Chapter Measuring content validity of academic psychological capital and locus of control in fresh graduates

Positive psychological capital (PsyCap; hope, resilience, self-efficacy, and optimism) and locus of control (LoC; internal and external) denote psychological dimensions which have been identified as crucial resources for occupational satisfaction and success. These dimensions could impact fresh graduates’ ability to stand the labour market in times of crisis. Two instruments, called Academic PsyCap and Academic LoC, have been specifically developed to evaluate these dimensions among fresh graduates. The two instruments consist of 34 and 10 items respectively, which have been selected, through factor analyses, from a large initial pool of items administered to fresh graduated at the University of Padova. Results suggested adequate psychometric properties for both Academic PsyCap and Academic LoC. The factor structure of the two instruments was confirmed (CFI = .92, RMSEA = .07, SRMR = .07 for Academic PsyCap; CFI = .96, RMSEA = .05, SRMR = .05 for Academic LoC), and internal consistency was satisfactory for all the subscales. The two instruments are presented, and their psychometric properties are described

Chapter Psychometric properties of a new scale for measuring academic positive psychological capital

Positive psychological capital (PsyCap) is the name given to a set of psychological dimensions (hope, resilience, self-efficacy, and optimism) that may support students in their effort to achieve better academic results and even improve the employability of graduates. These dimensions could help students to achieve better academic results and impact fresh graduates’ ability to stand the labour market in times of crisis. A scale, called Academic PsyCap, was specifically developed to evaluate the four PsyCap dimensions among students and fresh graduates. To deeply investigate the structural validity of the scale, three alternative models (one-factor model, correlated four-factor model, bifactor model) were run on the responses provided by about 1,600 fresh graduates at the University of Padua. The results indicated that the bifactor model fit the data better than the other two models. In this model, all items significantly loaded on both their own domain specific factor and on the general factor. The values of Percentage of Uncontaminated Correlations (PUC), Explained Common Variance (ECV), and Hierarchical Omega suggested that multidimensionality in the scale was not severe enough to disqualify the use of a total PsyCap score. The scale was found to be invariant across gender and academic degree (bachelor’s and master’s degree). Internal consistency indices were satisfactory for the four dimensions and the total scale

Recovering a probabilistic knowledge structure by constraining its parameter space

In the Basic Local Independence Model (BLIM) of Doignon and Falmagne (Knowledge Spaces, Springer, Berlin, 1999), the probabilistic relationship between the latent knowledge states and the observ- able response patterns is established by the introduction of a pair of parameters for each of the problems: a lucky guess probability and a careless error probability. In estimating the parameters of the BLIM with an empirical data set, it is desirable that such probabilities remain reasonably small. A special case of the BLIM is proposed where the parameter space of such probabilities is constrained. A simulation study shows that the constrained BLIM is more effective than the unconstrained one, in recovering a probabilistic knowledge structure

Il modello Many Facet Rasch Measurement per il benchmarking

The Many Facer Rasch Measurement model (Linacre, 1989) is a Rasch model which allows us to estimate parameters not only for person's "ability" in a item and the item "difficulty", but also for other aspects (facets) that are believed to be relevant to the research goals. Because of its peculia~ities,this model is especially indicated for benchmarking. It combines the properties of every Rasch model with the flexibility of use and the ease in considering different dimensions which share the same latent trait. In this article, the model features in relation to benchmarking are discussed, and benchmarking is ap- plied to hospitals and departments as regards patients' satisfaction