A Comparison of CFA, ESEM, and BSEM in Test Structure Analysis

Xiao, Yue; Liu, Hongyun; Hau, Kit‐Tai

doi:10.1080/10705511.2018.1562928

Cited by 63 publications

(72 citation statements)

References 37 publications

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“…The general fit of the model was assessed following the cut-off point recommendations proposed by Schreiber (e.g., CFI>.95, TLI>.95, and RMSEA < .06) [50]. Later, to make a more brief and optimized scale, a revised version was established by, removing items on the basis of three criteria: (1) retention of strong factorial loadings (λ > .5) [51], (2) removal of redundant items [52], and (3) removal of items with strong cross-loadings (>.3) [53,54]. Additionally, based on the debugged structure, four models were tested: models with two covariate factors using ESEM-GEOMIN (M2a) and CFA (M2b) approaches; one-factor CFA (M3); second-order CFA (M4) with first-order factor variance fixed to 1; and bi-factor CFA (M5).…”

Section: Discussionmentioning

confidence: 99%

Development and evidence of validity of the HIV risk perception scale for young adults in a Hispanic-American context

et al. 2020

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HIV/AIDS is a public health problem that is transmitted through risky sexual behavior. The literature suggests that the perception of HIV risk is a motivator for the prevention of risky sexual behaviors. There is no culturally adapted scale to assess HIV risk perception in the Hispanic-American population. The aim of this research was to develop a scale to assess HIV risk perception in Hispanic-American young adults. A cross-sectional instrumental design was used, with a sample of students from the Chilean city with the highest HIV rates. Participants (n = 524) were between 18 and 33 years old, of whom 51% were women, 84.4% said they were heterosexual and 43.7% said they had not been tested for HIV/AIDS. The final scale has 9 items and 2 dimensions: (1) perceived risk susceptibility and (2) perceived risk severity. The results showed that the identified structure provided adequate levels of reliability (ω > .8) and presented evidence of validity, based on the internal structure of the test (i.e., using ESEM) and on the relationship with other variables (i.e., the sexual risk behaviors scale). In addition, the results showed strong invariance between the scores for men and women. It is concluded that the HIV risk perception scale has adequate psychometric properties to assess HIV risk perception in equivalent samples.

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Section: Discussionmentioning

confidence: 99%

Development and evidence of validity of the HIV risk perception scale for young adults in a Hispanic-American context

et al. 2020

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“…The possibility to overcome the limitations of the ICM by means of ESEM and BSEM has also been investigated by Xiao, Liu, and Hau series of model modifications may capitalize on chance (e.g., MacCallum, Roznowski, & Necowitz, 1992). Overall, population loadings can more precisely be estimated by means of ESEM than by means of ICM, but ESEM depends on the method of factor rotation and even on the specification of parameters of the rotation method (Xiao et al, 2019;. In consequence, ESEM might be used as an alternative to ICM in several research contexts, but it is not a general alternative for those, who want to investigate specific hypotheses on loading patterns or intended measurement models.…”

Section: Introductionmentioning

confidence: 99%

“…Even when researchers should not use small-variance priors mechanically (MacCallum, Edwards, & Cai, 2012) and even when a sensitivity analysis for priors has been recommended (Depaoli & van de Schoot, 2017), it is notable that the use of correct zero priors with small variance instead of exact zero loadings in BSEM resulted in loading estimates that were close to the population loadings (Xiao et al, 2019). This is in line with previous results indicating that BSEM might be an interesting alternative to ICM (Muthé n & Asparouhov, 2012; Reis, 2017) when hypotheses on loading patterns are investigated.…”

Section: Introductionmentioning

confidence: 99%

“…Although previous simulation studies already provided important results on the ICM, ESEM, and BAYES (Xiao et al, 2019), the BSS has previously not been systematically compared to BAYES. Moreover, previous simulation studies on the BSS were based on a more complicated procedure using salient loading weights and on only three common factors (Beauducel, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Overcoming Limitations of the Independent Clusters Model for CFA by Means of Bayes-Estimation and Buffered Simple Structure

Beauducel¹,

Hilger²

2020

IJSP

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The model of buffered simple structure is discussed as a method for modeling cross-loadings in confirmatory factor analysis. This method introduces assumptions from item sampling theory into confirmatory factor analysis. The independent clusters model, buffered simple structure, and Bayes estimation were compared by means of a simulation study based on three different population types. Population type A had zero cross-loadings, population type B had symmetrically distributed nonzero cross-loadings, and population type C had asymmetrically distributed nonzero cross-loadings. It turned out for population A that, although the independent clusters model yields the best loading estimate, it did not outperform Bayes estimation and buffered simple structure with respect to the factor inter-correlation estimate and model fit. One reason for this unexpected result could be that the specification of zero-cross loadings is suboptimal even when only sampling error introduces some cross-loadings. For populations B and C Bayes estimation and buffered simple structure clearly outperformed the independent clusters model. Overall, the results indicate that depending on the structure of cross-loadings in the population and depending on the focus on loading estimates or factor inter-correlation estimates, different modeling approaches might be appropriate.

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“…La invariancia se estimó en cuatro niveles progresivos(Millsap, 2011): configuración (número equivalente y disposición de factores); métrico (factor de carga equivalente y modelo de ESEM de carga cruzada); escalar (equivalencia umbral de síntomas) y estricto (equivalencia de error de medición de síntomas). Además, se investigó la invariancia de la media latente para cada grupo y se estimó la magnitud y el significado de las diferencias de la media latente para cada factor(Xiao, Liu y Hau, 2019). Finalmente, se calculó la varianza común esperada (ECV) y el Coeficiente Omega Jerárquico (ωh).…”

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Multidimensional Evaluation of the General and Specific Factors of Adhd in Children Population With the Bifactor-Esem Approach

2019

RACP

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A Comparison of CFA, ESEM, and BSEM in Test Structure Analysis

Cited by 63 publications

References 37 publications

Development and evidence of validity of the HIV risk perception scale for young adults in a Hispanic-American context

Development and evidence of validity of the HIV risk perception scale for young adults in a Hispanic-American context

Overcoming Limitations of the Independent Clusters Model for CFA by Means of Bayes-Estimation and Buffered Simple Structure

Multidimensional Evaluation of the General and Specific Factors of Adhd in Children Population With the Bifactor-Esem Approach

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