Exploratory Factor Analysis With Small Sample Sizes

By J. D. de Winter, Dimitra Dodou, Peter A. Wieringa. Multivariate Behavioral Research, 2009

1,132 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Experimental and Cognitive Psychology, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/90cccaf437a22e8026a620d8288ac8a573921772

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. Alexandre J. S. Morin
2. Marylène Gagné
3. David Litalien
4. Herbert W. Marsh
5. J. D. de Winter
6. Richard M. Ryan
7. Dimitra Dodou
8. Robert J. Vallerand

In Experimental and Cognitive Psychology

1. Alexandre J. S. Morin
2. Marylène Gagné
3. P. Parker
4. Robert J. Vallerand
5. Richard M. Ryan
6. Herbert W. Marsh
7. David Litalien
8. G. F. Losier

In Management Science and Operations Research

1. Alexandre J. S. Morin
2. Marylène Gagné
3. Thomas A. Schmitt
4. David Litalien
5. Herbert W. Marsh
6. Robert J. Vallerand
7. P. Parker
8. Richard M. Ryan

Citations in the corpus

• Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size (2012, result): “N = 50 was considered a relatively small sample size yielding marginally acceptable congruence with the population loadings [15].”
• Construction and initial validation of the Marianismo Beliefs Scale (2010, background): “Good recovery of population factors can be achieved with a sample range of 100 to 200 (MacCallum et al, 1999 ; de Winter, Dodou, & Wieringa, 2009 ).”
• Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis (2011, background): “But as de Winter et al. (2009) demonstrated, large factor loadings, fewer factors, and greater numbers of items can result in reliable solutions for small samples.”
• Exploratory structural equation modeling: an integration of the best features of exploratory and confirmatory factor analysis. (2014, methodology): “As emphasized by Browne (2001; also see Asparouhov & Muthén 2009, Dolan et al. 2009), www.annualreviews.org • Exploratory Structural Equation Modeling 3.5 Changes may still occur before final publication online and in print A nn u.”
• Evidence of a continuum structure of academic self-determination: A two-study test using a bifactor-ESEM representation of academic motivation (2017, background): “Statistical research even shows that these types of models are far less demanding than what was previously thought in terms of sample size (e.g., de Winter et al., 2009), and that even in these cases, factor scores saved from preliminary measurement models may help to preserve the underlying nature…”