The Experts below are selected from a list of 4146 Experts worldwide ranked by ideXlab platform
Nicola Doering - One of the best experts on this subject based on the ideXlab platform.
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Measuring co-presence and social presence in virtual environments – psychometric construction of a German scale for a fear of public speaking scenario
Studies in health technology and informatics, 2016Co-Authors: Sandra Poeschl, Nicola DoeringAbstract:Virtual reality exposure therapy (VRET) applications use high levels of fidelity in order to produce high levels of presence and thereby elicit an emotional response for the user (like fear for phobia treatment). State of research shows mixed results for the correlation between anxiety and presence in virtual reality exposure, with differing results depending on specific anxiety disorders. A positive correlation for anxiety and presence for social anxiety disorder is not proven up to now. One reason might be that plausibility of the simulation, namely including key triggers for social anxiety (for example verbal and non-verbal behavior of virtual agents that reflects potentially negative human evaluation) might not be acknowledged in current presence questionnaires. A German scale for measuring co-presence and social presence for virtual reality (VR) fear of public speaking scenarios was developed based on a translation and adaption of existing co- presence and social presence questionnaires. A sample of N = 151 students rated co-presence and social presence after using a fear of public speaking application. Four correlated factors were derived by item- and principle axis factor analysis (Promax Rotation), representing the presenter’s reaction to virtual agents, the reactions of the virtual agents as perceived by the presenter, impression of interaction possibilities, and (co-)presence of other people in the virtual environment. The scale developed can be used as a starting point for future research and test construction for VR applications with a social context.
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Measuring co-presence and social presence in virtual environments - Psychometric construction of a german scale for a fear of public speaking scenario
Annual Review of CyberTherapy and Telemedicine, 2015Co-Authors: Sandra Poeschl, Nicola DoeringAbstract:Virtual reality exposure therapy (VRET) applications use high levels of fidelity in order to produce high levels of presence and thereby elicit an emotional response for the user (like fear for phobia treatment). State of research shows mixed results for the correlation between anxiety and presence in virtual reality exposure, with differing results depending on specific anxiety disorders. A positive correlation for anxiety and presence for social anxiety disorder is not proven up to now. One reason might be that plausibility of the simulation, namely including key triggers for social anxiety (for example verbal and non-verbal behavior of virtual agents that reflects potentially negative human evaluation) might not be acknowledged in current presence questionnaires. A German scale for measuring co-presence and social presence for virtual reality (VR) fear of public speaking scenarios was developed based on a translation and adaption of existing co-presence and social presence questionnaires. A sample of N = 151 students rated co-presence and social presence after using a fear of public speaking application. Four correlated factors were derived by item- and principle axis factor analysis (Promax Rotation), representing the presenter's reaction to virtual agents, the reactions of the virtual agents as perceived by the presenter, impression of interaction possibilities, and (co-)presence of other people in the virtual environment. The scale developed can be used as a starting point for future research and test construction for VR applications with a social context.
Sandra Poeschl - One of the best experts on this subject based on the ideXlab platform.
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Measuring co-presence and social presence in virtual environments – psychometric construction of a German scale for a fear of public speaking scenario
Studies in health technology and informatics, 2016Co-Authors: Sandra Poeschl, Nicola DoeringAbstract:Virtual reality exposure therapy (VRET) applications use high levels of fidelity in order to produce high levels of presence and thereby elicit an emotional response for the user (like fear for phobia treatment). State of research shows mixed results for the correlation between anxiety and presence in virtual reality exposure, with differing results depending on specific anxiety disorders. A positive correlation for anxiety and presence for social anxiety disorder is not proven up to now. One reason might be that plausibility of the simulation, namely including key triggers for social anxiety (for example verbal and non-verbal behavior of virtual agents that reflects potentially negative human evaluation) might not be acknowledged in current presence questionnaires. A German scale for measuring co-presence and social presence for virtual reality (VR) fear of public speaking scenarios was developed based on a translation and adaption of existing co- presence and social presence questionnaires. A sample of N = 151 students rated co-presence and social presence after using a fear of public speaking application. Four correlated factors were derived by item- and principle axis factor analysis (Promax Rotation), representing the presenter’s reaction to virtual agents, the reactions of the virtual agents as perceived by the presenter, impression of interaction possibilities, and (co-)presence of other people in the virtual environment. The scale developed can be used as a starting point for future research and test construction for VR applications with a social context.
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Measuring co-presence and social presence in virtual environments - Psychometric construction of a german scale for a fear of public speaking scenario
Annual Review of CyberTherapy and Telemedicine, 2015Co-Authors: Sandra Poeschl, Nicola DoeringAbstract:Virtual reality exposure therapy (VRET) applications use high levels of fidelity in order to produce high levels of presence and thereby elicit an emotional response for the user (like fear for phobia treatment). State of research shows mixed results for the correlation between anxiety and presence in virtual reality exposure, with differing results depending on specific anxiety disorders. A positive correlation for anxiety and presence for social anxiety disorder is not proven up to now. One reason might be that plausibility of the simulation, namely including key triggers for social anxiety (for example verbal and non-verbal behavior of virtual agents that reflects potentially negative human evaluation) might not be acknowledged in current presence questionnaires. A German scale for measuring co-presence and social presence for virtual reality (VR) fear of public speaking scenarios was developed based on a translation and adaption of existing co-presence and social presence questionnaires. A sample of N = 151 students rated co-presence and social presence after using a fear of public speaking application. Four correlated factors were derived by item- and principle axis factor analysis (Promax Rotation), representing the presenter's reaction to virtual agents, the reactions of the virtual agents as perceived by the presenter, impression of interaction possibilities, and (co-)presence of other people in the virtual environment. The scale developed can be used as a starting point for future research and test construction for VR applications with a social context.
