78,669 research outputs found

    Exploration and confirmation of the latent variable structure of the Jefferson scale of empathy.

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    OBJECTIVE: To reaffirm the underlying components of the JSE by using exploratory factor analysis (EFA), and to confirm its latent variable structure by using confirmatory factor analysis (CFA). METHODS: Research participants included 2,612 medical students who entered Jefferson Medical College between 2002 and 2012. This sample was divided into two groups: Matriculants between 2002 and 2007 (n=1,380) and be-tween 2008 and 2012 (n=1,232). Data for 2002-2007 matriculants were subjected to EFA (principal component factor extraction), and data for matriculants of 2008-2012 were used for CFA (structural equation modeling, and root mean square error for approximation. RESULTS: The EFA resulted in three factors: perspective-taking, compassionate care and walking in patient\u27s shoes replicating the 3-factor model reported in most of the previous studies. The CFA showed that the 3-factor model was an acceptable fit, thus confirming the latent variable structure emerged in the EFA. Corrected item-total score correlations for the total sample were all positive and statistically significant, ranging from 0.13 to 0.61 with a median of 0.44 (p=0.01). The item discrimination effect size indices (contrasting item mean scores for the top-third versus bottom-third JSE scorers) ranged from 0.50 to 1.4 indicating that the differences in item mean scores between top and bottom scorers on the JSE were of practical importance. Cronbach\u27s alpha coefficient of the JSE for the total sample was 0.80, ranging from 0.75 to 0.84 for matriculatnts of different years. CONCLUSIONS: Findings provided further support for under-lying constructs of the JSE, adding to its credibility

    Unconventional monetary policy and inflation expectations in the Euro area. CEPS Working Document No 2020/01, January 2020

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    With the ECB's policy rate having reached the zero lower bound, traditional monetary policy tools became ineffective and the ECB was forced to adopt a set of unconventional monetary policy (UMP) measures. This paper examines the effects of the ECB's UMP on inflation expectations in the Euro area as inflation expectations play a key role for achieving the inflation target of below, but close to 2%. Quantifying the impact of UMP is not straightforward, as standard empirical tools such as VAR cannot be applied. Hence, we use the Qual VAR approach pioneered by Dueker (2005) to overcome this problem. We indeed find that UMP leads to a rise in inflation expectations in the short run but that this effect appears to evaporate in the medium term. Our results put some doubt on the common claim that UMP has consistently contributed to a re-anchoring and a stabilisation of inflation expectations at the zero lower bound. Nevertheless, they indicate a rise in mediumterm real GDP growth triggered by UMP

    Towards the Final Frontier: Using Strategic Communication Activities to Engage the Latent Public as a Key Stakeholder in a Corporate Mission

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    Private corporations that do not normally interact with, nor regularly communicate with, the public often do not perceive the public as a relevant or active stakeholder. The public may not view themselves as a stakeholder, particularly when they are unaware of, have no direct dealings with, or do not have any problems associated with such a corporation. The current study, utilizing a national survey of the United States public (N = 424) found that through directed strategic communication activities of a private spaceflight corporation, utilizing social and new media tools, a latent public can perceive a corporation and its mission in a positive manner, and transition it towards a status of an aware public and possible active public. Positive perceptions were found regarding corporate credibility, brand awareness, public engagement, communicating a corporate mission, educating the public, and influencing public opinion

    A Statistical Social Network Model for Consumption Data in Food Webs

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    We adapt existing statistical modeling techniques for social networks to study consumption data observed in trophic food webs. These data describe the feeding volume (non-negative) among organisms grouped into nodes, called trophic species, that form the food web. Model complexity arises due to the extensive amount of zeros in the data, as each node in the web is predator/prey to only a small number of other trophic species. Many of the zeros are regarded as structural (non-random) in the context of feeding behavior. The presence of basal prey and top predator nodes (those who never consume and those who are never consumed, with probability 1) creates additional complexity to the statistical modeling. We develop a special statistical social network model to account for such network features. The model is applied to two empirical food webs; focus is on the web for which the population size of seals is of concern to various commercial fisheries.Comment: On 2013-09-05, a revised version entitled "A Statistical Social Network Model for Consumption Data in Trophic Food Webs" was accepted for publication in the upcoming Special Issue "Statistical Methods for Ecology" in the journal Statistical Methodolog

    CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection

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    Detecting whether a news article is fake or genuine is a crucial task in today's digital world where it's easy to create and spread a misleading news article. This is especially true of news stories shared on social media since they don't undergo any stringent journalistic checking associated with main stream media. Given the inherent human tendency to share information with their social connections at a mouse-click, fake news articles masquerading as real ones, tend to spread widely and virally. The presence of echo chambers (people sharing same beliefs) in social networks, only adds to this problem of wide-spread existence of fake news on social media. In this paper, we tackle the problem of fake news detection from social media by exploiting the very presence of echo chambers that exist within the social network of users to obtain an efficient and informative latent representation of the news article. By modeling the echo-chambers as closely-connected communities within the social network, we represent a news article as a 3-mode tensor of the structure - and propose a tensor factorization based method to encode the news article in a latent embedding space preserving the community structure. We also propose an extension of the above method, which jointly models the community and content information of the news article through a coupled matrix-tensor factorization framework. We empirically demonstrate the efficacy of our method for the task of Fake News Detection over two real-world datasets. Further, we validate the generalization of the resulting embeddings over two other auxiliary tasks, namely: \textbf{1)} News Cohort Analysis and \textbf{2)} Collaborative News Recommendation. Our proposed method outperforms appropriate baselines for both the tasks, establishing its generalization.Comment: Presented at ASONAM'1
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