Confirmatory analysis of exploratively obtained factor structures

Factor structures obtained by exploratory factor analysis (EFA) often turn out to fit poorly in confirmative follow-up studies. In the present study, the authors assessed the extent to which results obtained in EFA studies can be replicated by confirmatory factor analysis (CFA) in the same sample. More specifically, the authors used CFA to test three different factor models on several correlation matrices of exploratively obtained factor structures that were reported in the literature. The factor models varied with respect to the role of the smaller factor pattern coefficients. Results showed that confirmatory factor models in which all low EFA pattern coefficients were fixed to zero fitted especially poorly. The authors conclude that it may be justified to use a less constrained model when testing a factor model by allowing some correlation among the factors and some of the lower factor pattern coefficients to differ from zero

Pijnmeting met de MPQ-DLV. Een kijkje in de data

Multivariate analysis of psychological data - ou

External analysis for three-mode principal component models

Datatheorie voor analyse van individuele verschille

Implicit theories of personality: Further evidence of extreme response style

Datatheorie voor analyse van individuele verschille

Solving non-uniqueness in agglomerative hierarchical clustering using multidendrograms

In agglomerative hierarchical clustering, pair-group methods suffer from a problem of non-uniqueness when two or more distances between different clusters coincide during the amalgamation process. The traditional approach for solving this drawback has been to take any arbitrary criterion in order to break ties between distances, which results in different hierarchical classifications depending on the criterion followed. In this article we propose a variable-group algorithm that consists in grouping more than two clusters at the same time when ties occur. We give a tree representation for the results of the algorithm, which we call a multidendrogram, as well as a generalization of the Lance and Williams' formula which enables the implementation of the algorithm in a recursive way.Comment: Free Software for Agglomerative Hierarchical Clustering using Multidendrograms available at http://deim.urv.cat/~sgomez/multidendrograms.ph

Predictors of mood response to acute tryptophan depletion: a reanalysis

Multivariate analysis of psychological data - ou

Meerdimensionale schaaltechnieken voor gelijkenis- en keuzedata

Multivariate analysis of psychological data - ou

Review of: S.S. Schiffman, M.L. Reynolds, & F.W. Young, "Introduction to multidimensional scaling. Theory, methods, and applications"

Datatheorie voor analyse van individuele verschille