scholarly journals Testing for Heterogeneous Factor Loadings Using Mixtures of Confirmatory Factor Analysis Models

2010 ◽  
Vol 1 ◽  
Author(s):  
Heather M. Buzick
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Factor Loadings ◽  
Confirmatory Factor ◽  
Analysis Models ◽  
Heterogeneous Factor

A Comparison of Reliability Estimation Based on Confirmatory Factor Analysis and Exploratory Structural Equation Models

2021 ◽  
pp. 001316442110089
Author(s):  
Yuanshu Fu ◽  
Zhonglin Wen ◽  
Yang Wang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Reliability Estimation ◽  
Model Fit ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Composite Reliability

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is often found to be substantially better than that of ICM-CFA. The present study first illustrated the method used to estimate composite reliability under ESEM and then compared the difference between ESEM and ICM-CFA in terms of composite reliability estimation under various indicators per factor, target factor loadings, cross-loadings, and sample sizes. The results showed no apparent difference in using ESEM or ICM-CFA for estimating composite reliability, and the rotation type did not affect the composite reliability estimates generated by ESEM. An empirical example was given as further proof of the results of the simulation studies. Based on the present study, we suggest that if the model fit of ESEM (regardless of the utilized rotation criteria) is acceptable but that of ICM-CFA is not, the composite reliability estimates based on the above two models should be similar. If the target factor loadings are relatively small, researchers should increase the number of indicators per factor or increase the sample size.

Giving G a Meaning: An Application of the Bifactor-(S-1) Approach to Realize a More Symptom-Oriented Modeling of the Beck Depression Inventory–II

Assessment ◽  
2018 ◽  
Vol 27 (7) ◽  
pp. 1429-1447 ◽  
Author(s):  
Manuel Heinrich ◽  
Pavle Zagorscak ◽  
Michael Eid ◽  
Christine Knaevelsrud
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Beck Depression Inventory ◽  
Factor Structure ◽  
Clinical Sample ◽  
First Order ◽  
Confirmatory Factor ◽  
Bifactor Models ◽  
Current Article ◽  
Analysis Models

The Beck Depression Inventory–II is one of the most frequently used scales to assess depressive burden. Despite many psychometric evaluations, its factor structure is still a topic of debate. An increasing number of articles using fully symmetrical bifactor models have been published recently. However, they all produce anomalous results, which lead to psychometric and interpretational difficulties. To avoid anomalous results, the bifactor-(S-1) approach has recently been proposed as alternative for fitting bifactor structures. The current article compares the applicability of fully symmetrical bifactor models and symptom-oriented bifactor-(S-1) and first-order confirmatory factor analysis models in a large clinical sample ( N = 3,279) of adults. The results suggest that bifactor-(S-1) models are preferable when bifactor structures are of interest, since they reduce problematic results observed in fully symmetrical bifactor models and give the G factor an unambiguous meaning. Otherwise, symptom-oriented first-order confirmatory factor analysis models present a reasonable alternative.

scholarly journals Modeling the construct validity of the Berlin Intelligence Structure Model

2015 ◽  
Vol 32 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Heinz-Martin Süß ◽  
André Beauducel
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Construct Validity ◽  
Model Test ◽  
Representative Sample ◽  
Integrative Model ◽  
Structure Model ◽  
Confirmatory Factor ◽  
Analysis Models ◽  
High Degree

The Berlin Intelligence Structure Model is a hierarchical and faceted model which is originally based on an almost representative sample of tasks found in the literature. Therefore, the Berlin Intelligence Structure Model is an integrative model with a high degree of generality. The present paper investigates the construct validity of this model by using different confirmatory factor analysis models. The results show that the model assumptions are supported only in part by the data. Moreover, it is demonstrated that there are different possibilities to incorporate the Berlin Intelligence Structure Model assumptions into confirmatory factor analysis models. The results are discussed with regard to the validity of the Berlin Intelligence Structure Model test, and the validity of the model.

Polish adaptation of the Revised Cheek and Buss Shyness scale and a significance of shyness in the context of personality traits and metatraits

2018 ◽  
Author(s):  
Maria Magdalena Kwiatkowska ◽  
Katarzyna Kwiatkowska ◽  
Radosław Rogoza
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Romantic Relationships ◽  
Personality Traits ◽  
Personality Trait ◽  
Self Report ◽  
The Internet ◽  
Confirmatory Factor ◽  
Polish Adaptation ◽  
Analysis Models

The aim of the current study is to prepare the Polish adaptation of the Revised Cheek and Buss Shyness Scale and to analyze relations between shyness, loneliness and personality traits. The study was conducted via the Internet, and a total of N = 314 adults participated and completed a set of self-report measures. Via comparisons of several confirmatory factor analysis models we demonstrated that the structure of the Polish adaptation of the scale is unidimensional. Consistent with research hypotheses, shyness was mostly correlated with loneliness and introversion; moreover, shyness was strongly negatively related to the personality metatrait of Plasticity and weakly but also negatively with the metatrait of Stability. In addition, individuals not engaged in romantic relationships scored significantly higher in shyness and loneliness, and lower in extraversion, openness, and Plasticity than individuals engaged in unformalized relationships. The results support the conclusion that shyness is a personality trait that significantly affects behavior by limiting the possibilities for adapting to changing environmental conditions.

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