A new powerful transformation for maximum likelihood common factor analysis (MLCFA)

Persoone, P.; Gryse, Roger De; Volder, P. De

doi:10.1016/0368-2048(94)02284-4

Cited by 11 publications

(10 citation statements)

References 14 publications

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“…Further, CFA aims to examine the ability of models with predefined factors to fit actual data and if data obeys the normal distribution maximum likelihood (ML) which is a common method of estimation within CFA [ 68 ] will be used. In terms of the RS-14 and its related scales, the consistency of its structure with single-factor structures was investigated via factor analytic research.…”

Section: Methodsmentioning

confidence: 99%

Psychometric properties of the Chinese version of the Resilience Scale (RS-14): Preliminary results

et al. 2020

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Background In recent years, resilience has received extensive attention in psychology. The 14-Item Resilience Scale (RS-14) has been developed as a newer and shorter version of the resilience scale and has been applied in Western countries. In Eastern cultures, however, and particularly among Chinese populations, its factor structure remains unverified. The purpose of this study is to realize the first evaluation of the psychometric characteristics of the Chinese version of the RS-14 in young adults from Mainland China. Methods The resilience scale, Connor-Davidson resilience scale, general health questionnaire 12, perceived stress scale 14, general self-efficacy scale and meaning in life questionnaire were used to investigate 1010 undergraduates (321 male college students, 689 female college students, aged 17–25 years; mean age = 20.27; SD = 1.572). We evaluated the item quality, latent structure, reliability, criterion validity and differential item functioning on the gender variable. Results Through the analysis methods of exploratory and confirmatory factor-analytic, the original single-factor model has been proven to be applicable within the Chinese population. Both an adequate construct validity and an excellent degree of reliability were reflected in the data. In addition, test-retest evinced good stability. The current study interrogates associations with external criteria, as well as providing evidence in support of the RS-14. Conclusion To sum up, this study showed that the RS-14 is a reliable assessment for measuring resilience in China, and provides an alternative to the original scale.

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Section: Methodsmentioning

confidence: 99%

Psychometric properties of the Chinese version of the Resilience Scale (RS-14): Preliminary results

et al. 2020

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“…Five decomposition methods based on different mathematical conditions or constraints were compared: (1) principal component analysis (PCA), , (2) maximum likelihood common factor analysis (MLCFA), ,,, (3) independent component analysis (ICA), , (4) self-modeling mixture analysis (SMA), , and (5) multivariate curve resolution (MCR) …”

Section: Methodsmentioning

confidence: 99%

In Quest of the Alanine R3 Radical: Multivariate EPR Spectral Analyses of X-Irradiated Alanine in the Solid State

et al. 2017

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The amino acid l-α-alanine is the most commonly used material for solid-state electron paramagnetic resonance (EPR) dosimetry, due to the formation of highly stable radicals upon irradiation, with yields proportional to the radiation dose. Two major alanine radical components designated R1 and R2 have previously been uniquely characterized from EPR and electron-nuclear double resonance (ENDOR) studies as well as from quantum chemical calculations. There is also convincing experimental evidence of a third minor radical component R3, and a tentative radical structure has been suggested, even though no well-defined spectral signature has been observed experimentally. In the present study, temperature dependent EPR spectra of X-ray irradiated polycrystalline alanine were analyzed using five multivariate methods in further attempts to understand the composite nature of the alanine dosimeter EPR spectrum. Principal component analysis (PCA), maximum likelihood common factor analysis (MLCFA), independent component analysis (ICA), self-modeling mixture analysis (SMA), and multivariate curve resolution (MCR) were used to extract pure radical spectra and their fractional contributions from the experimental EPR spectra. All methods yielded spectral estimates resembling the established R1 spectrum. Furthermore, SMA and MCR consistently predicted both the established R2 spectrum and the shape of the R3 spectrum. The predicted shape of the R3 spectrum corresponded well with the proposed tentative spectrum derived from spectrum simulations. Thus, results from two independent multivariate data analysis techniques strongly support the previous evidence that three radicals are indeed present in irradiated alanine samples.

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“…in terms of scores and loadings. To do this, consider the form of the PCA model normally arrived at through SVD:X =ÛŜV T (12) where X is mϫ n, Û is mϫ p, Ŝ is pϫ p and V is nϫ p. The caret on X denotes that these are the maximum likelihood estimates of the measurements in accordance with the p-dimensional model and U, Ŝ and V are obtained from the singular value decomposition of X , which is constrained to be rank p. Now X and Û are partitioned into the upper p rows (X 1 and Û 1 ) and the lower m Ϫ p rows (X 2 and U 2 ) to giveX…”

Section: Mlpca With No Intercept Termsmentioning

confidence: 99%

“…9,10 More recently, MLCFA has appeared in the chemical literature with claims that it performs better than PCA. 11,12 However, MLCFA was developed with the intention of finding structural models for random variables. As such, it estimates covariance matrices for random variables and does not generally use information about measurement errors.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood principal component analysis

Wentzell¹,

Andrews²,

Hamilton³

et al. 1997

J. Chemometrics

213

168

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SUMMARYThe theoretical principles and practical implementation of a new method for multivariate data analysis, maximum likelihood principal component analysis (MLPCA), are described. MLCPA is an analog to principal component analysis (PCA) that incorporates information about measurement errors to develop PCA models that are optimal in a maximum likelihood sense. The theoretical foundations of MLPCA are initially established using a regression model and extended to the framework of PCA and singular value decomposition (SVD). An efficient and reliable algorithm based on an alternating regression method is described. Generalization of the algorithm allows its adaptation to cases of correlated errors provided that the error covariance matrix is known. Models with intercept terms can also be accommodated. Simulated data and near-infrared spectra, with a variety of error structures, are used to evaluate the performance of the new algorithm. Convergence times depend on the error structure but are typically around a few minutes. In all cases, models determined by MLPCA are found to be superior to those obtained by PCA when non-uniform error distributions are present, although the level of improvement depends on the error structure of the particular data set.

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A new powerful transformation for maximum likelihood common factor analysis (MLCFA)

Cited by 11 publications

References 14 publications

Psychometric properties of the Chinese version of the Resilience Scale (RS-14): Preliminary results

Psychometric properties of the Chinese version of the Resilience Scale (RS-14): Preliminary results

In Quest of the Alanine R3 Radical: Multivariate EPR Spectral Analyses of X-Irradiated Alanine in the Solid State

Maximum likelihood principal component analysis

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