Search Results (3)

This paper presents the results of an exploration of the most resilient influences determining the attitude regarding prioritizing co-nationals over immigrants for access to employment. The source data were from the World Values Survey. After many selection and testing steps, a set of the seven most significant determinants was produced (a fair-to-good model as prediction accuracy). These seven determinants (a hepta-core model) correspond to some features, beliefs, and attitudes regarding emancipative values, gender discrimination, immigrant policy, trust in people of another nationality, inverse devoutness or making parents proud as a life goal, attitude towards work, the post-materialist index, and job preferences as more inclined towards self rather than community benefits. Additional controls revealed the significant influence of some socio-demographic variables. They correspond to gender, the number of children, the highest education level attained, employment status, income scale positioning, settlement size, and the interview year. All selection and testing steps considered many principles, methods, and techniques (e.g., triangulation via adaptive boosting (in the Rattle library of R), and pairwise correlation-based data mining—PCDM, LASSO, OLS, binary and ordered logistic regressions (LOGIT, OLOGIT), prediction nomograms, together with tools for reporting default and custom model evaluation metrics, such as ESTOUT and MEM in Stata). Cross-validations relied on random subsamples (CVLASSO) and well-established ones (mixed-effects). In addition, overfitting removal (RLASSO), reverse causality, and collinearity checks succeeded under full conditions for replicating the results. The prediction nomogram corresponding to the most resistant predictors identified in this paper is also a powerful tool for identifying risks. Therefore, it can provide strong support for decision makers in matters related to immigration and access to employment. The paper’s novelty also results from the many robust supporting techniques that allow randomly, and non-randomly cross-validated and fully reproducible results based on a large amount and variety of source data. The findings also represent a step forward in migration and access-to-job research. Full article

The paper describes PCDM and PCDM4MP as new tools and commands capable of exploring large datasets. They select variables based on identifying the absolute values of Pearson’s pairwise correlation coefficients between a chosen response variable and any other existing in the dataset. In addition, for each pair, they also report the corresponding significance and the number of non-null intersecting observations, and all this reporting is performed in a record-oriented manner (both source and output). Optionally, using threshold values for these three as parameters of PCDM, any user can select the most correlated variables based on high magnitude, significance, and support criteria. The syntax is simple, and the tools show the exploration progress in real-time. In addition, PCDM4MP can trigger different instances of Stata, each using a distinct class of variables belonging to the same dataset and resulting after simple name filtering (first letter). Moreover, this multi-processing (MP) version overcomes the parallelization limitations of the existing parallel module, and this is accomplished by using vertical instead of horizontal partitions of large flat datasets, dynamic generation of the task pattern, tasks, and logs, all within a single execution of this second command, and the existing qsub module to automatically and continuously allocate the tasks to logical processors and thereby emulating with fewer resources a cluster environment. In addition, any user can perform further selections based on the results printed in the console. The paper contains examples of using these tools for large datasets such as the one belonging to the World Values Survey and based on a simple variable naming practice. This article includes many recorded simulations and presents performance results. They depend on different resources and hardware configurations used, including cloud vs. on-premises, large vs. small amounts of RAM and processing cores, and in-memory vs. traditional storage. Full article

The privacy and security of online transactions and information exchange has always been a critical issue of e-commerce. However, there is a certain level of tolerance (a share of 36%) when it comes to so-called governments’ rights to monitor electronic mail messages and other information exchange as resulting from the answers of respondents from 51 countries in the latest wave (2017–2020) of the World Values Survey. Consequently, the purpose of this study is to discover the most significant influences associated with this type of tolerance and even causal relationships. The variables have been selected and analyzed in many rounds (Adaptive Boosting, LASSO, mixed-effects modeling, and different regressions) with the aid of a private cloud. The results confirmed most hypotheses regarding the overwhelming role of trust, public surveillance acceptance, and some attitudes indicating conscientiousness, altruistic behavior, and gender discrimination acceptance in models with good-to-excellent classification accuracy. A generated prediction nomogram included 10 ten most resilient influences. Another one contained only 5 of these 10 that acted more as determinants resisting reverse causality checks. In addition, some sociodemographic controls indicated significant variables afferent to the highest education level attained, settlement size, and marital status. The paper’s novelty stands on many robust techniques supporting randomly and nonrandomly cross-validated and fully reproducible results based on a large amount and variety of evidence. The findings also represent a step forward in research related to privacy and security issues in e-commerce. Full article