Showing papers by "Francesco Bartolucci published in 2015"

Assessing User Satisfaction in the Era of User Experience: Comparison of the SUS, UMUX, and UMUX-LITE as a Function of Product Experience

Abstract: Nowadays, practitioners extensively apply quick and reliable scales of user satisfaction as part of their user experience analyses to obtain well-founded measures of user satisfaction within time and budget constraints. However, in the human–computer interaction literature the relationship between the outcomes of standardized satisfaction scales and the amount of product usage has been only marginally explored. The few studies that have investigated this relationship have typically shown that users who have interacted more with a product have higher satisfaction. The purpose of this article was to systematically analyze the variation in outcomes of three standardized user satisfaction scales (SUS, UMUX, UMUX-LITE) when completed by users who had spent different amounts of time with a website. In two studies, the amount of interaction was manipulated to assess its effect on user satisfaction. Measurements of the three scales were strongly correlated and their outcomes were significantly affected by the amo...

A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates.

Abstract: Summary Mixed latent Markov (MLM) models represent an important tool of analysis of longitudinal data when response variables are affected by time-fixed and time-varying unobserved heterogeneity, in which the latter is accounted for by a hidden Markov chain. In order to avoid bias when using a model of this type in the presence of informative drop-out, we propose an event-history (EH) extension of the latent Markov approach that may be used with multivariate longitudinal data, in which one or more outcomes of a different nature are observed at each time occasion. The EH component of the resulting model is referred to the interval-censored drop-out, and bias in MLM modeling is avoided by correlated random effects, included in the different model components, which follow common latent distributions. In order to perform maximum likelihood estimation of the proposed model by the expectation–maximization algorithm, we extend the usual forward-backward recursions of Baum and Welch. The algorithm has the same complexity as the one adopted in cases of non-informative drop-out. We illustrate the proposed approach through simulations and an application based on data coming from a medical study about primary biliary cirrhosis in which there are two outcomes of interest, one continuous and the other binary.

A finite mixture latent trajectory model for modeling ultrarunners’ behavior in a 24-hour race

Abstract: A finite mixture latent trajectory model is developed to study the performance and strategy of runners in a 24-h long ultra running race. The model facilitates clustering of runners based on their speed and propensity to rest and thus reveals the strategies used in the race. Inference for the adopted latent trajectory model is achieved using an expectation-maximization algorithm. Fitting the model to data from the 2013 World Championships reveals three clearly separated clusters of runners who exhibit different strategies throughout the race. The strategies show that runners can be grouped in terms of their average moving speed and their propensity to rest during the race. The effect of age and gender on the probability of belonging to each cluster is also investigated.

A Multidimensional Finite Mixture Structural Equation Model for Nonignorable Missing Responses to Test Items

Abstract: We propose a structural equation model, which reduces to a multidimensional latent class item response theory model, for the analysis of binary item responses with nonignorable missingness. The missingness mechanism is driven by 2 sets of latent variables: one describing the propensity to respond and the other referred to the abilities measured by the test items. These latent variables are assumed to have a discrete distribution, so as to reduce the number of parametric assumptions regarding the latent structure of the model. Individual covariates can also be included through a multinomial logistic parameterization for the distribution of the latent variables. Given the discrete nature of this distribution, the proposed model is efficiently estimated by the expectation–maximization algorithm. A simulation study is performed to evaluate the finite-sample properties of the parameter estimates. Moreover, an application is illustrated with data coming from a student entry test for the admission to some univer...

Information matrix for hidden Markov models with covariates

Abstract: For a general class of hidden Markov models that may include time-varying covariates, we illustrate how to compute the observed information matrix, which may be used to obtain standard errors for the parameter estimates and check model identifiability. The proposed method is based on the Oakes' identity and, as such, it allows for the exact computation of the information matrix on the basis of the output of the expectation-maximization (EM) algorithm for maximum likelihood estimation. In addition to this output, the method requires the first derivative of the posterior probabilities computed by the forward-backward recursions introduced by Baum and Welch. Alternative methods for computing exactly the observed information matrix require, instead, to differentiate twice the forward recursion used to compute the model likelihood, with a greater additional effort with respect to the EM algorithm. The proposed method is illustrated by a series of simulations and an application based on a longitudinal dataset in Health Economics.

