The growing and progressive decrease in voter turnout affects almost all established democracies and all types of electoral consultations, albeit with different intensities. The aim of the paper is to identify which cultural and socio-economic factors may contribute to explain the voter turnout in the 2022 Italian parliamentary election. The determinants of non-voting are identified using data from the European Social Survey through a graphical modeling approach based on Bayesian networks.

A major concern with the use of non-probability samples is their lack of representativeness that, if not accounted for properly, may lead to large bias in survey estimates. Non-probability samples involve subjective methods for sample selection, so that inclusion probabilities are unknown and it is not possible to apply the traditional randomization theory for inference on the population parameters. In this paper, the uncertainty in survey estimates resulting from the non-identifiability of the sampling design acting in the non-probability sample, as well as its reduction due to availability of extra-sample information, is discussed. Next, the effect of non-identifiability on survey estimates accuracy is evaluated. Finally, an application to real enterprise data from Italy is performed.

The interrater agreement of ratings on a nominal scale regarding a group of targets (individuals, objects, etc.) is usually computed by kappa-type indices. Recently, a homogeneity index for a qualitative variable was considered to define a single target measure of agreement between raters for each target, and to propose a global measure of agreement for the whole group of targets allowing to overcome limitations affecting traditional kappa-type indices. In this paper, the sampling properties and the asymptotic distribution of the proposed index are investigated. Finally, a simulation study is performed to demonstrate the accuracy of the index and an application to real educational data is provided.

Non-voting affects all types of electoral consultations, albeit with different intensities. The electoral abstention has become an emergency since citizen participation is fundamental to the legitimacy of the representative system and the proper functioning of a democracy. The aim of the paper is to identify by a graphical modeling approach which cultural and socio-economic factors may contribute to explain the non-vote rate in the 2022 parliamentary election.

The extent of agreement between raters on nominally scaled data regarding a group of targets is usually computed by kappa-type indices. Recently, the homogeneity index for a qualitative variable was proposed to evaluate agreement between raters for each target, and to obtain a global measure of interrater agreement for the whole group of targets. In this presentation, some sampling properties of the proposed indices are investigated and a comparative application regarding medical data is provided.

Statistical matching aims to combine samples drawn from the same population, where each sample contains information only on some of the variables of interest. The lack of joint observations produces uncertainty about the data-generating model. The paper is devoted to the study of uncertainty in statistical matching for complex sample surveys when a proxy variable is only available in one sample. Such a variable can be used both to verify the conditional independence assumption and to provide a set of plausible estimates of the distribution of variables not jointly observed when such an assumption is not satisfied. Finally, a simulation study is performed and an application to integrate objective and subjective well-being measures is provided.

A major concern with the use of non-probability samples is their non-representativeness, which if not accounted properly, may led to large bias in the inference process. The question arising therefore is how to draw inference from such samples, regarding the population that they are believed to represent. In this paper the concept of uncertainty on data generating model, resulting from the lack of knowledge of the sampling design acting in the non-probability sample, is introduced. Furthermore, the reduction of uncertainty due to the availability of extra-sample information is discussed. Finally, the effect of the lack of identifiability on the accuracy of survey estimates is evaluated.

Non-probability samples involve some form of arbitrary selection of units into the sample, and, as a matter of fact, inclusion probabilities are unknown. Hence, it is not possible to apply probability randomization theory to make inference about the finite population parameters. In this paper the concept of uncertainty on data generating model, resulting from the lack of knowledge of the sampling design acting in the non-probability sample is discussed. A measure of uncertainty is introduced and its asymptotic proprieties are evaluated.

The main issue with non-probability samples is that the standard design-based approach cannot be applied as the selection mechanism is unknown. In this paper, the concept of uncertainty on data generating model, resulting from the lack of knowledge of the sampling design acting in the non-probability sample, is discussed. Furthermore, the effect on uncertainty due to the availability of extra-sample information is evaluated. First of all, the class of plausible distributions for the variable of interest is defined, a measure of uncertainty is introduced and its asymptotic properties are analysed. Next, a plausible estimate of the distribution of the variable of interest is constructed and its accuracy is evaluated. Finally, a simulation study is performed, and an application to a real case is provided.

In this paper measures of interrater absolute agreement for quantitative measurements based on the standard deviation are proposed. Such indices allow (i) to overcome the limits affecting the intraclass correlation index; (ii) to measure the interrater agreement on single targets. Estimators of the proposed measures are introduced and their sampling properties are investigated for normal and non-normal data. Simulated data are employed to demonstrate the accuracy and practical utility of the new indices for assessing agreement. Finally, an application to assess the consistency of measurements performed by radiologists evaluating tumor size of lung cancer is presented.

Data for statistical analysis is often available from different samples, with each sample containing measurements on only some of the variables of interest. Statistical matching attempts to generate a fused database containing matched measurements on all the target variables. In this article, we consider the use of statistical matching when the samples are drawn by informative sampling designs and are subject to not missing at random non-response. The problem with ignoring the sampling process and non-response is that the distribution of the data observed for the responding units can be very different from the distribution holding for the population data, which may distort the inference process and result in a matched database that misrepresents the joint distribution in the population. Our proposed methodology employs the empirical likelihood approach and is shown to perform well in a simulation experiment and when applied to real sample data.

