Bifactor Indices Calculator

Dueber, David

doi:10.13023/edp.tool.01

Cited by 78 publications

(36 citation statements)

References 0 publications

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“…Given the lack of consensus about rules of thumb for model fit indices (Marsh et al, 2004) and complexities with interpreting model fit indices in factor analyses of personality measures (Hopwood & Donnellan, 2010), we report exact values for all fit indices. We also report (see online supplemental materials, ) several additional statistical indices for the bifactor models recommended by Rodriguez et al (2015; see also Dueber, 2017), including omega hierarchical/omega hierarchical subscale, explained common variance (ECV), percent of uncontaminated correlations (PUC), item common variance attributed to a general dimension (I-ECV), a measure of construct reliability (i.e., H), and a measure of factor score determinacy (i.e., FD). In addition, we examined the congruence between the item-level factor loadings reported by Snyder et al (2015) with those observed in the present study; that is, when rank-ordered by magnitude of the factor loading for each temperament domain (i.e., EC, NE, PE), how similar is the order of items between Snyder et al (2015) and the present study.…”

Section: Methodsmentioning

confidence: 99%

The structure of adolescent temperament and associations with psychological functioning: A replication and extension of Snyder et al. (2015).

Lawson¹,

Atherton²,

Robins³

2021

Journal of Personality and Social Psychology

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The present study attempts to replicate and extend Snyder et al. (2015, JPSP). The original study examined the latent factor structure of the EATQ-R, a commonly used measure of adolescent temperament, and then showed that the resulting latent factors (i.e., Effortful Control, Negative Emotionality, and Positive Emotionality) had theoretically meaningful concurrent associations with several measures of adolescent functioning (depression, anxiety, ADHD, relational aggression, and school performance and behavior). We performed these same analyses using data from a large sample of Mexican-origin youth (N=674), and also examined prospective associations between the three EATQ-R factors and measures of adolescent functioning assessed two years later. We found some evidence supporting the bifactor model fit reported in the original study but poor replication of the correlations among latent factors. Additionally, model comparisons demonstrated that correlated factors models produced more interpretable factors than the bifactor models. In contrast, we replicated most of the concurrent correlations (and extended the findings to prospective associations) between the EATQ-R factors and measures of adolescent functioning, supporting the construct validity of the EATQ-R as a measure of adolescent temperament. Thus, these findings raise concerns about the generalizability of the factor structure identified by Snyder et al. (2015), but bolster claims about the generalizability of the concurrent and predictive validity of the EATQ-R. Overall, differences between the present findings and those of Snyder et al. highlight the importance of ongoing construct validation in youth temperament research, especially with participants from groups traditionally underrepresented in psychological research.

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Section: Methodsmentioning

confidence: 99%

The structure of adolescent temperament and associations with psychological functioning: A replication and extension of Snyder et al. (2015).

Lawson¹,

Atherton²,

Robins³

2021

Journal of Personality and Social Psychology

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“…Given that the fit of the bifactor model was adequate in regard to RMSEA and SRMR and only marginally inadequate in regard to CFI, the standardized factor loadings for both the unidimensional and bifactor models were then entered into Dueber’s (2017) model-based dimensionality and reliability calculator. Table 2 summarizes the results.…”

Section: Resultsmentioning

confidence: 99%

“…We first estimated unidimensional, common factors, bifactor, and hierarchical models of the TAS-20. Second, following the recommendations of Reise, Bonifay, et al (2013), we calculated model-based reliability and omega coefficients to assess the TAS-20’s dimensionality, which required entering the standardized factor loadings for both the unidimensional and bifactor models into a model-based reliability calculator developed by Dueber (2017). For the CFAs and the testing of Hypotheses 1–6, we used M plus v.8.3 (Muthén & Muthén, 1998–2015) SEM software.…”

Section: Methodsmentioning

confidence: 99%

Variance composition, reliability, validity, and measurement invariance of the Toronto Alexithymia Scale-20 in nonclinical samples.

Levant¹,

Parent²,

Shu³

et al. 2022

Psychology of Men & Masculinities

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The present study extended prior work on the Toronto Alexithymia Scale-20 (TAS-20). The TAS-20 total scale score is commonly used in research in the psychology of men and masculinities and in clinical practice, but most published confirmatory factor analyses (CFA’s) do not support this use. Using a sample of 913 men and women, variance composition was assessed, and model-based dimensionality and reliability coefficients were calculated, finding evidence for unidimensionality, which supports the use of the total scale raw score but not that of the subscales, although the model fit was less than desired. Convergent and concurrent evidence was found for the validity of the TAS-20 in men as a unidimensional scale (N = 505) by examining relationships with latent variables of several constructs in the nomological network. An assessment of measurement invariance by gender (men, women) found evidence for metric invariance, indicating that cisgender men and women understand the scale in the same way. It is recommended that the TAS-20 scale developers follow recommended procedures to trim composite measurement scales to improve the psychometric properties (i.e., model fit) of the TAS.

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“…Furthermore, a root mean square error of approximation (RMSEA) < .06 and a standardized root mean square residual (SRMR) < .08 indicate acceptable model fit (Hu & Bentler, 1999). McDonald’s omega and omega hierarchical were calculated using a freely available bifactor indices Excel calculator (Dueber, 2017). As the test sample size exceeded those recommended for confirmatory factor analyses (Comrey & Lee, 1992), the sample was deemed sufficiently powered and formal sample size calculations were not conducted.…”

Section: Methodsmentioning

confidence: 99%

Measuring Burnout in Social Work

Doherty

Mallett

McFadden

2021

European Journal of Psychological Assessment

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Abstract. Several studies challenge the three-dimensional structure of the Maslach Burnout Inventory – Human Services Survey (MBI-HSS), citing alternative measurement models including bifactor models. While bifactor models have merit, if data sampling violates assumptions of Stochastic Measurement Theory (SMT) the bifactor model requires modification prior to application. The present study compared five alternative MBI-HSS factor models using both Confirmatory Factor Analysis (CFA) and Exploratory Structural Equation Modeling (ESEM). Data from a cross-sectional survey of United Kingdom (UK) social workers were examined ( N = 1257), with validation analyses conducted in an independent sample ( N = 162). Bifactor models, re-specified to account for SMT, provided good fit. However, improved fit was observed for a bifactor-ESEM specification, in both test (χ2 = 1,112.93, df = 149, p < .001, CFI = .969, RMSEA = .072, 90% CI [.068, .076]) and validation (χ2 = 227.89, df = 149, p < .001, CFI = .978, RMSEA = .057, 90% CI [.042, .072]) samples. The results confirm the MBI-HSS possesses a bifactor structure in UK social workers when SMT is considered, and that bifactor-ESEM may provide a better framework to examine MBI-HSS.

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Bifactor Indices Calculator

Cited by 78 publications

References 0 publications

The structure of adolescent temperament and associations with psychological functioning: A replication and extension of Snyder et al. (2015).

The structure of adolescent temperament and associations with psychological functioning: A replication and extension of Snyder et al. (2015).

Variance composition, reliability, validity, and measurement invariance of the Toronto Alexithymia Scale-20 in nonclinical samples.

Measuring Burnout in Social Work

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