayesh Prasad}, title = {A Causal Model for Software Cost Estimating Error}, journal ={IEEE Transactions on Software Engineering}, volume = {24}, number = {2}, issn = {00985589}, year = {1998}, pages = {137148}, doi = {http://doi.ieeecomputersociety.org/10.1109/32.666827}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Software Engineering TI  A Causal Model for Software Cost Estimating Error IS  2 SN  00985589 SP137 EP148 EPD  137148 A1  Albert L. Lederer, A1  Jayesh Prasad, PY  1998 KW  Cost estimating KW  software development. VL  24 JA  IEEE Transactions on Software Engineering ER   
Abstract—Software cost estimating is an important concern for software managers and other software professionals. The hypothesized model in this research suggests that an organization's use of an estimate influences its estimating practices which influence both the basis of the estimating process and the accuracy of the estimate. The model also suggests that the estimating basis directly influences the accuracy of the estimate. A study of business information systems managers and professionals at 112 different organizations using causal analysis with Equations Modeling System (EQS) refined the model. The refined model shows that no managerial practice in this study discourages the use of intuition, guessing, and personal memory in cost estimating. Although user commitment and accountability appear to foster algorithmbased estimating, such an algorithmic basis does not portend greater accuracy. Only one managerial practice—the use of the estimate in performance evaluations of software managers and professionals—presages greater accuracy. By implication, the research suggests somewhat ironically that the most effective approach to improve estimating accuracy may be to make estimators, developers, and managers more accountable for the estimate even though it may be impossible to direct them explicitly on how to produce a more accurate one.
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