A bayesian model for mixed responses and skew laten variable to measure HRQoL in children

The aim of the thesi is to formulate a suitable Item Response Theory (IRT) based model to measure HRQoL (as latent variable) using a mixed responses questionnaire and relaxing the hypothesis of normal distributed latent variable. The new model is a combination of two models already presented in literature, that is, a latent trait model for mixed responses and an IRT model for Skew Normal latent variable. It is developed in a Bayesian framework, a Markov chain Monte Carlo procedure is used to generate samples of the posterior distribution of the parameters of interest. The proposed model is test on a questionnaire composed by 5 discrete items and one continuous to measure HRQoL in children, the EQ-5D-Y questionnaire. A large sample of children collected in the schools was used. In comparison with a model for only discrete responses and a model for mixed responses and normal latent variable, the new model has better performances, in term of deviance information criterion (DIC), chain convergences times and precision of the estimates.

L'impiego del modello di Rash per la valutazione della soddisfazione del paziente nell'ambito delle strutture sanitarie di ricovero

Hospitals and health service providers are use to collect data about patient’s opinion to improve patient health status and communication with them and to upgrade the management and the organization of the health service provided. A lot of survey are carry out for this purpose and several questionnaire are built to measure patient satisfaction. In particular patient satisfaction is a way to describe and assess the level of hospital service from the patient’s point of view. It is a cognitive and an emotional response to the hospital experience. Methodologically patient satisfaction is defined as a multidimensional latent variable. To assess patient satisfaction Item Response Theory has greater advantages compared to Classical Test Theory. Rasch model is a one-parameter model which belongs to Item Response Theory. Rasch model yield objective measure of the construct that are independent of the set of people interviewed and of set of items used. Rasch estimates are continuous and can be useful to “calibrate” the scale of the latent trait. This research attempt to investigate the questionnaire currently adopted to measure patient satisfaction in an Italian hospital, completed by a large sample of 3390 patients. We verify the multidimensional nature of the variable, the properties of the instrument and the level of satisfaction in the hospital. Successively we used Rasch estimates to describe the most satisfied and the less satisfied patients.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.

Clustering of variables around latent components: an application in consumer science

The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.

Induction Motor Diagnosis in Variable Speed Drives

Several diagnostic techniques are presented for the detection of electrical fault in induction motor variable speed drives. These techinques are developed taking into account the impact of the control system on machine variables and non stationary operating conditions.

Pulsating variable stars as tracers of galactic structure and interaction mechanisms

My PhD project has been focused on the study of the pulsating variable stars in two ultra-faint dwarf spheroidal satellites of the Milky Way, namely, Leo IV and Hercules; and in two fields of the Large Magellanic Cloud (namely, the Gaia South Ecliptic Pole calibration field, and the 30 Doradus region) that were repeatedly observed in the KS band by the VISTA Magellanic Cloud (VMC, PI M.R. Cioni) survey of the Magellanic System.

In defence of modeling simultaneity for a correct approximation of cultural aspects: implications for food consumers studies with latent variables

Dealing with latent constructs (loaded by reflective and congeneric measures) cross-culturally compared means studying how these unobserved variables vary, and/or covary each other, after controlling for possibly disturbing cultural forces. This yields to the so-called ‘measurement invariance’ matter that refers to the extent to which data collected by the same multi-item measurement instrument (i.e., self-reported questionnaire of items underlying common latent constructs) are comparable across different cultural environments. As a matter of fact, it would be unthinkable exploring latent variables heterogeneity (e.g., latent means; latent levels of deviations from the means (i.e., latent variances), latent levels of shared variation from the respective means (i.e., latent covariances), levels of magnitude of structural path coefficients with regard to causal relations among latent variables) across different populations without controlling for cultural bias in the underlying measures. Furthermore, it would be unrealistic to assess this latter correction without using a framework that is able to take into account all these potential cultural biases across populations simultaneously. Since the real world ‘acts’ in a simultaneous way as well. As a consequence, I, as researcher, may want to control for cultural forces hypothesizing they are all acting at the same time throughout groups of comparison and therefore examining if they are inflating or suppressing my new estimations with hierarchical nested constraints on the original estimated parameters. Multi Sample Structural Equation Modeling-based Confirmatory Factor Analysis (MS-SEM-based CFA) still represents a dominant and flexible statistical framework to work out this potential cultural bias in a simultaneous way. With this dissertation I wanted to make an attempt to introduce new viewpoints on measurement invariance handled under covariance-based SEM framework by means of a consumer behavior modeling application on functional food choices.

Multidimensional item response theory models with general and specific latent traits for ordinal data

The aim of the thesis is to propose a Bayesian estimation through Markov chain Monte Carlo of multidimensional item response theory models for graded responses with complex structures and correlated traits. In particular, this work focuses on the multiunidimensional and the additive underlying latent structures, considering that the first one is widely used and represents a classical approach in multidimensional item response analysis, while the second one is able to reflect the complexity of real interactions between items and respondents. A simulation study is conducted to evaluate the parameter recovery for the proposed models under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameter recovery is particularly sensitive to the sample size, due to the model complexity and the high number of parameters to be estimated. For a sufficiently large sample size the parameters of the multiunidimensional and additive graded response models are well reproduced. The results are also affected by the trade-off between the number of items constituting the test and the number of item categories. An application of the proposed models on response data collected to investigate Romagna and San Marino residents' perceptions and attitudes towards the tourism industry is also presented.

Apple latent infection caused by Neofabraea alba: host-pathogen interaction and disease management

Apple latent infection caused by Neofabraea alba: host-pathogen interaction and disease management Bull’s eye rot (BER) caused by Neofabraea alba is one of the most frequent and damaging latent infection occurring in stored pome fruits worldwide. Fruit infection occurs in the orchard, but disease symptoms appear only 3 months after harvest, during refrigerated storage. In Italy BER is particularly serious for late harvest apple cultivar as ‘Pink Lady™’. The purposes of this thesis were: i) Evaluate the influence of ‘Pink Lady™’ apple primary metabolites in N. alba quiescence ii) Evaluate the influence of pH in five different apple cultivars on BER susceptibility iii) To find out not chemical method to control N. alba infection iv) Identify some fungal volatile compounds in order to use them as N. alba infections markers. Results regarding the role of primary metabolites showed that chlorogenic, quinic and malic acid inhibit N. alba development. The study based on the evaluation of cultivar susceptibility, showed that Granny Smith was the most resistant apple cultivar among the varieties analyzed. Moreover, Granny Smith showed the lowest pH value from harvest until the end of storage, supporting the thesis that ambient pH could be involved in the interaction between N. alba and apple. In order to find out new technologies able to improve lenticel rot management, the application of a non-destructive device for the determination of chlorophyll content was applied. Results showed that fruit with higher chlorophyll content are less susceptible to BER, and molecular analyses comforted this result. Fruits with higher chlorophyll content showed up-regulation of PGIP and HCT, genes involved in plant defence. Through the application of PTR-MS and SPME GC-MS, 25 volatile organic compounds emitted by N. alba were identified. Among them, 16 molecules were identified as potential biomarkers.