Many large-scale assessment programs in education utilize “conditioning models” that incorporate both cognitive item responses and additional respondent background variables relevant for the population of interest. The set of respondent background variables serves as a predictor for the latent traits (proficiencies/abilities) and is used to obtain a conditional prior distribution for these trai...

Normality testing remains an important issue for researchers, despite many solutions that have been published and in use a long time. There is need normality areas of research application, among them Quality control, or more precisely, the investigation Shewhart-type control charts. We modified some our previous results concerning charts by using empirical distribution function, proper choice q...

Networks appear in many fields, from finance to medicine, engineering, biology and social science. They often comprise of a very large number of entities, the nodes, and the interest lies in inferring the interactions between these entities, the edges, from relatively limited data. If the underlying network of interactions is sparse, two main statistical approaches are used to retrieve such a s...

In this article, we propose a parametric bootstrap (PB) test for testing main, simple and interaction effects in heteroscedastic two-way MANOVA models under multivariate normality. The PB test is shown to be invariant under permutation-transformations, and affinetransformations, respectively.Moreover, the PB test is independent of the choice ofweights used to define the parameters uniquely. The...

Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for normality in statistical analysis using SPSS.

Reaction time (RT) is one of the most common types of measure used in experimental psychology. Its distribution is not normal (Gaussian) but resembles a convolution of normal and exponential distributions (Ex-Gaussian). One of the major assumptions in parametric tests (such as ANOVAs) is that variables are normally distributed. Hence, it is acknowledged by many that the normality assumption is ...

Conventional statistical tests are usually called parametric tests. Parametric tests are used more frequently than nonparametric tests in many medical articles, because most of the medical researchers are familiar with and the statistical software packages strongly support parametric tests. Parametric tests require important assumption; assumption of normality which means that distribution of s...