A Bootstrap Procedure for Evaluating Goodness-of-Fit Indices of Structural Equation and Confirmatory Factor Models

1989 ◽  
Vol 26 (1) ◽  
pp. 105-111 ◽  
Author(s):  
Paula Fitzgerald Bone ◽  
Subhash Sharma ◽  
Terence A. Shimp
Keyword(s):  
Structural Equation ◽  
Goodness Of Fit ◽  
Factor Models ◽  
Model Fit ◽  
Fit Indices ◽  
Bootstrap Sampling ◽  
Bootstrap Procedure ◽  
Number Of Factors ◽  
Confirmatory Factor ◽  
Competing Models

The authors propose a bootstrap procedure for evaluating the goodness-of-fit indices for structural equation and confirmatory factor models. Monté Carlo simulations are applied to obtain a bootstrap sampling distribution (BSD) for each fit statistic. Then the BSD is used to evaluate model fit. Because the BSD takes into consideration sample size and model characteristics (e.g., number of factors, number of indicators per factor), its application in the proposed procedure makes it possible to compare the fits of competing models. Two previous studies are reanalyzed in illustrating how to implement the proposed procedure.

scholarly journals Validation of a Questionnaire Developed to Evaluate a Pediatric eHealth Website for Parents

2020 ◽  
Vol 17 (8) ◽  
pp. 2671 ◽  
Author(s):  
Bruno José Nievas Soriano ◽  
Sonia García Duarte ◽  
Ana María Fernández Alonso ◽  
Antonio Bonillo Perales ◽  
Tesifón Parrón Carreño
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Fit Indices ◽  
Content Validation ◽  
Confirmatory Factor Analyses ◽  
Expert Review ◽  
Confirmatory Factor ◽  
Competing Models ◽  
Confirmatory Analysis ◽  
Adequate Internal Consistency

There is a need for health professionals to provide parents with not only evidence-based child health websites but also instruments to evaluate them. The main aim of this research was to develop a questionnaire for measuring users’ evaluation of the usability, utility, confidence, the well-child section, and the accessibility of a Spanish pediatric eHealth website for parents. We further sought to evaluate the content validity and psychometric reliability of the instrument. A content validation study by expert review was performed, and the questionnaire was pilot tested. Psychometric analyses were used to establish scales through exploratory and confirmatory factor analyses. Reliability studies were performed using Cronbach’s alpha and two split-half methods. The content validation of the questionnaire by experts was considered as excellent. The pilot web survey was completed by 516 participants. The exploratory factor analysis excluded 27 of the 41 qualitative initial items. The confirmatory factor analysis of the resultant 14-item questionnaire confirmed the five initial domains detected in the exploratory confirmatory analysis. The goodness of fit for the competing models was established through fit indices and confirmed the previously established domains. Adequate internal consistency was found for each of the subscales as well as the overall scale.

scholarly journals Explicating the South African Psychological Ownership Questionnaire’s confirmatory factor analysis model fit: A Bayesian structural equation modelling approach

2019 ◽  
Vol 45 ◽  
Author(s):  
Pieter Schaap
Keyword(s):  
South African ◽  
Structural Equation ◽  
Measurement Model ◽  
Model Fit ◽  
Psychological Ownership ◽  
Fit Indices ◽  
Chi Square ◽  
Equation Modelling ◽  
Confirmatory Factor ◽  
Global Fit

Orientation: The rigid application of conventional confirmatory factor analysis (CFA) techniques, the overreliance on global model fit indices and the dismissal of the chi-square statistic appear to have an adverse impact on the research of psychological ownership measures.Research purpose: The purpose of this study was to explicate the South African Psychological Ownership Questionnaire’s (SAPOS’s) CFA model fit using the Bayesian structural equation modelling (BSEM) technique.Motivation for the study: The need to conduct this study derived from a renewed awareness of the incorrect use of the chi-square statistic and global fit indices of CFA in social sciences research.Research approach/design and method: The SAPOS measurement model fit was explicated on two study samples consisting, respectively, of 712 and 254 respondents who worked in various organisations in South Africa. A Bayesian approach to CFA was used to evaluate if local model misspecifications were substantive and justified the rejection of the SAPOS model.Main findings: The findings suggested that a rejection of the SAPOS measurement model based on the results of the chi-square statistic and global fit indices would be unrealistic and unfounded in terms of substantive test theory.Practical/managerial implications: BSEM appeared to be a valuable diagnostic tool to pinpoint and evaluate local CFA model misspecifications and their effect on a measurement model.Contribution/value-add: This study showed the importance of considering local misspecifications rather than only relying the chi-square statistic and global fit indices when evaluating model fit.

