Assessing Cutoff Values of SEM Fit Indices: Advantages of the Unbiased SRMR Index and Its Cutoff Criterion Based on Communality

2022 ◽  
pp. 1-13
Author(s):  
Carmen Ximénez ◽  
Alberto Maydeu-Olivares ◽  
Dexin Shi ◽  
Javier Revuelta
Keyword(s):  
Fit Indices ◽  
Cutoff Values ◽  
Cutoff Criterion

Is That Measure Really One-Dimensional?

Methodology ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 188-196 ◽  
Author(s):  
Esther T. Beierl ◽  
Markus Bühner ◽  
Moritz Heene
Keyword(s):  
Model Fit ◽  
Nuisance Parameters ◽  
Factorial Validity ◽  
Fit Indices ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
One Dimensional ◽  
Confirmatory Factor Analysis Model ◽  
Cutoff Values ◽  
Confirmatory Factor

Abstract. Factorial validity is often assessed using confirmatory factor analysis. Model fit is commonly evaluated using the cutoff values for the fit indices proposed by Hu and Bentler (1999) . There is a body of research showing that those cutoff values cannot be generalized. Model fit does not only depend on the severity of misspecification, but also on nuisance parameters, which are independent of the misspecification. Using a simulation study, we demonstrate their influence on measures of model fit. We specified a severe misspecification, omitting a second factor, which signifies factorial invalidity. Measures of model fit showed only small misfit because nuisance parameters, magnitude of factor loadings and a balanced/imbalanced number of indicators per factor, also influenced the degree of misfit. Drawing from our results, we discuss challenges in the assessment of factorial validity.

A note on the cutoff values of alternative fit indices to evaluate measurement invariance for ESEM models

2019 ◽  
Vol 44 (2) ◽  
pp. 166-174
Author(s):  
Ying Jin
Keyword(s):  
Measurement Invariance ◽  
Structural Equation ◽  
Mean Squared Error ◽  
Simulated Data ◽  
Index Formula ◽  
Equation Modeling ◽  
Approximation Formula ◽  
Fit Indices ◽  
Cutoff Values ◽  
Partial Invariance

This research examines the performance of the previously proposed cutoff values of alternative fit indices (i.e., change in comparative fit index [[Formula: see text]], change in Tucker–Lewis index [[Formula: see text]], and change in root mean squared error of approximation [[Formula: see text]]) to evaluate measurement invariance for exploratory structural equation modeling (ESEM) models with simulated data. It is important to revisit these cutoff values because they were used widely in validity studies utilizing ESEM models to evaluate measurement invariance for ordinal indicators, but in fact, these cutoff values were developed under confirmatory factor analysis models with continuous indicators. Results of this study show that different cutoff values of [Formula: see text], [Formula: see text], and [Formula: see text] should be used for ESEM models with ordinal indicators. Evaluation of partial invariance for ESEM models is also discussed.

scholarly journals Guessing and Nature of Multidimensionality Matter: A Cautionary Note on the Use of Fit Indices to Assess Unidimensionality of Binary Data

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Yong Luo
Keyword(s):  
Binary Data ◽  
Statistical Power ◽  
Model Fit ◽  
Simulation Studies ◽  
Fit Indices ◽  
Dramatic Decrease ◽  
Cutoff Values ◽  
Model Fit Indices ◽  
Multiple Choice Items ◽  
Analysis Models

Use of cutoff values for model fit indices to assess dimensionality of binary data representing scores on multiple-choice items is a popular approach among researchers and practitioners, and the commonly used cutoff values are based on simulation studies that used as the generating model factor analysis models, which are compensatory models without modeling guessing. Consequently, it remains unknown how those cutoff values for model fit indices would perform when (a) guessing exists in data, and (b) data follow a noncompensatory multidimensional structure. In this paper, we conducted a comprehensive simulation study to investigate how guessing affected the statistical power of commonly used cutoff values for RMSEA, CFA, and TLI (RMSEA > 0.05; CFA < 0.95; TLI < 0.95) to detect violation of unidimensionality of binary data with both compensatory and noncompensatory models. The results indicated that when data were generated with compensatory models, increase of guessing values resulted in the systematic decrease of the power of RMSEA, CFA, and TLI to detect multidimensionality and in some conditions, a small increase of guessing value can result in dramatic decrease of their statistical power. It was also found that when data were generated with noncompensatory models, use of cutoff values of RMSEA, CFA, and TLI for unidimensionality assessment had unacceptably low statistical power, and while change of guessing magnitude could considerably change their statistical power, such changes were not systematic as in the compensatory models.  

