1,721,768 research outputs found
Dynamic Fuzzy Rating Tracker (DYFRAT): A novel methodology for modeling real-time dynamic cognitive processes in rating scales
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A probabilistic approach for evaluating the sensitivity to Fake Data in Structural Equation Modeling.
No full textIn this paper we address the issue of evaluating the sensitivity of goodness-of-fit indices in
structural equation modeling when fake data are considered in three different factorial models with varying sample sizes (n= 50, 100 and 200). The sensitivity evaluation is carried out by means of a simulation
procedure which combines a standard Monte Carlo approach and a new probabilistic version of a recent
data perturbation procedure called Sample Generation by Replacements (SGR, Lombardi, Pastore and
Nucci, 2004). Probabilistic SGR (PSGR) will be used to generate data perturbations based on three different models of faking: fake-uniform, fake-good (deception) and fake-bad (malingering). For each scenario
of faking the performance of four very popular goodness-of-fit indices (two absolute indices: GFI, and
AGFI; and two incremental indices: CFI and NNFI) will be evaluated
Copper toxicity in Prunus cerasifera: growth and antioxidant enzymes responses of in vitro grown plants
No full textEvaluating the Sensitivity ofGoodness-of-Fit Indices to Data Perturbation:An Integrated MC-SGR Approach
No full textThe issue of perturbations in real or simulated data has been substantially neglected in evaluating the adequacy of fit indices used to test covariance structure modeling. Nevertheless, it is certainly legitimate to wonder whether fit indices are reliably sensitive to data corruption. In particular, we would expect that a good index should approach its maximum under correct model specification and uncorrupted data, but also degrade substantially under massive data perturbation. In this paper we provide a possible methodological solution to the problem of evaluating the sensitivity of fit indices in structural equation modeling when perturbed data are considered. In particular, in our study the sensitivity of four different fit indices (two absolute fit-indices: GFI and AGFI, and two incremental fit-indices: CFI and NNFI) to perturbed data is examined in three different factorial models. The sensitivity evaluation is carried out by means of a new integrated approach which combines standard
Monte Carlo (MC) simulations and a recent data generating procedure called
Sample Generation by Replacements (SGR, [Lombardi et al., 2004])
Semantic relevance and semantic disorders
No full textSemantic features are of different importance in concept representation. The concept elephant may be more easily identified from the feature than from the feature . As propose a new model of semantic memory to measure the relevance of semantic features for a concept and use this model to investigate the controversial issue of category specificity. Category-specific patients have an impairment in one domain of knowledge (e.g., living), whereas the other domain (e.g., nonliving) is relatively spared. We show that categories differ in the level of relevance and that, when concepts belonging to living and nonliving categories are equated to this parameter, the category-specific disorder disappears. Our findings suggest that category specificity, as well as other semantic-related effects, may be explained by a semantic memory model in which concepts are represented by semantic features with associated relevance values
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