Wilma A M Vollebergh - One of the best experts on this subject based on the ideXlab platform.
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psychometric properties of the world health organization disability assessment schedule used in the european study of the epidemiology of mental disorders
International Journal of Methods in Psychiatric Research, 2008Co-Authors: M A Buistbouwman, Johan Ormel, R De Graaf, G Vilagut, J Alonso, E Van Sonderen, Wilma A M VolleberghAbstract:This study assessed the factor structure, internal consistency, and discriminatory validity of the World Health Organization Disability Assessment Schedule (WHODAS) version used in the European Study of the Epidemiology of Mental Disorders (ESEMeD). In total 8796 adults were assessed using the ESEMeD WHODAS (22 severity and 8 frequency items). An Exploratory Factor Analysis (EFA) with Promax Rotation was done with a random 50%. The other half was used for confi rmatory factor analysis (CFA) comparing models (a) suggested by EFA; (b) hypothesized a priori; and (c) reduced with four items. A CFA model with covariates was conducted in the whole sample to assess invariance across Mediterranean (Spain, France and Italy) and non-Mediterranean (Belgium, Germany and the Netherlands) countries. Cronbach’s alphas and discriminatory validity were also examined. EFA identifi ed seven factors (explained variance: 80%). The reduced model (six factors, four frequency items excluded) presented the best fi t [Confi rmatory Fit Index (CFI) = 0.992, Tucker‐Lewis Index (TLI) = 0.996, Root Mean Square Error of Approximation (RMSEA) = 0.024]. The second-order factor structure also fi tted well (CFI = 0.987, TLI = 0.991, RMSEA = 0.036). Measurement non-invariance was found for Embarrassment. Cronbach’s alphas ranged from 0.84 for Participation to 0.93 for Mobility. Preliminary data suggest acceptable discriminatory validity. Thus, the ESEMeD WHODAS may well be a valuable shortened version of the WHODAS-II, but future users should reconsider the fi lter questions. Copyright © 2008 John Wiley & Sons, Ltd.
Daniel J Beal - One of the best experts on this subject based on the ideXlab platform.
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optimizing principal components analysis of event related potentials matrix type factor loading weighting extraction and Rotations
Clinical Neurophysiology, 2005Co-Authors: Joseph Dien, Daniel J Beal, Patrick BergAbstract:Abstract Objective Given conflicting recommendations in the literature, this report seeks to present a standard protocol for applying principal components analysis (PCA) to event-related potential (ERP) datasets. Methods The effects of a covariance versus a correlation matrix, Kaiser normalization vs. covariance loadings, truncated versus unrestricted solutions, and Varimax versus Promax Rotations were tested on 100 simulation datasets. Also, whether the effects of these parameters are mediated by component size was examined. Results Parameters were evaluated according to time course reconstruction, source localization results, and misallocation of ANOVA effects. Correlation matrices resulted in dramatic misallocation of variance. The Promax Rotation yielded much more accurate results than Varimax Rotation. Covariance loadings were inferior to Kaiser Normalization and unweighted loadings. Conclusions Based on the current simulation of two components, the evidence supports the use of a covariance matrix, Kaiser normalization, and Promax Rotation. When these parameters are used, unrestricted solutions did not materially improve the results. We argue against their use. Results also suggest that optimized PCA procedures can measurably improve source localization results. Significance Continued development of PCA procedures can improve the results when PCA is applied to ERP datasets.
Joseph Dien - One of the best experts on this subject based on the ideXlab platform.
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optimizing principal components analysis of event related potentials matrix type factor loading weighting extraction and Rotations
Clinical Neurophysiology, 2005Co-Authors: Joseph Dien, Daniel J Beal, Patrick BergAbstract:Abstract Objective Given conflicting recommendations in the literature, this report seeks to present a standard protocol for applying principal components analysis (PCA) to event-related potential (ERP) datasets. Methods The effects of a covariance versus a correlation matrix, Kaiser normalization vs. covariance loadings, truncated versus unrestricted solutions, and Varimax versus Promax Rotations were tested on 100 simulation datasets. Also, whether the effects of these parameters are mediated by component size was examined. Results Parameters were evaluated according to time course reconstruction, source localization results, and misallocation of ANOVA effects. Correlation matrices resulted in dramatic misallocation of variance. The Promax Rotation yielded much more accurate results than Varimax Rotation. Covariance loadings were inferior to Kaiser Normalization and unweighted loadings. Conclusions Based on the current simulation of two components, the evidence supports the use of a covariance matrix, Kaiser normalization, and Promax Rotation. When these parameters are used, unrestricted solutions did not materially improve the results. We argue against their use. Results also suggest that optimized PCA procedures can measurably improve source localization results. Significance Continued development of PCA procedures can improve the results when PCA is applied to ERP datasets.
