scholarly journals Spurious Factor Analysis

Econometrica ◽  
2021 ◽  
Vol 89 (2) ◽  
pp. 591-614
Author(s):  
Alexei Onatski ◽  
Chen Wang

This paper draws parallels between the principal components analysis of factorless high‐dimensional nonstationary data and the classical spurious regression. We show that a few of the principal components of such data absorb nearly all the data variation. The corresponding scree plot suggests that the data contain a few factors, which is corroborated by the standard panel information criteria. Furthermore, the Dickey–Fuller tests of the unit root hypothesis applied to the estimated “idiosyncratic terms” often reject, creating an impression that a few factors are responsible for most of the nonstationarity in the data. We warn empirical researchers of these peculiar effects and suggest to always compare the analysis in levels with that in differences.

2007 ◽  
Vol 100 (2) ◽  
pp. 355-364 ◽  
Author(s):  
Sook-Jeong Lee

The purpose of this study was to examine the psychometric properties of the Specific Interpersonal Trust scale of Johnson-George and Swap in Korean samples as a part of the process of providing an exemplary tool for intercultural studies of trust. A translated version of the original scale was administered to 337 university students (157 males, 180 females) in Seoul, Korea. Data were subjected to a principal components analysis and a confirmatory factor analysis. In principal components analysis for the Korean sample ( n = 167), three factors were identified and labeled: Overall Trust (Cronbach α=.89), Emotional Trust (Cronbach α = .88), and Reliableness (Cronbach α=.84). A confirmatory factor analysis ( n=170) showed that the three-factor model was valid for the sample (χ2/ df= 1.78, RMR=.06, RMSEA = .07, TLI=.92, CFI=.93). Internal consistency reliability and factorial validity were satisfactory in the case of the Korean sample. The Korean version of the Specific Interpersonal Trust Scale made good use of three factors of trust and appeared to be valid without sex differences, while the original scale distinguished the Males subscale from the Females subscale. Implications and limitations of this study were discussed.


2021 ◽  
Vol 13 (37) ◽  
pp. 4188-4219
Author(s):  
Peter D. Wentzell ◽  
Cannon Giglio ◽  
Mohsen Kompany-Zareh

Principal components analysis (PCA) is widely used in analytical chemistry, but is only one type of broader range of factor analysis tools that are described in this article.


1975 ◽  
Vol 37 (3) ◽  
pp. 849-850 ◽  
Author(s):  
David W. Stewart ◽  
G. Mac Griffith

A principal components analysis of Sensation-seeking Scale IV ( n = 156 undergraduates) followed by a varimax rotation provided some support to the factorial validity of some of Zuckerman's subscales. It is suggested that the dimensions of sensation seeking were arbitrarily limited in earlier work on the problem.


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