A Monte Carlo Study of Recovery of Weak Factor Loadings in Confirmatory Factor Analysis

By C. Ximénez. 2006

50 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ce23baa23b8482edf568d3aaf099ff9272833405

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. C. Zygmont
2. Megha Budruk
3. David Gallardo-Pujol
4. Albert Maydeu-Olivares
5. J. D. de Winter
6. C. G. Forero
7. Mario R. Smith
8. Peter A. Wieringa

In Management Science and Operations Research

1. Albert Maydeu-Olivares
2. David Gallardo-Pujol
3. C. G. Forero
4. J. D. de Winter
5. Dimitra Dodou
6. C. Zygmont
7. Megha Budruk
8. Mario R. Smith

In Statistics and Probability

1. David Gallardo-Pujol
2. C. G. Forero
3. C. Zygmont
4. Megha Budruk
5. Albert Maydeu-Olivares
6. J. D. de Winter
7. Mario R. Smith
8. Dimitra Dodou

Citations in the corpus

• Exploratory Factor Analysis With Small Sample Sizes (2009, background): “Ximénez (2006) investigated the recovery of weak factors using CFA and found that the recovery of weak factors may be troublesome if their loadings are small and the factors orthogonal.”
• Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions (2014, background): “Reviews of articles in prominent psychology journals (Fabrigar, Wegener, MacCallum & Strahan, Generally methodologists prioritize a large sample when designing a factor analytic study, especially for recovery of weak factor loadings (Ximénez, 2006).”
• Place attachment and recreation experience preference: A further exploration of the relationship (2013, background): “25 may be considered weak but interpretable (Ximenez, 2006) and were therefore retained.”
• Factor Analysis with Ordinal Indicators: A Monte Carlo Study Comparing DWLS and ULS Estimation (2009, methodology): “Finally, we include items with low (.4) to high (.8) factor loadings in typical applications (Briggs & MacCallum, 2003; Ximénez, 2006).”