Similarity Structure Analysis and Structural Equation Modeling in Studying Latent Structures: An Application to the Attitudes towards Portuguese Language Questionnaire

Several international studies such as PISA and PILRS (Progress in International Reading Literacy Study), have stressed the importance of positive attitudes and behaviours as facilitators of individuals reading literacy during the school years and throughout their lives. Considering that there are not available instruments for assessing attitudes Towards Portuguese Language, it was proposed the development of the Attitudes towards Portuguese Language Questionnaire – ATPLQ (Questionário de Atitudes Face à Língua Portuguesa: QAFLP, Neto et al., 2011; Rebelo, 2012). The questionnaire has 22 Likert-type items, with four levels of response (Strongly Disagree, Disagree, Agree, Strongly Agree), spread, through exploratory factor analysis (EFA), over three attitudinal dimensions: Behavioural, Affective, and Motivational.In this study we aimed to analyse the ATPLQ’s latent structure with a pooled sample data of 1441 participants, applying similarity structure analysis (SSA) and confirmatory factor analysis of ordinal data (CFA). The SSA was carried out with Hudap in order to identify the structural properties of the questionnaire and to assess its adequacy in a Portuguese population. The CFA was carried out with LISREL in order to assure structural validity, i.e., accounting for factorial validity, but also for factors’ convergent and discriminant validity, and composite reliability. These psychometric features allowed the comparison of both the EFA derived model and the SSA derived model. We justify the selection of the SSA’s model, and we discuss the similarities between the results generated by SSA and LISREL procedures, highlighting their use in modeling constructs with ordinal indicators

A Detail Based Method for Linear Full Reference Image Quality Prediction

In this paper, a novel Full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical Differential Mean Opinion Score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.Comment: 15 pages, 9 figures. Copyright notice: The paper has been accepted for publication on the IEEE Trans. on Image Processing on 19/09/2017 and the copyright has been transferred to the IEE

Temporal similarity metrics for latent network reconstruction: The role of time-lag decay

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag strongly affects the metrics' reconstruction accuracy, depending on the network's clustering coefficient and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks

Upward Influence in Organizations: Test of A Model

A causal model of upward influence in organizations was proposed and tested on a sample of staff nurses and their supervisors in a hospital setting. LISREL results demonstrated that the proposed model fit the data well, and reflected a better fit than several alternative models that were estimated. The contributions and limitations of the present study are discussed, in addition to challenges and directions for future research

Determinant factors of structural similarity at the regional level: evidence from Portugal

There is scant evidence on the determinant factors of structural similarity between geographical spaces; moreover, it has been produced considering only the national level. The present study provides evidence on this topic at the regional level, based on the analysis of 275 Portuguese counties. The results obtained confirm the importance of several explanatory factors, suggesting that the structural similarity between Portuguese counties increases with geographical proximity, the existence of a shared boundary, the similarity of factor endowments in terms of physical and human capital and the similarity in terms of economic centrality and market dimension. Key words: productive structure, Portugal, structural similarity, factor endowments, economic geography

Evaluation of protein surface roughness index using its heat denatured aggregates

Recent research works on potential of different protein surface describing parameters to predict protein surface properties gained significance for its possible implication in extracting clues on protein's functional site. In this direction, Surface Roughness Index, a surface topological parameter, showed its potential to predict SCOP-family of protein. The present work stands on the foundation of these works where a semi-empirical method for evaluation of Surface Roughness Index directly from its heat denatured protein aggregates (HDPA) was designed and demonstrated successfully. The steps followed consist, the extraction of a feature, Intensity Level Multifractal Dimension (ILMFD) from the microscopic images of HDPA, followed by the mapping of ILMFD into Surface Roughness Index (SRI) through recurrent backpropagation network (RBPN). Finally SRI for a particular protein was predicted by clustering of decisions obtained through feeding of multiple data into RBPN, to obtain general tendency of decision, as well as to discard the noisy dataset. The cluster centre of the largest cluster was found to be the best match for mapping of Surface Roughness Index of each protein in our study. The semi-empirical approach adopted in this paper, shows a way to evaluate protein's surface property without depending on its already evaluated structure