A formula for the ratio of the variance of the pooled transformed data under departure from the binomial assumption to the variance of the pooled transformed data when the binomial assumption holds is given. (Authors)

It is argued that ordinal statistical methods are often more appropriate than their more common counterparts because conclusions will be unaffected by monotonic transformation of the variables; they are more statistically robust when used appropriately; and they often correspond more closely to the researcher's goals. (SLD)

In this article, an attempt is made to find all linear transformations that map a standard Pythagorean triple (a Pythagorean triple [x y z][superscript T] with y being even) into a standard Pythagorean triple, which have [3 4 5][superscript T] as their fixed point. All such transformations form a monoid S* under matrix product. It is found that S*…

A study is conducted regarding the change in teaching process in the geometry of transformations, teaching by small steps rather than by a complicated method. In the process of making mathematics teaching better, many challenges are faced, and several risks are taken to solve the questions asked by the students.

This study focused on unprompted changes in children's analogical reasoning on geometric tasks and the additional effect of a short training procedure. Participants were 36 grade 1 level children (M = 6;8 years) divided over a not-trained and a trained condition. The study was a 5-sessions migrogenetic procedure, with a follow-up test session…

The main purpose of this paper consists in deriving means for making statistical inferences about the distribution of u under the conditions of sigma' is equal to sigma" and sigma' is not equal to sigma". An application of the results to coefficient alpha is appended as an illustration. (CK)

A class of monotonic transformations which generalize the power transformation is fit to the independent and dependent variables in multiple regression so that the resulting additive relationship is optimized. Examples of analysis of real and artificial data are presented. (Author/JKS)

A rational approach to standard transformed scores which always results in the numerical to letter grade correspondence for any chosen hypothetical grade distribution model is developed. (Author/LMO)

Four types of metric scales are distinguished: absolute, ratio, difference, and interval. A general coefficient of association for two variables of the same scale type is developed which reduces to specific coefficients of association for each scale type. (NSF)

The goal of this Digest is to explore some of the issues involved in data transformation, with particular focus in the use of data transformation for the normalization of variables. The Digest is intended to serve as an aid to researchers who do not have extensive mathematical backgrounds or who had not had extensive exposure to this issue. It…

The assumption that is most important to the hypothesis testing procedure of multiple linear regression is the assumption that the residuals are normally distributed, but this assumption is not always tenable given the realities of some data sets. When normal distribution of the residuals is not met, an alternative method can be initiated. As an…

The problem of correlating variables from different scale types is discussed. A general correlation coefficient, based on symmetrization theory, is derived. The coefficient is invariant over permitted transformations of the variables for their respective (possibly nonequivalent) scale types. (Author/JKS)

The purpose of this paper is to outline the role of orthogonal rotation in canonical analysis, including the evaluative measures that need be reported and scrutinized upon application. (Author)

A Monte Carlo simulation was used to investigate the effect of data transformation on average correlations between Job Descriptive Index scores. In general, results indicate that transformation of scores did not result in marked changes in the correlations and that such transformation may not be worthwhile. (SLD)