A widely believed result about Q-sorts is shown by simulation; namely, that the hard-to-compute but statistically correct Spearman's ranked correlation, r s , may be substituted by the more common Pearson's r correlation. Recent versions of Microsoft Excel are excellent for reasonably sized simulations, especially those used in classroom settings.S imulation is commonly used in engineering and other situations as a manner of finding quick answers to mathematically challenging (and perhaps unsolvable) problems. For instance, the arrival and service of a complicated queuing network, such as a hospital emergency room, is easily described through simulation but very difficult to describe using direct probabilistic queuing theory, except under trivial assumptions (Kelton, Sadowski, & Sadowski, 1998). The use of simulation is less frequent in the social sciences but can still be useful (Garson, 1994).Q-methodology is an area of social research where we propose that simulation could be quite useful. In short, Q-methodology treats data from an individual as an entire vector, rather than doing an analysis of each item.A Q-sort is a common tool in Q-methodology and can also be used outside of the methodology. It is a questionnaire variant in which participants are asked to complete a pseudo ranking of the items. To compare to the traditional questionnaire, consider the instrument modeled here. Sixty-four items are rated on a 7-point scale. However, instead of having a free distribution, exactly five items must be placed in each extreme category, eight in the next, and so forth, creating a 5-8-12-14-12-8-5 fixed distribution when completed. Brown (1980) and Block (1979) give more detailed information on the technical aspects of Q-sorts.Two common ways of doing the vector analysis in Q-sorts are factor analysis and correlation. Correlation among subjects is the more simple and intuitive method of analysis. Pearson's r is the correlation taught in most introductory statistics courses, so most students are already aware of it; however, it is only appropriate for normal, continuous data. Spearman's rank correlation, r s , which performs a standard Pearson correlation on ranks of data, is recommended for ordinal data such as that found in Q-sorts (Gibbons, 1985). Pearson's r uses about one third the resources and is less complex than Spearman's r s , mak-