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

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.

2017 ◽  
Vol 121 (3) ◽  
pp. 548-565 ◽  
Author(s):  
Rina S. Fox ◽  
Teresa A. Lillis ◽  
James Gerhart ◽  
Michael Hoerger ◽  
Paul Duberstein

The DASS-21 is a public domain instrument that is commonly used to evaluate depression and anxiety in psychiatric and community populations; however, the factor structure of the measure has not previously been examined in oncologic settings. Given that the psychometric properties of measures of distress may be compromised in the context of symptoms related to cancer and its treatment, the present study evaluated the psychometric properties of the DASS-21 Depression and Anxiety scales in cancer patients ( n = 376) as compared to noncancer control participants ( n = 207). Cancer patients ranged in age from 21 to 84 years (mean = 58.3, standard deviation = 10.4) and noncancer control participants ranged in age from 18 to 81 years (mean = 45.0, standard deviation = 11.7). Multiple group confirmatory factor analysis supported the structural invariance of the DASS-21 Depression and Anxiety scales across groups; the factor variance/covariance invariance model was the best fit to the data. Cronbach’s coefficient alpha values demonstrated acceptable internal consistency reliability across the total sample as well as within subgroups of cancer patients and noncancer control participants. Expected relationships of DASS-21 Depression and Anxiety scale scores to measures of suicidal ideation, quality of life, self-rated health, and depressed mood supported construct validity. These results support the psychometric properties of the DASS-21 Depression and Anxiety scales when measuring psychological distress in cancer patients.


PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181908 ◽  
Author(s):  
Mia Scheffers ◽  
Marijtje A. J. van Duijn ◽  
Ruud J. Bosscher ◽  
Durk Wiersma ◽  
Robert A. Schoevers ◽  
...  

Author(s):  
Brian D. Haig

Chapter 6 argues that exploratory factor analysis is an abductive method of theory generation that exploits a principle of scientific inference known as the principle of the common cause. Factor analysis is an important family of multivariate statistical methods that is widely used in the behavioral and social sciences. The best known model of factor analysis is common factor analysis, which has two types: exploratory factor analysis and confirmatory factor analysis. A number of methodological issues that arise in critical discussions of exploratory factor analysis are considered. It is suggested that exploratory factor analysis can be profitably employed in tandem with confirmatory factor analysis.


2017 ◽  
Vol 78 (4) ◽  
pp. 537-568 ◽  
Author(s):  
Huub Hoofs ◽  
Rens van de Schoot ◽  
Nicole W. H. Jansen ◽  
IJmert Kant

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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