How to determine the number of factors to retain in exploratory factor analysis: A comparison of extraction methods under realistic conditions.

2019 ◽  
Vol 24 (4) ◽  
pp. 468-491 ◽  
Author(s):  
Max Auerswald ◽  
Morten Moshagen
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Extraction Methods ◽  
Number Of Factors

scholarly journals Likert scale and change in range of response categories vs. the factors extraction in EFA model

2015 ◽  
Vol 1 (311) ◽  
Author(s):  
Piotr Tarka
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Rating Scale ◽  
Likert Scale ◽  
Optimal Number ◽  
Number Of Factors ◽  
Scale Experiment ◽  
Methodological Aspects ◽  
The Impact ◽  
Selection Of

Abstract: The objective article is the comparative analysis of Likert rating scale based on the following range of response categories, i.e. 5, 7, 9 and 11 in context of the appropriate process of factors extraction in exploratory factor analysis (EFA). The problem which is being addressed in article relates primarily to the methodological aspects, both in selection of the optimal number of response categories of the measured items (constituting the Likert scale) and identification of possible changes, differences or similarities associated (as a result of the impact of four types of scales) with extraction and determination the appropriate number of factors in EFA model.Keywords: Exploratory factor analysis, Likert scale, experiment research, marketing

scholarly journals An exploratory factor analysis of the spontaneous reporting of severe cutaneous adverse reactions

2016 ◽  
Vol 8 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Manfred Hauben ◽  
Eric Hung ◽  
Wen-Yaw Hsieh
Keyword(s):  
Factor Analysis ◽  
T Cell ◽  
Exploratory Factor Analysis ◽  
Adverse Reactions ◽  
Overlap Syndromes ◽  
Number Of Factors ◽  
Shared Variance ◽  
Severe Cutaneous Adverse Reactions ◽  
Cutaneous Adverse Reactions ◽  
Variance Explained

Background: Severe cutaneous adverse reactions (SCARs) are prominent in pharmacovigilance (PhV). They have some commonalities such as nonimmediate nature and T-cell mediation and rare overlap syndromes have been documented, most commonly involving acute generalized exanthematous pustulosis (AGEP) and drug rash with eosinophilia and systemic symptoms (DRESS), and DRESS and toxic epidermal necrolysis (TEN). However, they display diverse clinical phenotypes and variations in specific T-cell immune response profiles, plus some specific genotype–phenotype associations. A question is whether causation of a given SCAR by a given drug supports causality of the same drug for other SCARs. If so, we might expect significant intercorrelations between SCARs with respect to overall drug-reporting patterns. SCARs with significant intercorrelations may reflect a unified underlying concept. Methods: We used exploratory factor analysis (EFA) on data from the United States Food and Drug Administration Adverse Event Reporting System (FAERS) to assess reporting intercorrelations between six SCARs [AGEP, DRESS, erythema multiforme (EM), Stevens–Johnson syndrome (SJS), TEN, exfoliative dermatitis (ExfolDerm)]. We screened the data using visual inspection of scatterplot matrices for problematic data patterns. We assessed factorability via Bartlett’s test of sphericity, Kaiser-Myer-Olkin (KMO) statistic, initial estimates of communality and the anti-image correlation matrix. We extracted factors via principle axis factoring (PAF). The number of factors was determined by scree plot/Kaiser’s rule. We also examined solutions with an additional factor. We applied various oblique rotations. We assessed the strength of the solution by percentage of variance explained, minimum number of factors loading per major factor, the magnitude of the communalities, loadings and crossloadings, and reproduced- and residual correlations. Results: The data were generally adequate for factor analysis but the amount of variance explained, shared variance, and communalities were low, suggesting caution in general against extrapolating causality between SCARs. SJS and TEN displayed most shared variance. AGEP and DRESS, the other SCAR pair most often observed in overlap syndromes, demonstrated modest shared variance, along with maculopapular rash (MPR). DRESS and TEN, another of the more commonly diagnosed pairs in overlap syndromes, did not. EM was uncorrelated with SJS and TEN. Conclusions: The notion that causality of a drug for one SCAR bolsters support for causality of the same drug with other SCARs was generally not supported.

scholarly journals The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation

2020 ◽  
pp. 001316442094289
Author(s):  
Amanda K. Montoya ◽  
Michael C. Edwards
Keyword(s):  
Factor Analysis ◽  
Root Mean Square ◽  
Exploratory Factor Analysis ◽  
Model Fit ◽  
Mean Square ◽  
Fit Indices ◽  
Scale Evaluation ◽  
Number Of Factors ◽  
Model Fit Indices ◽  
The Impact

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule ( N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.

scholarly journals Exploratory factor analysis with structured residuals for brain network data

2020 ◽  
pp. 1-27
Author(s):  
Erik-Jan van Kesteren ◽  
Rogier A. Kievit
Keyword(s):  
Factor Analysis ◽  
Dimension Reduction ◽  
Exploratory Factor Analysis ◽  
A Priori ◽  
Brain Regions ◽  
Brain Network ◽  
R Software ◽  
Network Analyses ◽  
Number Of Factors ◽  
The Brain

Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high-dimensional data to a small number of underlying variables. Techniques such as exploratory factor analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA by using structured residuals (EFAST), and (c) apply this technique to three large and varied brain-imaging network datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.

The poor fit of model fit for selecting number of factors in exploratory factor analysis for scale evaluation

2020 ◽  
Author(s):  
Amanda Kay Montoya ◽  
Michael C. Edwards
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Evaluation Framework ◽  
Model Fit ◽  
Model Specification ◽  
Fit Indices ◽  
Scale Evaluation ◽  
Number Of Factors ◽  
Model Fit Indices ◽  
The Impact

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean squared error of approximate (RMSEA), standardized root mean squared residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule (N = 1000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor EFA. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions which are over-factored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.

scholarly journals Estimating the Number of Factors in Exploratory Factor Analysis via out-of-sample Prediction Errors

2021 ◽  
Author(s):  
Jonas M B Haslbeck ◽  
Riet van Bork
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
High Performance ◽  
Factor Model ◽  
R Package ◽  
Prediction Errors ◽  
Number Of Factors ◽  
Out Of Sample ◽  
The One ◽  
Out Of Sample Prediction

Exploratory Factor Analysis (EFA) is one of the most popular statistical models in psychological science. A key problem in EFA is to estimate the number of factors. In this paper, we present a new method for estimating the number of factors based on minimizing the out-of-sample prediction error of candidate factor models. We show in an extensive simulation study that our method slightly outperforms existing methods, including parallel analysis, the BIC, and Exploratory Graph Analysis. In addition, we show that among the high performance method our method is the one that is most robust across different specifications of the true factor model. We provide an implementation of our method in the R-package fspe.

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