Javascript must be enabled to continue!
Validation of the factor structure of the Experiences Questionnaire using Exploratory Graph Analysis
View through CrossRef
IntroductionDecentering describes the ability to shift the focus away from one’s subjective experience onto the experience itself. The Experiences Questionnaire (EQ) is a self-report measure that was developed to systematically assess changes in Decentering ability. Although several studies show the validity of the questionnaire, there are discrepancies between the factorial structure of the Decentering scale of the EQ (EQ-D) found in the initial study (one factor) and other studies (two factors). Therefore, the current study aimed to assess the dimensionality of the EQ-D using Exploratory Graph Analysis (EGA).MethodsIn total, 1,100 participants were recruited online (790 female, 307 male, 3 non-binary; age 18 to 65 years). Participants completed the EQ and the Rosenberg Self-esteem scale (RSES).ResultsThe bootstrapped EGA results revealed a two-dimensional structure of the EQ-D (Factor 1: Distanced Perspective, DP; Factor 2: Accepting Self-perception, AS) with high structural and item stability (all items > 0.70). The two dimensions of the EQ-D showed a high internal consistency (DP: ω = 0.74; AS: ω = 0.86) and discriminant validity with the rumination items of the EQ. Furthermore, a high convergent validity of the EQ was established, as the AS factor exhibited a significantly stronger correlation with self-esteem than the DP factor (z = 7.98, p < 0.001), which aligns with theoretical considerations suggesting that the AS factor encompasses aspects of self-compassion alongside decentering. We also found measurement invariance of the DP and AS factor across age, gender and country but not for education.DiscussionThese results support the EQ’s validity, demonstrated in a larger sample with a new methodology, aligning with existing two-factor decentering models literature.
Title: Validation of the factor structure of the Experiences Questionnaire using Exploratory Graph Analysis
Description:
IntroductionDecentering describes the ability to shift the focus away from one’s subjective experience onto the experience itself.
The Experiences Questionnaire (EQ) is a self-report measure that was developed to systematically assess changes in Decentering ability.
Although several studies show the validity of the questionnaire, there are discrepancies between the factorial structure of the Decentering scale of the EQ (EQ-D) found in the initial study (one factor) and other studies (two factors).
Therefore, the current study aimed to assess the dimensionality of the EQ-D using Exploratory Graph Analysis (EGA).
MethodsIn total, 1,100 participants were recruited online (790 female, 307 male, 3 non-binary; age 18 to 65 years).
Participants completed the EQ and the Rosenberg Self-esteem scale (RSES).
ResultsThe bootstrapped EGA results revealed a two-dimensional structure of the EQ-D (Factor 1: Distanced Perspective, DP; Factor 2: Accepting Self-perception, AS) with high structural and item stability (all items > 0.
70).
The two dimensions of the EQ-D showed a high internal consistency (DP: ω = 0.
74; AS: ω = 0.
86) and discriminant validity with the rumination items of the EQ.
Furthermore, a high convergent validity of the EQ was established, as the AS factor exhibited a significantly stronger correlation with self-esteem than the DP factor (z = 7.
98, p < 0.
001), which aligns with theoretical considerations suggesting that the AS factor encompasses aspects of self-compassion alongside decentering.
We also found measurement invariance of the DP and AS factor across age, gender and country but not for education.
DiscussionThese results support the EQ’s validity, demonstrated in a larger sample with a new methodology, aligning with existing two-factor decentering models literature.
Related Results
Validation in Doctoral Education: Exploring PhD Students’ Perceptions of Belonging to Scaffold Doctoral Identity Work
Validation in Doctoral Education: Exploring PhD Students’ Perceptions of Belonging to Scaffold Doctoral Identity Work
Aim/Purpose: The aim of this article is to make a case of the role of validation in doctoral education. The purpose is to detail findings from three studies which explore PhD stude...
Bootstrapping a Biodiversity Knowledge Graph
Bootstrapping a Biodiversity Knowledge Graph
The "biodiversity knowledge graph" is a nice metaphor for connecting biodiversity data sources, but can we actually build it? Do we have sufficient linked data available? Given tha...
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract
Accurately predicting drug sensitivity and understanding what is driving it are major challenges in drug discovery. Graphs are a natural framework for captu...
Domination of Polynomial with Application
Domination of Polynomial with Application
In this paper, .We .initiate the study of domination. polynomial , consider G=(V,E) be a simple, finite, and directed graph without. isolated. vertex .We present a study of the Ira...
E-Cordial Labeling of Some Families of Graphs
E-Cordial Labeling of Some Families of Graphs
An E-cordial labeling σ: E →{0,1} induces σ∗: V →{0,1} on graph G=(V,E), where (σ(v)=(∑_(u∈V)▒〖σ(uv)〗) mod 2 is taken over all edges uv∈E, and the labelling satisfies the condition...
Data Analytics on Graphs Part I: Graphs and Spectra on Graphs
Data Analytics on Graphs Part I: Graphs and Spectra on Graphs
The area of Data Analytics on graphs promises a paradigm shift, as we approach information processing of new classes of data which are typically acquired on irregular but structure...
A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction
A Joint-Learning-Based Dynamic Graph Learning Framework for Structured Prediction
Graph neural networks (GNNs) have achieved remarkable success in structured prediction, owing to the GNNs’ powerful ability in learning expressive graph representations. However, m...
FAKTORISASI PADA GRAF REGULER
FAKTORISASI PADA GRAF REGULER
This research aims to: (1) know the criteria of a graph that has a -factor, (2) know the conditions of a regular graph that has a 1-factorization , (3) know the conditions of a reg...

