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Invariance of the “NEO-PI-R” factor structure across exploratory and confirmatory factor analyses
Personality and Individual Differences, 2005
Confirmatory factor analyses (CFA) carried out in the last decades to test the Five-factor simple structure have generally obtained negative results. suggested that CFA limitations were the main cause of such negative results. They also claim that Procrustes Orthogonal rotation method is an adequate procedure to test the replicability of the Five-factor structure. The aim of the present paper is twofold: (A) comparing several Exploratory factor procedures (including the Procrustes rotation) to test what is the most appropriate one to analyse the replicability of the Five-factor structure, and (B) replicating the CFA results of the McCrae et al.Õs study with a larger number of subjects. The normative American (N = 1000), and Spanish standardization samples of the NEO-PI-R, together with an independent university sample (N = 948) were analysed. Results were replicated in the three samples, and suggest that: (1) structural properties of the Five-factor model, as measured by the NEO-PI-R, are invariant irrespective of the Exploratory factor procedure used, and (2) when CFA limitations are surpassed, the Five-factor structure is supported.
Confirmatory Analysis of Exploratively Obtained Factor Structures
Educational and Psychological Measurement, 2001
Factor structures obtained by exploratory factor analysis (EFA) often turn out to fit poorly in confirmative follow-up studies. In the present study, the authors assessed the extent to which results obtained in EFA studies can be replicated by confirmatory factor analysis (CFA) in the same sample. More specifically, the authors used CFA to test three different factor models on several correlation matrices of exploratively obtained factor structures that were reported in the literature. The factor models varied with respect to the role of the smaller factor pattern coefficients. Results showed that confirmatory factor models in which all low EFA pattern coefficients were fixed to zero fitted especially poorly. The authors conclude that it may be justified to use a less constrained model when testing a factor model by allowing some correlation among the factors and some of the lower factor pattern coefficients to differ from zero.
Personality and Individual Differences, 1997
Establishing factorial invariance across samples and forms has important implications for efforts to build a taxonomy of personality traits. These methods provide a strong test of measurement properties and, if factorial invariance obtains, allow meaningful factor comparisons across groups. Results are presented for an invariant second-order factor structure in two forms (Form C and Clinical Analysis Questionnaire) of the 16PF and across six independent samples: four samples consisting of male and female police applicants (combined N = 15,332) and two samples of male felons (combined N = 15,460). Independently rotated common factor analyses indicated that six previously reported factors (Extraversion, Anxiety, Control, Independence, Sensitive Awareness, and Intelligence) were well-replicated in all samples. A restricted factor solution (salient loadings only) with identical factor loadings (metric factorial invariance) was found to hold remarkably well across all samples. Further constraints on the factor model known as 'strict factorial invariance' ; invariant factor loadings, mean intercepts, and unique variances) were found to provide a good fit to the data within police applicant and felon samples. The findings from this study offer strong evidence in support of a stable five-factor personality structure of the 16PF across different forms and people. 0 1997
Behavior Research Methods
A common difficulty in the factor analysis of items designed to measure psychological constructs is that the factor structures obtained using exploratory factor analysis tend to be rejected if they are tested statistically with a confirmatory factor model. An alternative to confirmatory factor analysis is unrestricted factor analysis based on Procrustes rotation, which minimizes the distance from a target matrix proposed by the researcher. In the present article, we focus on the situation in which researchers propose a partially specified target matrix but are prepared to allow their initial target to be refined. Here we discuss RETAM as a new procedure for objectively refining target matrices. To date, it has been recommended that this kind of refinement be guided by human judgment. However, our approach is objective, because the threshold value is computed automatically (not decided on by the researcher) and there is no need to manually compute a number of factor rotations every time. The new procedure was tested in an extensive simulation study, and the results suggest that it may be a useful procedure in factor analysis applications based on incomplete measurement theory. Its feasibility in practice is illustrated with an empirical example from the personality domain. Finally, RETAM is implemented in a well-known noncommercial program for performing unrestricted factor analysis.
Best Practices for Exploratory Factor Analysis: Target Rotations and Correlated Errors
Factor analysis is commonly used in the development and evaluation of tests. When items are not pure measures of the theoretical constructs of interests, Exploratory Factor Analysis (EFA) provides a more suitable model of a test’s factor structure than Confirmatory Factor Analysis. However, EFA has been under-utilized largely because of its seeming lack of theoretical rigor, the arbitrariness of rotation types, and an inability to adjust for idiosyncratic item associations from method and design effects. We demonstrate that implementation of Exploratory Structural Equation Modeling (ESEM) in Mplus (Asparouhov & Muthén, 2009) overcomes these shortfalls, and allows the estimation of EFA models with theoretically-driven target rotation techniques and correlated errors. These techniques improve model fit and theoretical interpretability, and represent significant advances in EFA modeling methods.
Understanding and controlling rotations in factor analytic models
Chemometrics and Intelligent Laboratory Systems, 2002
Positive Matrix Factorization (PMF) is a least-squares approach for solving the factor analysis problem. It has been implemented in several forms. Initially, a program called PMF2 was used. Subsequently, a new, more flexible modeling tool, the Multilinear Engine, was developed. These programs can utilize different approaches to handle the problem of rotational indeterminacy. Although both utilize non-negativity constraints to reduce rotational freedom, such constraints are generally insufficient to wholly eliminate the rotational problem. Additional approaches to control rotations are discussed in this paper: (1) global imposition of additions among “scores” and subtractions among the corresponding “loadings” (or vice versa), (2) constraining individual factor elements, either scores and/or loadings, toward zero values, (3) prescribing values for ratios of certain key factor elements, or (4) specifying certain columns of the loadings matrix as known fixed values. It is emphasized that application of these techniques must be based on some external information about acceptable or desirable shapes of factors. If no such a priori information exists, then the full range of possible rotations can be explored, but there is no basis for choosing one of these rotations as the “best” result. Methods for estimating the rotational ambiguity in any specific result are discussed.
NEO-PI-R Factor Structure in College Students
Little is known about the effectiveness and validity of the revised NEO personality inventory (NEO-PI-R) for identifying the personality traits of the big five in Indian context on students’ sample. The main objectives of this study were to examine the replicability of the five-factor model and to establish external validity for personality traits in this population. A total of 205 technology students completed the NEO-PI-R, Emotional intelligence scale and Oxford Happiness Questionnaire. Using principal component analysis with varimax rotation, the dimensions of personality in the Indian students sample clearly replicate the five-factor structure for N,C, and A except A5 facet. Whereas, O and E did not get high loading of their all facets. Psychometric properties of NEO-PI-R have been discussed in this paper