The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…

Relative to given scale properties of each of two paired variables, a program for identification and computation of the following indices of relationship is provided: phi, Spearman rank order, Kendall's Tau, Pearson's product moment, biserial, and point biserial. (Author/RC)

Formulas and a FORTRAN program for computing Kendall's Tau as well as a generalized Spearman rho coefficient from ordered contingency tables are described. (Author)

This paper presents an empirical analysis of similarities and differences between two statistics, G and Phi, which treat genuinely dichotomous data. These results can aid researchers in selecting between these two statistics and in evaluating results from the use of one v the other. (Author)

Copies of this paper and a source listing which includes input and output data for sample problems can be obtained from the author at Temple University, Philadelphia, Penna. (Author/MB)

The PM statistical index, which indicates the probability that a person will belong to a particular clinical class, is described. The coefficient is similar to the G index but is easier to compute. An empirical example is presented. (JKS)

Examines the similarity between the coordinates which resulted when correlations were used as similarity measures and the factor loadings obtained by factor analyzing the same correlation matrix. Real data, a set of error free data, and some computer generated data containing deliberately introduced sampling error are analyzed. (RC)

This paper explains how to combine probabilities from discrete distributions, such as probability distributions for nonparametric tests. (Author/BW)