The Future of Statistical Software Proceedings of a Forum (1991) / Chapter Skim
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B. Position Statements: Additional Material Submitted by Symposium Participants
B. Position Statements: Additional Material Submitted by Symposium Participants
Pages 77-84
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Appendix B Position Statements: Additional Material Submitted by Symposium Participants 77
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The supporting statistical software has been of limited utility in dealing with these functional relationships. The picture becomes more complex when one introduces stochastic components to the models and complex sample designs for collecting data.
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Furthermore, the diagnostics for dealing with large data sets need to be very different from those envisioned for smaller data sets for which we can easily scan residuals, plots and other simple displays, or tables for all of our data. For example, techniques for focusing on a few outlier observations need to be generalized to focus on outlier sets.
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At another agency, SAS is the principal mainframe package available. While this is a powerful package suitable for many important statistical activities, other packages provide strengths not found in SAS.
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Even procedures that allow weights often treat them as a way of indicating multiple occurrences of the same observed value.
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I feel that such software, by ignoring type II error analyses for simple alternatives, has helped make hypothesis testing disreputable, when it should be one of our viable tools. I hope that future generations of statistical software will take into account the need for type II error analysis.
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In particular, ~ believe that the interests of survey researchers from the social sciences who analyze primarily categorical data are being largely neglected by the panel. Most of the presentations point out the importance of EDA methods in appropriately dealing with violations of the assumptions of traditional techniques for Quantitative analysis.
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