This paper serves to remind the reader, that factor analysis in the case of dichotomous variables will often lead to artifi cial factors. In other words, the factors correspond pri-marily to certain levels of item diffi culty. A numerical example will be given in order to illustrate this. It is argued that, for instance, factoring tetrachoric correlations instead of conventionally used Pearson correlations would lead to more content valid results
This paper presents heuristic explanations of factor scores, structure coefficients, and communality...
<p>Percentage of over-dimensionalised solutions from exploratory factor analysis applying criteria l...
2 This article compares two methods, Principle Component Analysis (PCA) and Generalized Least Square...
ABSTRACT. Despite known shortcomings of the procedure, exploratory factor analysis of dichotomous te...
Three methods of factor analyzing dichotomously scored item performance data ';ere compared usi...
While factor analysis is one of the most often used techniques in psychometrics, comparing or combin...
We provide a basic review of the data screening and assumption testing issues relevant to explorator...
Factor analysis is an analysis of influence of separate factors (reasons) on the resulting indicator...
Construct validity, Polychoric correlations, Pearson correlation, Factor analysis,
Many factor analysis and multidimensional item response models for dichotomous variables have been p...
Factor analysis is a statistical technique, the aim of which is to simplify a complex data set by re...
A statistical simulation was performed to compare four least-squares methods of factor analysis on...
Introduction: Factor analysis is frequently carried out with categorical data in the field of educat...
The factor analysis of items often produces spurious results in the sense that unidimensional scales...
A classical advantage of factor analysis is its provision for possible generalizability of the &...
This paper presents heuristic explanations of factor scores, structure coefficients, and communality...
<p>Percentage of over-dimensionalised solutions from exploratory factor analysis applying criteria l...
2 This article compares two methods, Principle Component Analysis (PCA) and Generalized Least Square...
ABSTRACT. Despite known shortcomings of the procedure, exploratory factor analysis of dichotomous te...
Three methods of factor analyzing dichotomously scored item performance data ';ere compared usi...
While factor analysis is one of the most often used techniques in psychometrics, comparing or combin...
We provide a basic review of the data screening and assumption testing issues relevant to explorator...
Factor analysis is an analysis of influence of separate factors (reasons) on the resulting indicator...
Construct validity, Polychoric correlations, Pearson correlation, Factor analysis,
Many factor analysis and multidimensional item response models for dichotomous variables have been p...
Factor analysis is a statistical technique, the aim of which is to simplify a complex data set by re...
A statistical simulation was performed to compare four least-squares methods of factor analysis on...
Introduction: Factor analysis is frequently carried out with categorical data in the field of educat...
The factor analysis of items often produces spurious results in the sense that unidimensional scales...
A classical advantage of factor analysis is its provision for possible generalizability of the &...
This paper presents heuristic explanations of factor scores, structure coefficients, and communality...
<p>Percentage of over-dimensionalised solutions from exploratory factor analysis applying criteria l...
2 This article compares two methods, Principle Component Analysis (PCA) and Generalized Least Square...