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.

The role of the Rocky Mountains in shaping the atmospheric mean state and the response to tropical forcing in idealised simulations.

Understanding the natural and forced variability of the atmospheric general circulation and its drivers is one of the grand challenges in climate science. It is of paramount importance to understand to what extent the systematic error of climate models affects the processes driving such variability. This is done by performing a set of simulations (ROCK experiments) with an intermediate complexity atmospheric model (SPEEDY), in which the Rocky Mountains orography is increased or decreased to influence the structure of the North Pacific jet stream. For each of these modified-orography experiments, the climatic response to idealized sea surface temperature anomalies of varying intensity in the El Niño Southern Oscillation (ENSO) region is studied. ROCK experiments are characterized by variations in the Pacific jet stream intensity whose extension encompasses the spread of the systematic error found in Coupled Model Intercomparison Project (CMIP6) models. When forced with ENSO-like idealised anomalies, they exhibit a non-negligible sensitivity in the response pattern over the Pacific North American region, indicating that the model mean state can affect the model response to ENSO. It is found that the classical Rossby wave train response to ENSO is more meridionally oriented when the Pacific jet stream is weaker and more zonally oriented with a stronger jet. Rossby wave linear theory suggests that a stronger jet implies a stronger waveguide, which traps Rossby waves at a lower latitude, favouring a zonal propagation of Rossby waves. The shape of the dynamical response to ENSO affects the ENSO impacts on surface temperature and precipitation over Central and North America. A comparison of the SPEEDY results with CMIP6 models suggests a wider applicability of the results to more resources-demanding climate general circulation models (GCMs), opening up to future works focusing on the relationship between Pacific jet misrepresentation and response to external forcing in fully-fledged GCMs.