A Psychometric Study of the Quality Perception Questionnaire

2012 ◽  
Vol 71 (2) ◽  
pp. 101-106 ◽  
Author(s):  
Raffaele Cioffi† ◽  
Anna Coluccia ◽  
Fabio Ferretti ◽  
Francesca Lorini ◽  
Aristide Saggino ◽  
...  
Keyword(s):  
Structural Equation ◽  
Factor Model ◽  
Equation Modeling ◽  
Fit Indices ◽  
Quality Perception ◽  
Italian Survey ◽  
Confirmatory Factor ◽  
Instrument Measuring ◽  
Hospital Structures

The present paper reexamines the psychometric properties of the Quality Perception Questionnaire (QPQ), an Italian survey instrument measuring patients’ perceptions of the quality of a recent hospital admission experience, in a sample of 4400 patients (Mage = 56.42 years; SD = 19.71 years, 48.8% females). The 14-item survey measures four factors: satisfaction with medical doctors, nursing staff, auxiliary staff, and hospital structures. First, we tested two models using a confirmatory factor analysis (structural equation modeling): a four orthogonal factor and a four oblique factor model. The SEM fit indices and the χ² difference suggested the acceptance of the second model. We then did a simulation using a bootstrap with 1000 replications. Results confirmed the four oblique factor solution. Third, we tested whether there were significant differences with respect to age or sex. The multivariate general linear model showed no significant differences in the factors with respect to sex or age.

Validation of the Short Dark Triad in a German Sample

2020 ◽  
pp. 1-12
Author(s):  
Caroline Wehner ◽  
Ulrike Maaß ◽  
Marius Leckelt ◽  
Mitja D. Back ◽  
Matthias Ziegler
Keyword(s):  
Sensation Seeking ◽  
Factor Model ◽  
Structural Models ◽  
Self Esteem ◽  
Dark Triad ◽  
Fit Indices ◽  
Nomological Network ◽  
Competitive Test ◽  
Three Factor ◽  
Behavioral Criteria

Abstract. The structure, correlates, and assessment of the Dark Triad are widely discussed in several fields of psychology. Based on the German version of the Short Dark Triad (SDT), we add to this by (a) providing a competitive test of existing structural models, (b) testing the nomological network, and (c) proposing an ultrashort 9-item version of the SDT (uSDT). A sample of N = 969 participants provided data on the SDT and a range of further measures. Our competitive test of five structural models revealed that fit indices and nomological network assumptions were best met in a three-factor model, with separate factors for psychopathy, Machiavellianism, and narcissism. The results provided an extensive overview of the raw, unique, and shared associations of Dark Triad dimensions with narcissism facets, sadism, impulsivity, self-esteem, sensation seeking, the Big Five, maladaptive personality traits, sociosexual orientation, and behavioral criteria. Finally, the uSDT exhibited satisfactory psychometric properties. The highest overlap in expected relations between SDT and uSDT, and convergent and discriminant measures was also found for the three-factor model. Our study underlines the utility of a three-factor model of the Dark Triad, extends findings on its nomological network, and provides an ultrashort instrument.

