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 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 Confirmatory and Exploratory Factor Analysis for Validating the Phlegm Pattern Questionnaire for Healthy Subjects

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Hyunho Kim ◽  
Boncho Ku ◽  
Jong Yeol Kim ◽  
Young-Jae Park ◽  
Young-Bae Park
Keyword(s):  
Factor Analysis ◽  
Construct Validity ◽  
Exploratory Factor Analysis ◽  
Healthy Subjects ◽  
Discriminant Validity ◽  
Correlation Coefficients ◽  
Model Fit ◽  
Healthy Population ◽  
Fit Indices ◽  
Model Fit Indices

Background. Phlegm pattern questionnaire (PPQ) was developed to evaluate and diagnose phlegm pattern in Korean Medicine and Traditional Chinese Medicine, but it was based on a dataset from patients who visited the hospital to consult with a clinician regarding their health without any strict exclusion or inclusion. In this study, we reinvestigated the construct validity of PPQ with a new dataset and confirmed the feasibility of applying it to a healthy population.Methods. 286 healthy subjects were finally included and their responses to PPQ were acquired. Confirmatory factor analysis (CFA) was conducted and the model fit was discussed. We extracted a new factor structure by exploratory factor analysis (EFA) and compared the two factor structures.Results. In CFA results, the model fit indices are acceptable (RMSEA = 0.074) or slightly less than the good fit values (CFI = 0.839, TLI = 0.860). Many average variances extracted were smaller than the correlation coefficients of the factors, which shows the somewhat insufficient discriminant validity.Conclusions. Through the results from CFA and EFA, this study shows clinically acceptable model fits and suggests the feasibility of applying PPQ to a healthy population with relatively good construct validity and internal consistency.

scholarly journals Using Fit Statistic Differences to Determine the Optimal Number of Factors to Retain in an Exploratory Factor Analysis

2019 ◽  
Vol 80 (2) ◽  
pp. 217-241 ◽  
Author(s):  
W. Holmes Finch
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Optimal Number ◽  
Parallel Analysis ◽  
Fit Indices ◽  
Number Of Factors ◽  
Indicator Variables ◽  
Model Fit Indices ◽  
Fit Index ◽  
Normally Distributed

Exploratory factor analysis (EFA) is widely used by researchers in the social sciences to characterize the latent structure underlying a set of observed indicator variables. One of the primary issues that must be resolved when conducting an EFA is determination of the number of factors to retain. There exist a large number of statistical tools designed to address this question, with none being universally optimal across applications. Recently, researchers have investigated the use of model fit indices that are commonly used in the conduct of confirmatory factor analysis to determine the number of factors to retain in EFA. These results have yielded mixed results, appearing to be effective when used in conjunction with normally distributed indicators, but not being as effective for categorical indicators. The purpose of this simulation study was to compare the performance of difference values for several fit indices as a method for identifying the optimal number of factors to retain in an EFA, with parallel analysis, which is one of the most reliable such extant methods. Results of the simulation demonstrated that the use of fit index difference values outperformed parallel analysis for categorical indicators, and for normally distributed indicators when factor loadings were small. Implications of these findings are discussed.

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 Exploratory and confirmatory factor analysis of the Roteiro de Avaliação da Consciência Fonológica, a phonological awareness test

2017 ◽  
Vol 34 (2) ◽  
pp. 211-218 ◽  
Author(s):  
Acácia Aparecida Angeli dos SANTOS ◽  
Thatiana Helena de LIMA
Keyword(s):  
Factor Analysis ◽  
Public Schools ◽  
Confirmatory Factor Analysis ◽  
Phonological Awareness ◽  
Exploratory Factor Analysis ◽  
Fit Indices ◽  
School Students ◽  
Confirmatory Factor ◽  
Model Fit Indices ◽  
The One