LMest: an R package for latent Markov models for categorical longitudinal data

Abstract: Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data (Bartolucci et al., 2013), especially when response variables are categorical. These models have a great potential of application for the analysis of social, medical, and behavioral data as well as in other disciplines. We propose the R package LMest, which is tailored to deal with these types of model. In particular, we consider a general framework for extended LM models by including individual covariates and by formulating a mixed approach to take into account additional dependence structures in the data. Such extensions lead to a very flexible class of models, which allows us to fit different types of longitudinal data. Model parameters are estimated through the expectation-maximization algorithm, based on the forward-backward recursions, which is implemented in the main functions of the package. The package also allows us to perform local and global decoding and to obtain standard errors for the parameter estimates. We illustrate its use and the most important features on the basis of examples involving applications in health and criminology.

Ranking scientific journals via latent class models for polytomous item response data

Abstract: Summary We propose a model-based strategy for ranking scientific journals starting from a set of observed bibliometric indicators that represent imperfect measures of the unobserved ‘value’ of a journal. After discretizing the available indicators, we estimate an extended latent class model for polytomous item response data and use the estimated model to cluster journals. We illustrate our approach by using the data from the Italian research evaluation exercise that was carried out for the period 2004–2010, focusing on the set of journals that are considered relevant for the subarea statistics and financial mathematics. Using four bibliometric indicators (IF, IF5, AIS and the h-index), some of which are not available for all journals, and the information contained in a set of covariates, we derive a complete ordering of these journals. We show that the methodology proposed is relatively simple to implement, even when the aim is to cluster journals into a small number of ordered groups of a fixed size. We also analyse the robustness of the obtained ranking with respect to different discretization rules.

A comparison between the g-index and the h-index based on concentration

Abstract: I discuss how, given a certain number of articles and citations of these articles, the h-index and the g-index are affected by the level of concentration of the citations. This offers the opportunity for a comparison between these 2 indices from a new perspective.

cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models

Abstract: We illustrate R package cquad for conditional maximum likelihood estimation of the quadratic exponential (QE) model proposed by Bartolucci and Nigro (2010) for the analysis of binary panel data. The package also allows us to estimate certain modified versions of the QE model, which are based on alternative parametrizations, and it includes a function for the pseudo conditional likelihood estimation of the dynamic logit model, as proposed by Bartolucci and Nigro (2012). We also illustrate a reduced version of this package that is available in Stata. The use of the main functions of this package is based on examples using labor market data.

Composite likelihood inference for hidden Markov models for dynamic networks

Abstract: We introduce a hidden Markov model for dynamic network data where directed relations among a set of units are observed at different time occasions. The model can also be used with minor adjustments to deal with undirected networks. In the directional case, dyads referred to each pair of units are explicitly modelled conditional on the latent states of both units. Given the complexity of the model, we propose a composite likelihood method for making inference on its parameters. This method is studied in detail for the directional case by a simulation study in which different scenarios are considered. The proposed approach is illustrated by an example based on the well-known Enron dataset about email exchange.