Large amount of data are today available, that are easier and faster to collect than survey data, bringing new challenges. One of them is the nonprobability nature of these big data that may not represent the target population properly and hence result in highly biased estimators. In this article two approaches for dealing with selection bias when the selection process is nonignorable are discussed. The first one, based on the empirical likelihood, does not require parametric specification of the population model but the probability of being in the nonprobability sample needed to be modeled. Auxiliary information known for the population or estimable from a probability sample can be incorporated as calibration constraints, thus enhancing the precision of the estimators. The second one is a mixed approach based on mass imputation and propensity score adjustment requiring that the big data membership is known throughout a probability sample. Finally, two simulation experiments and an application to income data are performed to evaluate the performance of the proposed estimators in terms of robustness and efficiency.

Nowadays there is increasing availability of good quality official statistics data. The construction of multivariate statistical models possibly leading to the identification of causal relationships is of interest. In this context Bayesian networks play an important role. A crucial step consists in learning the structure of a Bayesian network. One of the most widely used procedures is the PC algorithm consisting in carrying out several independence tests on the available data set and in building a Bayesian network according to the tests results. The PC algorithm is based on the irremissible assumption that data are independent and identically distributed. Unfortunately, official statistics data are generally collected through complex sampling designs, then the aforementioned assumption is not met. In such a context the PC algorithm fails in learning the structure. To avoid this, the sample selection must be taken into account in the structural learning process. In this paper, a modified version of the PC algorithm is proposed for inferring causal structure from complex survey data. It is based on resampling techniques for finite populations. A simulation experiment showing the robustness with respect to departures from the assumptions and the good performance of the proposed algorithm is carried out.

Many methods for measuring agreement among raters have been proposed and applied in many domains in the areas of education, psychology, sociology, and medical research. A brief overview of the most used measures of interrater absolute agreements for ordinal rating scales is provided, and a new index is proposed that has several advantages. In particular, the new index allows to evaluate the agreement between raters for each single case (subject or object), and to obtain also a global measure of the interrater agreement for the whole group of cases evaluated. The possibility of having evaluations of the agreement on the single case is particularly useful, for example, in situations where the rating scale is being tested, and it is necessary to identify any changes to it, or to request the raters for a specific comparison on the single case in which the disagreement occurred. The index is not affected by the possible concentration of ratings on a very small number of levels of the ordinal scale.

Many methods for measuring agreement among raters have been proposed and applied in many domains in the areas of education, psychology, sociology, and medical research. A brief overview of the most used measures of interrater absolute agreements for ordinal rating scales is provided, and a new index is proposed that has several advantages. In particular, the new index allows to evaluate the agreement between raters for each single case (subject or object), and to obtain also a global measure of the interrater agreement for the whole group of cases evaluated. The possibility of having evaluations of the agreement on the single case is particularly useful, for example, in situations where the rating scale is being tested, and it is necessary to identify any changes to it, or to request the raters for a specific comparison on the single case in which the disagreement occurred. The index is not affected by the possible concentration of ratings on a very small number of levels of the ordinal scale.

The goal of statistical matching, at a macro level, is the estimation of the joint distribution of variables separately observed in independent samples. The lack of joint information on the variables of interest leads to uncertainty about the data generating model. In this paper we propose the use of graphical models to deal with the statistical matching uncertainty for multivariate categorical variables. The use of Bayesian networks in the statistical matching context allows both to introduce extra sample information on the dependence structure between the variables of interest and to use such an information to factorize the joint probability distribution according to the graph decomposition of a multivariate dependence in lower dimension components. This representation of the joint probability distribution, taking advantage of local relationships, allows to simplify both parameters estimation and statistical matching quality evaluation in a multivariate context. A simulation experiment is performed in order to evaluate the performance of the proposed methodology with and without auxiliary information, as well as to compare it with the saturated multinomial model, in terms of uncertainty reduction. Finally, an application to a real case is provided. Results show a considerable improvement in the quality of statistical matching when the dependence structure is taken into account.

Statistical matching attempts to combine the information obtained from different, non-overlapping samples, selected from the same target population, to form a matched sample containing the data in the different samples. The aim of this paper is to propose a nonparametric approach of handling statistical matching under informative sampling and not missing at random (NMAR) nonresponse, by use of empirical likelihood.

A measure of interrater absolute agreement for ordinal scales is proposed capitalizing on the dispersion index for ordinal variables proposed by Giuseppe Leti. The procedure allows to overcome the limits affecting traditional measures of interrater agreement in different fields of application. An unbiased estimator of the proposed measure is introduced and its sampling properties are investigated. In order to construct confidence intervals for interrater absolute agreement both asymptotic results and bootstrapping methods are used and their performance is evaluated. Simulated data are employed to demonstrate the accuracy and practical utility of the new procedure for assessing agreement. Finally, an application to a real case is provided.

This exploratory survey concerns the innovation of e-learning systems through the individualization of forms of tutoring, the development of specific professional skills - essential in tertiary education -, and the increase in affective usability with which intends to create a good virtual immersion experience with easy accessibility and positive emotional and attentional involvement. In order to investigate the needs and expectations for a better qualification of virtual learning environments and online didactic, we administered the "Questionnaire on the evaluation of the quality of the educational experience" to the students of the Degree in an online degree program of Roma Tre University. We distinguish assessments provided by young adult students aged 18 to 32 years, mature adults aged 33 to 45 years, and senior adults aged 46 to 58 years. The results indicate the importance attributed in particular by the younger age groups (from 18 to 45 years) to the role of tutor as mediator. The expected professional skills include the request to develop the "individualization capacity of the teaching" and the "organizational and communicative-relational skills". There are also specific expectations of enhancing the "affective usability" of virtual learning environments. Thanks to these forms of innovation of e-learning systems, it will be possible to promote online attention and learning processes.