Advances in Latent Variable Measurement Modeling

Coulacoglou, Carina; Saklofske, Donald H.

doi:10.1016/b978-0-12-802219-1.00004-3

Cited by 7 publications

(9 citation statements)

References 167 publications

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“…Bi-factor models consider the answer to each item to be determined by two information components simultaneously (with2in-item-multidimensionality; WIM; [20,21]). Regarding the construct HRQoL, each item has to be assigned to a general (i.e.…”

Section: Bi-factor Models Of the Sf-8 Structurementioning

confidence: 99%

Confirmatory and bi-factor analysis of the Short Form Health Survey 8 (SF-8) scale structure in a German general population sample

Wirtz

Schulz

Brähler

2021

Health Qual Life Outcomes

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Background The SF-8 is a short form of the SF-36 Health Survey, which is used for generic assessment of physical and mental aspects of health-related quality of life (HRQoL). Each of the 8 dimensions of the SF-36 is covered by a single item in the SF-8. The aim of the study was to examine the latent model structure of the SF-8. Method One-, two- and three dimensional as well as bi-factor structural models were defined and estimated adopting the ML- as well as the WLSMV-algorithm for ordinal data. The data were collected in a German general population sample (N = 2545 persons). Results A two- (physical and mental health) and a three-dimensional CFA structure (in addition overall health) represent the empirical data information adequately [CFI = .987/.995; SRMR = .024/.014]. If a general factor is added, the resulting bi-factor models provide a further improvement in data fit [CFI = .999/.998; SRMR = .001]. The individual items are much more highly associated with the general HRQoL factor (loadings: .698 to .908) than with the factors physical, mental, and overall health (loadings: −.206 to .566). Conclusions In the SF-8, each item reflects mainly general HRQoL (general factor) as well as one of the three components physical, mental, and overall health. The findings suggest in particular that the evaluation of the information of the SF-8 items can be validly supplemented by a general value HRQoL.

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Section: Bi-factor Models Of the Sf-8 Structurementioning

confidence: 99%

Confirmatory and bi-factor analysis of the Short Form Health Survey 8 (SF-8) scale structure in a German general population sample

Wirtz

Schulz

Brähler

2021

Health Qual Life Outcomes

View full text Add to dashboard Cite

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“…(1) In EFA, no explicit theory-driven hypotheses are formulated and tested. Therefore, the interpretation of the derived factors are post hoc and may vary across studies (Coulacoglou & Saklofske, 2017; Harrington, 2009). In contrast, CFA is theory-driven, and factors are defined a priori.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we hold that the data-driven nature of EFA is the main reason why results from EFA do not converge on the number and theoretical explanations of latent factors. Consequently, the present study will apply CFA and SEM for investigating the homogeneity and structure of the HGSHS:A (Coulacoglou & Saklofske, 2017; Harrington, 2009) as a canonical example of hypnotic-suggestibility scales. (2) Even though EFAs have shown that variance in the items of the HGSHS:A must be explained by more than one factor, it is unclear whether a model, which, in addition to specific grouping factors, also assumes a general factor (G-factor) of hypnotizability, will explain the data better than a simple multifactor model.…”

Section: Introductionmentioning

confidence: 99%

“…(1) In EFA, no explicit theory-driven hypotheses are formulated and tested. Therefore, the interpretations of the derived factors are post hoc and may vary across studies (Coulacoglou & Saklofske, 2017; Harrington, 2009). That means, in EFA, the analysis starts by selecting the number of latent factors; which indicator loads on which factor is then determined by using reduced correlational matrices.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we hold that the data-driven nature of EFA is the main reason why results from EFA do not converge on the number and theoretical explanations of latent factors. Consequently, the present study will apply CFA and SEM (Coulacoglou & Saklofske, 2017; Harrington, 2009) for investigating the homogeneity and structure of HGSHS:A as a canonical example of hypnotic-suggestibility scales.…”

Section: Introductionmentioning

confidence: 99%

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Can Hypnotic Susceptibility be Explained by Bifactor Models? Structural Equation Modeling of the Harvard Group Scale of Hypnotic Susceptibility - Form A

Zahedi

Sommer

2021

Preprint

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Individuals differ in their responsiveness to (post-)hypnotic suggestions. However, defining and measuring hypnotizability is contentious because standardized scales, such as the Harvard group scale (HGSHS:A), measure a mixture of general-suggestibility and its increase due to hypnotic induction (hypnotic-suggestibility). Exploratory factor analysis (FA) of standardized scales found them to be heterogeneous; however, the number and nature of latent factors are debated. We applied Confirmatory FA to HGSHS:A scores of 477 volunteers and tested several theory-driven models. Scores were best explained by a bifactor model consisting of a G-factor, tapping into hypnotizability, and three grouping factors, measuring specific suggestibilities, each requiring a unique combination of three top-down cognitive functions: cognitive-simulation, sensory-adaptation, and problem-solving. Structural equation modeling revealed that the simulation-adaptation factor (requiring cognitive-simulation and sensory-adaptation), predicts the other suggestibility factors. These results demonstrate the multifaceted structure of hypnotic-suggestibility and underscore the desideratum for developing a more differentiated scale, focusing on simulation-adaption suggestions.

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Evaluation of farmers’ adaptation capacity indicators with declining groundwater levels in Kazerun plain, Iran

Ghorbani

Ganji

Khosravi

et al. 2023

Sustain. Water Resour. Manag.

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Advances in Latent Variable Measurement Modeling

Cited by 7 publications

References 167 publications

Confirmatory and bi-factor analysis of the Short Form Health Survey 8 (SF-8) scale structure in a German general population sample

Confirmatory and bi-factor analysis of the Short Form Health Survey 8 (SF-8) scale structure in a German general population sample

Can Hypnotic Susceptibility be Explained by Bifactor Models? Structural Equation Modeling of the Harvard Group Scale of Hypnotic Susceptibility - Form A

Evaluation of farmers’ adaptation capacity indicators with declining groundwater levels in Kazerun plain, Iran

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