A latent Markov model from a new perspective with an application

Abstract: We propose the use of the latent Markov model in a context of the estimation of multiple causal effects when dealing with observational studies and there are unobserved baseline differences between individuals. The proposed model, tailored for longitudinal data analysis in its basic formulation, has been first introduced by Wiggins (1951) and then formalized in his Ph.D thesis, Wiggins (1955). In Bartolucci et al. (2013) several extensions of the first basic formulation are given and new models have been proposed. The fact that the assumptions encoded by the model may be represented with the help of a path diagram contributes to make such class of models a powerful tool for the analysis of statistical data. In fact, as stated in Pennoni (2014), such models may be seen as built on the foundation of graphical causal models first proposed by Wright (1921) in genetics. Many statistical models tailored for the estimation of the causal effects have been proposed from that period. The potential outcome framework resulted to be one of the most useful tool. However, in a longitudinal setting the latter it is not still well developed as well as for some powerful models developed in the econometric context, see also Romeo (2014). Building on the foundation of the above models and on a recent proposal of Lanza et al. (2013), we introduce a new use of the propensity score weighting (Rosenbaum and Rubin, 1983) when dealing with a multivariate responses observed at multiple time occasions. We show some assumptions which have to be sustainable for the use of the proposed approach in the context of study. The use of the latent Markov model helps to get a reliable estimate of the average causal effect. An interesting feature of the proposed approach is its flexibility given by the adopted parameterization which allows us to deal with any kind of response variable. The model is fitted by a maximum likelihood estimation procedure based on first estimating a multinomial logit model for the probability of taking each type of treatment given suitably chosen pretreatment covariates. Then, a weighted log-likelihood of the LM model, with weights computed on the basis of the estimates computed at the previous step, is maximized so as to obtain final parameter estimates. This second step relies on the EM algorithm (Baum et al., 1970; Dempster et al., 1977) and reliable standard errors for the model parameters are obtained by using a nonparametric bootstrap method (Davison and Hinkley, 1997). The proposed application is particularly suitable to show the model formulation as that it concerns the evaluation of human capital development which is related to a critical period of ∗Presented at the meeting of the FIRB (“Futuro in ricerca” 2012) project “Mixture and latent variable models for causal-inference and analysis of socio-economic data”, Roma (IT), January 01-23, 2015

A misspecification test for finite-mixture logistic models for clustered binary and ordered responses

Abstract: An alternative to using normally distributed random effects in modeling clustered binary and ordered responses is based on using a finite-mixture. This approach gives rise to a flexible class of generalized linear mixed models for item responses, multilevel data, and longitudinal data. A test of misspecification for these finite-mixture models is proposed which is based on the comparison between the Marginal and the Conditional Maximum Likelihood estimates of the fixed effects as in the Hausman’s test. The asymptotic distribution of the test statistic is derived; it is of chi-squared type with a number of degrees of freedom equal to the number of covariates that vary within the cluster. It turns out that the test is simple to perform and may also be used to select the number of components of the finite-mixture, when this number is unknown. The approach is illustrated by a series of simulations and three empirical examples covering the main fields of application.

Latent variable models for the analysis of socio-economic data

Abstract: The present Special Issue is intended to cover statistical approaches based either on continuous or discrete latent variable models, with a particular focus on approaches combining the use of both types of latent variable within the same model. The aim is to give a (partial) picture of recent developments about these models and their applications in the social and economic sciences. As it is well known, the term “latent variables” refers to variables that are not directly observable but are assumed to affect the observable ones in different ways. Latent variables are typically included in a statistical model to make it more flexible and, in particular, for:

Robust maximum likelihood estimation of latent class models

Abstract: We develop a suitable reweighting approach to deal with outliers when maximum likelihood estimation is used to estimate latent class models. In such a context, the EM algorithm is used and the presence of outliers and spurious observations is common. The Proposed method is motivated by an application aimed at finding clusters of offending behaviours.

Composite likelihood inference in a discrete latent variable model for two-way "clustering-by-segmentation" problems

Abstract: We consider a discrete latent variable model for two-way data arrays, which allows one to simultaneously produce clusters along one of the data dimensions (e.g. exchangeable observational units or features) and contiguous groups, or segments, along the other (e.g. consecutively ordered times or locations). The model relies on a hidden Markov structure but, given its complexity, cannot be estimated by full maximum likelihood. We therefore introduce composite likelihood methodology based on considering different subsets of the data. The proposed approach is illustrated by simulation, and with an application to genomic data.