Resumo A análise da consistência interna de uma medida psicológica é uma necessidade aceite na comunidade científica. Entre os diferentes métodos que nos fornecem estimativas do grau

Sponsorship studies have generally been focused on attitudinal measures of fan loyalty to understand the reactions to abstract sponsors. This study examines the relationships between both attitudinal and behavioral loyalty with sponsorship awareness, attitude toward two actual sponsors, and purchase intentions. Data were collected among fans of a professional soccer team, and the results of a structural equation model provide evidence that attitudinal loyalty impacts the attitude toward both sponsors and purchase intentions. Behavioral loyalty influences sponsorship awareness, and impacts differently the attitude and purchase intentions toward each sponsor. Sponsorship awareness influences significantly the attitude toward both sponsors, while the attitude toward the sponsor was the strongest predictor of purchase intentions. These findings highlight the importance of examining actual sponsors and suggest managerial implications, such as the need for sponsors to help attract fans to the stadium and to design additional activation strategies to improve sponsorship value.

BackgroundDementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test.ResultsPress' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5.ConclusionsWhen taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.

A two-factor structure of SCS with strong psychometric validity was supported in clinical and non-clinical samples. Helping individuals with limited experiences of compassion to develop positive internal processing systems seems to be related with better mental health, self-acceptance and self-nurturing abilities. The non-probabilistic sampling limits the generalization of our conclusions.

Student engagement is a key factor in academic achievement and degree completion, though there is much debate about the operationalization and dimensionality of this construct. The goal of this paper is to describe the development of an psycho-educational oriented measure -the University Student Engagement Inventory (USEI). This measure draws on the conceptualization of engagement as a multidimensional construct, including cognitive, behavioural and emotional engagement. Participants were 609 Portuguese University students (67 % female) majoring in Social Sciences, Biological Sciences or Engineering and Exact Sciences. The content, construct and predictive validity, and reliability of the USEI were tested. The validated USEI was composed of 15 items, and supported the tri-factorial structure of student engagement. We documented evidence of adequate reliability, factorial, convergent and discriminant validities. USEI's concurrent validity, with the Utrecht Work Engagement Scale-Student Survey, and the predictive validity for self-reported academic achievement and intention to dropout from school were also observed.

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Adenosine neuromodulation depends on a balanced activation of inhibitory A 1 (A 1 R) and facilitatory A 2A receptors (A 2A R). Both A 1 R and A 2A R modulate hippocampal glutamate release and NMDA-dependent long-term potentiation (LTP) but ageing affects the density of both A 1 R and A 2A R. We tested the effects of selective A 1 R and A 2A R antagonists in the modulation of synaptic transmission and plasticity in rat hippocampal slices from three age groups (young adults, 2-3 month; middle-aged adults, 6-8 months; aged, 18-20 months). The selective A 2A R antagonist SCH58261 (50 nm) attenuated LTP in all age groups, with a larger effect in aged ()63 ± 7%) than in middle-aged adults ()36 ± 9%) or young adult rats ()36 ± 9%). In contrast, the selective A 1 R antagonist DPCPX (50 nm) increased LTP magnitude in young adult rats (+42 ± 6%), but failed to affect LTP magnitude in the other age groups. Finally, in the continuous presence of DPCPX, SCH58261 caused a significantly larger inhibition of LTP amplitude in aged ()71 ± 45%) than middle-aged ()28 ± 9%) or young rats ()11 ± 2%). Accordingly, aged rats displayed an increased expression of A 2A R mRNA in the hippocampus and a higher number of glutamatergic nerve terminals equipped with A 2A R in aged (67 ± 6%) compared with middle-aged (34 ± 7%) and young rats (25 ± 5%). The results show an enhanced A 2A R-mediated modulation of LTP in aged rats, in accordance with the age-associated increased expression and density of A 2A R in glutamatergic terminals. This age-associated gain of function of A 2A R modulating synaptic plasticity may underlie the ability of A 2A R antagonists to prevent memory dysfunction in aged animals.

The Montreal Cognitive Assessment (MoCA) is a brief instrument developed for the screening of milder forms of cognitive impairment. The present study aims to assess the construct related validity of the MoCA through the establishment of the factorial, convergent, and discriminant related validities, and the reliability of data. In a Portuguese sample of 830 participants, several models were tested using Confirmatory Factor Analysis. Although all tested models showed a good fit, the six-factor model based on the conceptual model proposed by the MoCA's authors showed a significantly better fit. The results allowed us to establish the factorial, convergent, and discriminant validity of this six-dimensional structure. An overall psychometric adequacy of the items, and a good reliability were also found. This study contributes to overcome an important gap in the construct related validity of this instrument. The present findings corroborate the six-dimensional structure of the MoCA and provide good evidence of the construct related validity. The MoCA has proved to be an appropriate measure for cognitive screening taking into account different cognitive domains, which will enable clinicians and researchers to use this test and its six latent dimensions to achieve a better understanding of the individuals' cognitive profile. (JINS, 2012, 18, 242-250)