scholarly journals Mengakomodasi Efek Metode dalam Pengujian Validitas Konstruk Melalui Analisis Faktor Konfirmatori

2017 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Wahyu Widhiarso
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Equation Modeling ◽  
Fit Indices ◽  
Method Effect ◽  
Confirmatory Factor ◽  
Method Effects ◽  
Item Writing

Literatures in the field of psychometrics recommend researchers to employvarious of methods on measuring individual attributes. Ideally,each methods are complementary and measuresthe construct designed to be measured. However, some problems arise when among the methods is unique and unrelated to the construct being measured. The uniqueness of method can lead what is called the method effect. In testing construct validity using confirmatory factor analysis, the emergence of this effect tend to reducing the goodness of fit indices of the model. There are many ways to solve these problem, one of themis controling the method effects and accommodate it to the model. This paper introduces how to accommodate method effecton the confirmatory factor analysis using structural equation modeling. In the application section, author identify the emergence of method effects due to the differences item writing direction (favorable-unfavorable). The analysis showed that method effectemerge from different writing direction.

scholarly journals The Effects of Political Cynicism and National Siege Mentality on the Internalization of an Anti-European Sentiment

2016 ◽  
Vol 6 (1) ◽  
pp. 22
Author(s):  
Zlatko Šram
Keyword(s):  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Model ◽  
Survey Methods ◽  
Adult Population ◽  
Equation Model ◽  
Fit Indices ◽  
Political Cynicism ◽  
Confirmatory Factor ◽  
Unidimensional Construct

<p>This paper aims to provide an insight into the political-psychological understanding of an attitudinal construct labeled anti-European sentiment. A structural equation model for prediction was developed and evaluated by using full information mximum likelihood estimates obtained from LISREL 8.52 computer program. Assumption was that both political cynicism and national siege mentality would have an effect on anti-European sentiment. The data reported here were obtained by standard survey methods on the sample of adult population in Croatia (N=533). Confirmatory factor analysis (CFA) was performed to explore factorial and construct validity of the measures used in this research. CFA yielded unidimensional construct measurements with acceptable fit indices. Structural model indicated that exogenous variables (political cynicism, national siege mentality) have significant effects on the anti-European sentiment used as an endogenous (dependent) variable. Goodness-of-fit indices suggested acceptable fit of the model (RMSEA=0.07, CFI=0.97, NNFI=0.97, SRMR=0.05). Given the amount of variance of anti-European sentiment, it was showen that political cynicism and national siege mentality have strong predictive validity for anti-European sentiment (43 percent of the variance was explained by the structural model). In order to explain the interactions among the variables investigated, the author proposed the distrust-threat model of political hostility.</p>

Validation of a questionnaire developed to evaluate a Spanish eHealth paediatric web for parents. (Preprint)

2019 ◽  
Author(s):  
Bruno José Nievas Soriano ◽  
Sonia García Duarte ◽  
Ana María Fernández Alonso ◽  
Antonio Bonillo Perales ◽  
Tesifón Parrón Carreño
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Fit Indices ◽  
Content Validation ◽  
Confirmatory Factor Analyses ◽  
P Content ◽  
Confirmatory Factor ◽  
Competing Models ◽  
Adequate Internal Consistency ◽  
Paediatric Health

