A response to comments on internal correlation for statistical analysis, as proposed by the present authors (1989), is provided. Focus is on issues raised by W. W. Rozeboom (1989). Comments by J. H. Schuenemeyer (1989) and R. Bargmann (1989) are briefly considered. (TJH)

Use of linear combinations of Fisher's "z" transformations as a combined test for the common correlation parameter based on "k" independent sample correlations has been previously studied. This article considers additional "z" additive properties and methods of combining independent studies when planning the number of…

The internal correlation--a measure of dependency in a set of variables--is discussed and generalized. Applications of the internal correlation coefficient and its generalizations are given for several data-analytic situations. The internal correlation is illustrated and the concept is expanded to a series of additional indices. (TJH)

Use of internal correlation for statistical analysis--proposed by G. W. Joe and J. L. Mendoza (1989)--is discussed. Focus is on the "content" question (what this application can do with the information that statistics contain) and the "eloquence" question (the advantages of this means of encoding information over other means). (TJH)

Combining information to estimate standardized partial regression coefficients in a linear model is discussed. A combined estimate obtained from the pooled correlation matrix is proposed, and its large sample distribution is obtained. The method is generalized to handle a random effects model in which correlation parameters vary across studies.…

A method for the estimation of covariance structure models under polynomial constraints (such as quadratic constraints) is presented. Estimation is on maximum likelihood principles, and the test statistics, parameter estimates, and standard errors are based on a statistical theory which takes the constraints into account. (Author/JKS)

Barcikowski has provided tables for use in situations where means are to be used as the unit of analysis. This article argues that the conditions specified for use of these tables are not practical. It explicates a methodology for carrying out analyses based on group means. (Author/JAZ)

Multidimensional item response data were created. The strength of a general factor, the number of common factors, the distribution of items loadingon common factors, and the number of items in simulated tests were manipulated. LOGIST effectively recovered both item and trait parameters in nearly all of the experimental conditions. (Author/JAZ)