2021
DOI: 10.47626/2237-6089-2020-0058
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Psychometric properties and factor structure of the Repetitive Thinking Questionnaire: Persian versions of the RTQ-31 and RTQ-10

Abstract: Introduction:Repetitive thinking as a transdiagnostic factor plays an essential role in the development and maintenance of emotional disorders. Two versions of the Repetitive Thinking Questionnaire (RTQ-31 and RTQ-10) are the best-known measures used for assessing repetitive thinking in clinical and nonclinical samples. The present study was conducted to evaluate the psychometric properties and factor structure of Persian versions of them. Methods: Participants were 592 students assessed with the RTQ-31, the R… Show more

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Cited by 5 publications
(4 citation statements)
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“…The RTQ-10 is rated on a 5-point scale, where 1 represents "not at all true", 3 indicates "somewhat true", and 5 re ects "very true". The Persian version of the RTQ-10 was reported as having high internal consistency (α = .77; Hasani et al, 2021). The RTQ-10 also had adequate internal consistency (α = .89) in the present study.…”
Section: Depression Anxiety Stress Scale (Dass-21supporting
confidence: 58%
“…The RTQ-10 is rated on a 5-point scale, where 1 represents "not at all true", 3 indicates "somewhat true", and 5 re ects "very true". The Persian version of the RTQ-10 was reported as having high internal consistency (α = .77; Hasani et al, 2021). The RTQ-10 also had adequate internal consistency (α = .89) in the present study.…”
Section: Depression Anxiety Stress Scale (Dass-21supporting
confidence: 58%
“…The repetitive thinking questionnaire (RTQ-10) ( 62 ) is a unidimensional scale for measuring repetitive thinking. The RTQ-10 is rated on a 5-point scale, where 1 represents “ not at all true ,” 3 indicates “ somewhat true ,” and 5 reflects “ very true .” The Persian version of the RTQ-10 was reported as having high internal consistency [α = 0.77; ( 63 )]. The RTQ-10 also had adequate internal consistency ( α = 0.89) in the present study.…”
Section: Methodsmentioning
confidence: 99%
“…To perform back-propagation, the gradient of the loss function with respect to all parameters θ must be computed, which is done using the adjoint sensitivity method [27]. Building off of the NeuralODE author's model implementation in PyTorch [28], we made biologically motivated modifications to the architecture and incorporated userdefined prior domain knowledge, as described below.…”
Section: Neural Ordinary Differential Equations (Neuralodes)mentioning
confidence: 99%
“…We developed PHOENIX (Prior-informed Hill-like ODEs to Enhance Neuralnet Integrals with eXplainability) as a scalable method for estimating dynamical systems governing gene expression through an ODE-based machine learning framework that is flexible enough to avoid model misspecification and is guided by insights from systems biology that facilitate biological interpretation of the resulting models [25,26]. At its core, PHOENIX models temporal patterns of gene expression using neural ordinary differential equations (NeuralODEs) [27,28], an advanced computational method commensurate with the scope of human gene regulatory networks-with more than 25,000 genes and 1600 TFs-and a limited number of samples. We implement an innovative NeuralODE architecture that inherits the universal function approximation property (and thus the flexibility) of neural networks while resembling Hill-Langmuir kinetics, which have been used to model dynamic transcription factor binding site occupancy [10,29,30].…”
mentioning
confidence: 99%