BACKGROUND Parents need information about their children’s symptoms and the Internet is a major resource that may serve as a convenient repository of health information for parents. There is an urgent need for health professionals to provide parents with evidence-based child health websites. There is also a need for instruments to measure their accessibility, usability, usefulness and confidence building. OBJECTIVE The main aim of the present study was to develop a questionnaire for measuring users’ evaluation of different aspects of a Spanish paediatric Health web for parents. We further sought to evaluate the content validity and psychometric reliability of our instrument. METHODS After a review of the literature we designed an initial 40-qualitative item pilot survey, following the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) guidelines. We performed a content validation study by experts review and the survey was then administered via web. Psychometric analyses were used to establish scales through exploratory and confirmatory factor analyses. Reliability studies were performed using Cronbach's Alpha and Two-Split Half Method. RESULTS Content validation of the questionnaire by experts was considered as excellent. The pilot web survey was completed by 516 volunteer participants. Exploratory factor analysis allowed us to exclude 26 of the 40 initial items. Confirmatory factor analysis of the resultant 14-item questionnaire confirmed the five initial domains previously detected in the exploratory confirmatory analysis. The goodness of fit for the competing models was established through fit indices and confirmed the previously established domains. Adequate internal consistency was found for each of the subscales as well as the overall scale. CONCLUSIONS Effectiveness and reliability are essential aspects of eHealth interventions and should be properly evaluated. Although our research has limitations, we can assume that our questionnaire is appropriate for the evaluation of an eHealth Spanish paediatric web for parents.

Should We Use F-Tests for Model Fit Instead of Chi-Square in Overidentified Structural Equation Models?

2018 ◽  
Vol 23 (3) ◽  
pp. 487-510 ◽  
Author(s):  
Daniel McNeish
Keyword(s):  
Data Model ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Small Sample ◽  
Model Fit ◽  
Ratio Test ◽  
Fit Indices ◽  
Chi Square ◽  
Advantages And Disadvantages

Debate continues about whether the likelihood ratio test ( T ML) or goodness-of-fit indices are most appropriate for assessing data-model fit in structural equation models. Though potential advantages and disadvantages of these methods with large samples are often discussed, shortcomings concomitant with smaller samples are not. This article aims to (a) highlight the broader small sample issues with both approaches to data-model fit assessment, (b) note that what constitutes a small sample is common in empirical studies (approximately 20% to 50% in review studies, depending on the definition of “small”), and (c) more widely introduce F-tests as a desirable alternative than the traditional T ML tests, small-sample corrections, or goodness-of-fit indices with smaller samples. Both goodness-of-fit indices and comparing T ML to a chi-square distribution at smaller samples leads to overrejection of well-fitting models. Simulations and example analyses show that F-tests yield more desirable statistical properties—with or without normality—than standard approaches like chi-square tests or goodness-of-fit indices with smaller samples, roughly defined as N < 200 or N: df < 3.

scholarly journals Why We Need to Abandon Fixed Cutoffs for Goodness-of-Fit Indices: A Comprehensive Simulation and Possible Solutions

2021 ◽  
Author(s):  
Katharina Groskurth ◽  
Matthias Bluemke ◽  
Clemens M. Lechner
Keyword(s):  
Goodness Of Fit ◽  
Model Misspecification ◽  
Model Fit ◽  
Fit Indices ◽  
New Approach ◽  
Response Options ◽  
Factor Loadings ◽  
Confirmatory Factor ◽  
Main Effect ◽  
Extraneous Data

To evaluate model fit in confirmatory factor analysis, researchers compare goodness-of-fit indices (GOFs) against fixed cutoff values derived from simulation studies. However, these cutoffs may not be as broadly applicable as researchers typically assume, especially when used in settings not covered in the simulation scenarios from which these cutoffs were derived. Thus, we aim to evaluate (1) the sensitivity of GOFs to model misspecification and (2) their susceptibility to extraneous data and analysis characteristics (i.e., estimator, number of indicators, number of response options, distribution of response options, loading magnitude, sample size, and factor correlation). Our study includes the most comprehensive simulation on that matter to date. This enables us to uncover several previously unknown or at least underappreciated issues with GOFs. All widely used GOFs are far more susceptible to extraneous influences in even more complex ways than generally appreciated, and their sensitivity to misspecifications in factor loadings and factor correlations varies significantly across different scenarios. For instance, one of those strong influences on all GOFs constituted the magnitude of factor loadings (either as a main effect or two-way interaction with other characteristics). The strong susceptibility of GOFs to data and analysis characteristics showed that the practice of judging the fit of models against fixed cutoffs is more problematic than so-far assumed. Hitherto unnoticed effects on GOFs imply that no general cutoff rules can be applied to evaluate model fit. We discuss alternatives for assessing model fit and develop a new approach to tailor cutoffs for GOFs to research settings.

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