The Performance of the Polychoric Correlation Coefficient and Selected Fitting Functions in Confirmatory Factor Analysis with Ordinal Data

1991 ◽  
Vol 28 (4) ◽  
pp. 491-497 ◽  
Author(s):  
Edward E. Rigdon ◽  
Carl E. Ferguson
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Correlation Coefficient ◽  
Simulation Study ◽  
Ordinal Data ◽  
Standard Errors ◽  
Polychoric Correlation ◽  
Chi Square ◽  
Confirmatory Factor

In a simulation study, no combination of the polychoric correlation coefficient with any LISREL 7 fitting function produced unbiased estimated standard errors or a correctly distributed chi square statistic. However, there were major differences in the performance of the five fitting functions in the analysis of ordinal data.

The Empirical Verification of an Assignment of Items to Subtests

2008 ◽  
Vol 68 (6) ◽  
pp. 923-939 ◽  
Author(s):  
Ilse Stuive ◽  
Henk A. L. Kiers ◽  
Marieke E. Timmerman ◽  
Jos M. F. ten Berge
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Simulation Study ◽  
Common Factor ◽  
Model Error ◽  
Unique Variance ◽  
Empirical Verification ◽  
Multiple Group ◽  
Analysis Methods ◽  
Confirmatory Factor

This study compares two confirmatory factor analysis methods on their ability to verify whether correct assignments of items to subtests are supported by the data. The confirmatory common factor (CCF) method is used most often and defines nonzero loadings so that they correspond to the assignment of items to subtests. Another method is the oblique multiple group (OMG) method, which defines subtests as unweighted sums of the scores on all items assigned to the subtest, and (corrected) correlations are used to verify the assignment. A simulation study compares both methods, accounting for the influence of model error and the amount of unique variance. The CCF and OMG methods show similar behavior with relatively small amounts of unique variance and low interfactor correlations. However, at high amounts of unique variance and high interfactor correlations, the CCF detected correct assignments more often, whereas the OMG was better at detecting incorrect assignments.

scholarly journals The Sports Mental Toughness Questionnaire (SMTQ): A psychometric evaluation of the Turkish version

2017 ◽  
Vol 11 (2) ◽  
pp. 90-98 ◽  
Author(s):  
Bulent Okan Miçooğulları
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Degrees Of Freedom ◽  
Reliability And Validity ◽  
Psychometric Evaluation ◽  
Mental Toughness ◽  
Chi Square ◽  
Analysis Technique ◽  
Confirmatory Factor ◽  
Fit Index

The objective of this study was to adapt the Sports Mental Toughness Questionnaire (SMTQ) for use in Turkey, and to test its reliability and validity. With a sample of 184 males (mean ± s: age 24.22 ± 3.01 years) and 153 females (mean ± s: age 21.54 ± 3.82 years) total 337 athletes (mean ± s: age 21.76 ± 4.2 years) drawn from 20 sport classifications, confirmatory factor analysis technique to evaluate the psychometric properties of the SMTQ. Athletes completed 14 item SMTQ was applied to all volunteered participants. Afterwards Confirmatory Factor Analysis was conducted by Analysis Moments of Structures 18. Comparative fit index, non-normed fit index and root mean square error of approximation were used to check if the model fit the data. Chi-square/degrees of freedom ratio was found as (χ2/df) 1.46. The other parameters were determined as RMSEA= .74, NNFI= .90, and CFI= .90. The confirmatory factor analysis results supported the three-factor structure and indicated proper models should include correlations among the three factors. Internal consistency estimates ranged from .69 to .78 and were consistent with values reported by previous studies. Based on these findings, “Sports Mental Toughness Questionnaire” was found to be a valid and reliable instrument.

scholarly journals Physician Mental Workload Scale in China: Development and Psychometric Evaluation

BMJ Open ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. e030137 ◽  
Author(s):  
Chuntao Lu ◽  
Yinhuan Hu ◽  
Qiang Fu ◽  
Samuel Governor ◽  
Liuming Wang ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Correlation Coefficient ◽  
Content Validity ◽  
Mental Workload ◽  
Reliability And Validity ◽  
Correlation Coefficients ◽  
Public Hospitals ◽  
Validity Index ◽  
Confirmatory Factor