Abstract The objective of this study is to investigate the evidence of construct validity of a phonological awareness instrument. Exploratory factor analysis was carried out on data collected from 510 elementary and middle school students in 2nd and 6th grades attending two different public schools in the city of São Paulo, Brazil; most were males with mean age of 8.4 years. Confirmatory factor analysis was carried out on data collected from 427 students from other four Brazilian states in the same grades; most were females with mean age of 9.3 years. The instrument used was the Roteiro de Avaliação da Consciência Fonológica, a phonological awareness test. The exploratory factor analysis showed a three-factor solution. As for the confirmatory factor analysis, of the two models tested, the one that indicated better model fit indices was composed of three factors. The model found is adequate for the task carried out in this study. However, more studies should be carried out to further refine the instrument.

scholarly journals Pembuktian Validitas terkait Struktur Tes Potensi Akademik Pascasarjana (PAPS) Universitas Gadjah Mada

2019 ◽  
Vol 46 (2) ◽  
pp. 145
Author(s):  
Wahyu Widhiarso
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Factor Loading ◽  
Model Fit ◽  
The Other ◽  
Fit Indices ◽  
Confirmatory Factor ◽  
Model Fit Indices ◽  
Academic Potential ◽  
Potential Test

This study examined the construct validity of the Graduate Academic Potential Test (PAPS). The examination was performed on all existing PAPS series (6 forms) to identify the consistency of dimensionality structure of PAPS. Data of this study were analyzed using confirmatory factor analysis (CFA). The results of the analysis support assumption that the structure of the PAPS test is unidimensional. All of the model fit indices support the decision that the unidimensional model fit the data. The study also examined factor loading that the non-verbal components, especially the quantitative components that had a higher factor weight than the other components.

scholarly journals The Precision of the Overall Data-Model Fit for Different Design Features in Confirmatory Factor Analysis

2021 ◽  
Vol 11 (1) ◽  
pp. 6766-6774
Author(s):  
D. Almaleki
Keyword(s):  
Factor Analysis ◽  
Data Model ◽  
Empirical Evaluation ◽  
Model Fit ◽  
Fit Indices ◽  
Multiple Features ◽  
Study Results ◽  
Confirmatory Factor ◽  
Model Fit Indices ◽  
Study Context

Factor Analysis (FA) is the study of variance within a group. Within-Subject Variance (WSV) is affected by multiple features in a study context such as the Experimental Design (ED) or the Sampling Design (SD). The aim of this study is to provide an empirical evaluation of the influence of different aspects of ED and SD on WSV in the context of FA in terms of model precision. The study results showed that the precisions of the overall model fit indices TLI and CFI, as functions of VTF, STV, h2, and their interaction, varied, as did the precisions of the overall model fit indices GFI, AGFI, and RMSEA as functions of VTF, STV, and their interactions. Overall, when the VTF is 4:1 or 7:1, the required STV is 16:1 or above 32:1 or above to show precision in factor solution.

scholarly journals Professional Fulfillment: Concept and Instrument Proposition

Psico-USF ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 27-39
Author(s):  
Ligia Carolina Oliveira-Silva ◽  
Juliana Barreiros Porto ◽  
John Arnold
Keyword(s):  
Factor Analysis ◽  
Structural Equation ◽  
Model Fit ◽  
Career Goals ◽  
Equation Modeling ◽  
Fit Indices ◽  
Confirmatory Factor ◽  
Model Fit Indices ◽  
The Right ◽  
Professional Fulfillment

Abstract This paper aims to propose a concept and an instrument of professional fulfillment (PF), which is theoretically defined as the perception of having attained or being on the right track for attaining one’s most important career goals. The Professional Fulfillment Scale (PFS) was developed in order to operationalize PF, being tested across two studies. Regarding Study 1, in which 406 workers took part, results from exploratory factor analysis evidenced construct validity for PFS. In Study 2, in which 270 workers took part, results from confirmatory factor analysis and structural equation modeling presented satisfactory model fit indices for PFS. We conclude that both the professional fulfillment concept and the scale are useful for mapping people’s importance and achievement of career goals and evaluation of progress, working as a diagnostic tool for career management.

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