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Table 1 Sorting Schemes Used by Three Hypothetical Subjects (A, B, and C) the items in each category used. by each subject may be requested. The contingency table frequencies can also be printed. The main computations in the program result in the printing of the observed number of agreements between the subjects (i.e., the number of times the subjects agree in sorting a pair of items into the same or different categories), the number of agreements that would be expected on the basis of chance, the variance of the agreement statistic, and the value of Z for testing the null hypothesis. Language and Computer. The 175-line program is written in BASIC for a VAX 11-780 computer. Current dimensions allow up to 20 categories per subject and up to 25 objects per category. A reduction in these dimensions would be necessary to implement the program on a system with less than 48K of memory. The version of BASIC used on the VAX 11-780 has certain features that may not be common to other versions. For example, variable names of up to six characters have been used and may need to be shortened on systems in which this is not permitted. Another feature that may not be available on some systems is the FORUNTIL statement. These statements have been used in AGREE primarily to facilitate data entry. They can, however, easily be replaced by additional input statements in which the user specifies the number and size of each subject's categories in advance. Other nonstandard features used in AGREE, such as lines with multiple statements separated by back slashes, are relatively minor and in most cases can be dealt with by routine editing. Example. In the hypothetical example that follows, three subjects, A, B, and C, have been asked to sort 10 countries into meaningful categories. The results are shown in Table 1. A uses four categories, B uses three categories, and C uses six categories. Successive pairwise comparisons of these sorting schemes using AGREE produce the statistics shown in Table 2. A and C show

AGREE: A BASIC program for assessing similarity in categorization judgments