ObjectiveThe purpose of our study is to develop a mental workload scale for physicians in China and assess the scale’s reliability and validity.DesignThe instrument was developed over three phases involving 396 physicians from different tiers of comprehensive public hospitals in China. In the first phase, an initial item pool was developed through a systematic literature review. The second phase consisted of two rounds of Delphi expert consultations and a pilot survey. The third phase tested the reliability and validity of the instrument.SettingPublic hospitals in China.ParticipantsA total of 396 physicians from different tiers of comprehensive public hospitals in China participated in this study in 2018.Primary and secondary outcome measuresCronbach’s α, content validity index, item-total score correlation coefficient, dimension-total score correlation coefficient and indices of confirmatory factor analysis.ResultsSix dimensions (mental demands, physical demands, temporal demands, perceived risk, frustration level and performance) and 12 items were identified in the instrument. For reliability, Cronbach’s α for the whole scale was 0.81. For validity, the corrected item-content validity index of each item ranged from 0.85 to 1, item-total score correlation coefficients ranged from 0.31 to 0.75, and the correlation coefficients between the dimensions and total score ranged from 0.37 to 0.72. The results of the confirmatory factor analysis showed that the goodness-of-fit indices of the scale were satisfactory.ConclusionThe instrument showed good reliability and validity, and it is useful for diagnosing the mental workload of physicians.

Determinants of Standard Errors of MLEs in Confirmatory Factor Analysis

Psychometrika ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. 633-648 ◽  
Author(s):  
Ke-Hai Yuan ◽  
Ying Cheng ◽  
Wei Zhang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Standard Errors ◽  
Confirmatory Factor

Psychometric Properties of the Persian Version of Pregnancy Symptoms Inventory (PSI)

2021 ◽  
pp. 105477382110673
Author(s):  
Solmaz Ghanbari-Homaie ◽  
Mohammad Asghari Jafarabadi ◽  
Sonia Hasani ◽  
Mojgan Mirghafourvand
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Pregnant Women ◽  
Psychometric Properties ◽  
Correlation Coefficient ◽  
Exploratory Factor Analysis ◽  
Intra Class Correlation ◽  
Persian Version ◽  
Confirmatory Factor ◽  
Intra Class Correlation Coefficient

The aim of this study was to determine the psychometric properties of the Persian version of pregnancy symptoms inventory. A methodological study. This study was conducted on 220 pregnant women. Construct validity was measured by exploratory factor analysis and confirmatory factor analysis. Reliability was measured by intra-class correlation coefficient and internal consistency. Since the items 12 (snoring) and 16 (thrush) failed to obtain the minimum principal axis factoring in exploratory factor analysis, they were removed from the Persian version. Confirmatory factor analysis showed a good fit for the extracted model. Cronbach’s alpha was .94 for the frequency items and .95 for the limitation items. Intra-class correlation coefficient was between .58 and 1 for frequency items and between .73 and 1 for limitation items. The Persian version of pregnancy symptoms inventory was a valid and reliable scale to be used for Iranian pregnant women.

scholarly journals Evaluating Model Fit in Bayesian Confirmatory Factor Analysis With Large Samples: Simulation Study Introducing the BRMSEA

2017 ◽  
Vol 78 (4) ◽  
pp. 537-568 ◽  
Author(s):  
Huub Hoofs ◽  
Rens van de Schoot ◽  
Nicole W. H. Jansen ◽  
IJmert Kant
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Sample Size ◽  
Simulation Study ◽  
Reliability And Validity ◽  
Model Fit ◽  
Model Complexity ◽  
Large Sample ◽  
Large Samples ◽  
Confirmatory Factor

Bayesian confirmatory factor analysis (CFA) offers an alternative to frequentist CFA based on, for example, maximum likelihood estimation for the assessment of reliability and validity of educational and psychological measures. For increasing sample sizes, however, the applicability of current fit statistics evaluating model fit within Bayesian CFA is limited. We propose, therefore, a Bayesian variant of the root mean square error of approximation (RMSEA), the BRMSEA. A simulation study was performed with variations in model misspecification, factor loading magnitude, number of indicators, number of factors, and sample size. This showed that the 90% posterior probability interval of the BRMSEA is valid for evaluating model fit in large samples ( N≥ 1,000), using cutoff values for the lower (<.05) and upper limit (<.08) as guideline. An empirical illustration further shows the advantage of the BRMSEA in large sample Bayesian CFA models. In conclusion, it can be stated that the BRMSEA is well suited to evaluate model fit in large sample Bayesian CFA models by taking sample size and model complexity into account.

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