# Agreement Matrices

The results of the analyses of the sociometric matrices among the members of the Brotherhood are presented in Table 6. Although all significance tests associated with the mantelapprox.m and triadapprox.m programs are significant at the level α = .0005, the size of the index values varies considerably. The concordance between the sociometric matrices measured during the 10th and last week is high, with gradient indices of (11 (A 2, A 3) = .8481) and (12 (A 2, A 3) = .7010). However, the concordance between other pairs of matrices is significantly lower. For example, the concordance between the matrices measured during the first and tenth weeks is measured with (1 (A 1, A 2) = .3375) and (12 (A 1, A 2) = .2428) for the mantle or interior gradient index. The concordance indices between the measures of the first and the last week are even lower. The reason for these results is that dramatic rankings occur at the beginning of the semester, but during the 10th week, the members of the Brotherhood are firmer in their rankings, which remain consistent during the rest of the semester. The problem of comparing the concordance of matrices has a long history in the behavioral sciences. Applications include detection/confusion data analysis, similarity judgments, and social networking relationships. The mantle index is the best-known match index because of its relationship to correlation. However, as Hubert (1978, 1987) and Brusco (2004) noted, the mantle index can only be strongly influenced by a small number of elements in the matrices. Other compliance measures based on sampling elements within rows or columns can also be used to study matrix highlighting. If you create a matrix with the Priority and Configuration Item Type dimensions, it means that each call associated with an agreement using that matrix has its escalation and injury times based on the values entered in the priority matrix and the call`s configuration item.

The options for dimension X take precedence and are not specified. Typically, you organize matrices by priority. However, if you want the matrix to be based only on a dimensional criterion Y, select Not specified. Our first example uses Q = 4 mutilations of acoustic confusion based on data initially collected by Morgan, Chambers and Morton (1973) and then analyzed by Hubert and Golledge (1977) and Brusco (2002). The first two confusion matrices in S concern a recognition task for n = 9 digits (1, 2,…,9): matrix A 1 corresponds to the recognition of a male spokesperson, while A 2 represents a female spokesperson. The last two matrices in S are linked to memory tasks for the same n = 9 digits: matrices A 3 and A 4 are linked to two different female spokespeople. The four matrices of S were arranged in such a way that the rows corresponded to the attraction represented and the columns of the answer. In addition, according to the procedure used by Brusco (2004) in his concordance analysis between 19 matrices of visual and tactical confusion, the four S matrices were normalized on the basis of the line amounts (stimulation sum). These standardized matrices are shown in Table 1. For each matrix A q in S, an ijq is the percentage of the responses of the number j if the number i was the stimulus presented. Before presenting the specific indices of the measurement agreement, we provide some preliminary definitions of the data.

We begin with the definition of S = {A 1, A 2, …, A Q } as a quantity of n × n approximation. Approximation points A q = [a ijq] can take many different forms, for example: (i) points of confusion for the same set of stimuli obtained by different authors, (ii) similarity judgments for the same group of marks measured at different times; (iii) social network links between the same group of actors for different relationships (friendship, seeking advice, cooperation, etc.) and (iv) the same group of actors for the same relationship, but at different times….