2013
DOI: 10.1016/j.beproc.2012.11.016
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Exploratory factor analysis with small sample sizes: A comparison of three approaches

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Cited by 81 publications
(64 citation statements)
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“…The factor structures extracted by REFA, with an anti‐image regularization scheme and varimax rotation [Jung & Lee, ; Jung, personal communication] and by PCA with varimax rotation were highly comparable. The REFA solution had slightly lower loadings of factors, relative to PCA, as predicted [Jung, ; Konečná et al, ]. Targeted orthogonal Procrustes rotation [McCrae et al, ] confirmed this similarity‐there was an overall solution congruence of 1.00 when rounded to two decimal places.…”
Section: Resultsmentioning
confidence: 54%
See 1 more Smart Citation
“…The factor structures extracted by REFA, with an anti‐image regularization scheme and varimax rotation [Jung & Lee, ; Jung, personal communication] and by PCA with varimax rotation were highly comparable. The REFA solution had slightly lower loadings of factors, relative to PCA, as predicted [Jung, ; Konečná et al, ]. Targeted orthogonal Procrustes rotation [McCrae et al, ] confirmed this similarity‐there was an overall solution congruence of 1.00 when rounded to two decimal places.…”
Section: Resultsmentioning
confidence: 54%
“…Additionally, we reduced our data using regularized exploratory factor analysis (REFA), a method developed specifically for small sample sizes [Jung, ; Jung & Lee, ; Jung & Takane, ] using MATLAB software. The factor structures extracted by REFA, with an anti‐image regularization scheme and varimax rotation [Jung & Lee, ; Jung, personal communication] and by PCA with varimax rotation were highly comparable.…”
Section: Resultsmentioning
confidence: 99%
“…In case the sample size is small, the method preferred is Unweighted Least Squares (ULS). 26 Therefore, the ULS method was used as factor extraction method in the exploratory factor analysis. To determine the rotation method to be used, the correlation between the factors was investigated.…”
Section: Descriptive Characteristics Of the Patients And Caregiversmentioning
confidence: 99%
“…The communalities, i.e., the amount of variance in each item that is measured or accounted for by the components that were extracted with Varimax, are shown in Table II. Low communalities are considered to be those below 0.4, while those over 0.6 are considered high ( Jung, 2013). In factor analysis, only those factors or components with an eigenvalue greater than unity were considered, and only four components met that requirement.…”
Section: Factor Analysismentioning
confidence: 99%