Detecting Random Responses in a Personality Scale Using IRT-Based Person-Fit Indices

2019 ◽  
Vol 35 (1) ◽  
pp. 126-136 ◽  
Author(s):  
Tour Liu ◽  
Tian Lan ◽  
Tao Xin
Keyword(s):  
Experimental System ◽  
Real Data ◽  
Fit Indices ◽  
Random Response ◽  
Person Fit ◽  
Index Method ◽  
Single Person ◽  
Person Fit Index ◽  
Random Responses ◽  
Response Behaviors

Abstract. Random response is a very common aberrant response behavior in personality tests and may negatively affect the reliability, validity, or other analytical aspects of psychological assessment. Typically, researchers use a single person-fit index to identify random responses. This study recommends a three-step person-fit analysis procedure. Unlike the typical single person-fit methods, the three-step procedure identifies both global misfit and local misfit individuals using different person-fit indices. This procedure was able to identify more local misfit individuals than single-index method, and a graphical method was used to visualize those particular items in which random response behaviors appear. This method may be useful to researchers in that it will provide them with more information about response behaviors, allowing better evaluation of scale administration and development of more plausible explanations. Real data were used in this study instead of simulation data. In order to create real random responses, an experimental test administration was designed. Four different random response samples were produced using this experimental system.

Dimensionality and Reliability Assessment of the Pain Patient Profile Questionnaire

2008 ◽  
Vol 24 (1) ◽  
pp. 22-26 ◽  
Author(s):  
Brian E. McGuire ◽  
Michael J. Hogan ◽  
Todd G. Morrison
Keyword(s):  
Self Report ◽  
Pain Patient ◽  
Fit Indices ◽  
Depression And Anxiety ◽  
Oblique Rotation ◽  
Pain Experience ◽  
Patient Profile ◽  
Adequate Representation ◽  
Pain Patients ◽  
Report Measure

Abstract. Objective: To factor analyze the Pain Patient Profile questionnaire (P3; Tollison & Langley, 1995 ), a self-report measure of emotional distress in respondents with chronic pain. Method: An unweighted least squares factor analysis with oblique rotation was conducted on the P3 scores of 160 pain patients to look for evidence of three distinct factors (i.e., Depression, Anxiety, and Somatization). Results: Fit indices suggested that three distinct factors, accounting for 32.1%, 7.0%, and 5.5% of the shared variance, provided an adequate representation of the data. However, inspection of item groupings revealed that this structure did not map onto the Depression, Anxiety, and Somatization division purportedly represented by the P3. Further, when the analysis was re-run, eliminating items that failed to meet salience criteria, a two-factor solution emerged, with Factor 1 representing a mixture of Depression and Anxiety items and Factor 2 denoting Somatization. Each of these factors correlated significantly with a subsample's assessment of pain intensity. Conclusion: Results were not congruent with the P3's suggested tripartite model of pain experience and indicate that modifications to the scale may be required.

A Test of Three Sadism Measures

2020 ◽  
Vol 41 (4) ◽  
pp. 219-227 ◽  
Author(s):  
Bojana M. Dinić ◽  
Tara Bulut Allred ◽  
Boban Petrović ◽  
Anja Wertag
Keyword(s):  
Item Response ◽  
Social Groups ◽  
Similarity Index ◽  
Dark Triad ◽  
Hierarchical Regression ◽  
Fit Indices ◽  
Profile Similarity ◽  
Positive Attitudes ◽  
Confirmatory Factor ◽  
Positive Correlations

Abstract. The aim of this study was to evaluate psychometric properties of three sadism scales: Short Sadistic Impulse Scale (SSIS), Varieties of Sadistic Tendencies (VAST, which measures direct and vicarious sadism), and Assessment of Sadistic Personality (ASP). Sample included 443 participants (50.1% men) from the general population. Reliability based on internal consistency of all scales was good, and results of Confirmatory Factor Analysis (CFA) showed that all three scales had acceptable fit indices for the proposed structure. Results of Item Response Theory (IRT) analysis showed that all three scales had higher measurement precision (information) in above-average scores. Validity of the scales was supported through moderate to high positive correlations with the Dark Triad traits, especially psychopathy, as well as positive correlations with aggressiveness and negative with Honesty-Humility. Moreover, results of hierarchical regression analysis showed that all three measures of direct, but not vicarious sadism, contributed significantly above and beyond other Dark Triad traits to the prediction of increased positive attitudes toward dangerous social groups. The profile similarity index showed that the SSIS and the ASP were highly overlapping, while vicarious sadism seems distinct from other sadism scales.

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