## 3 Data and Methods
Actually, three types of LASSO have been tested in Stata (the LASSO package):  
* CV LASSO (a time-consuming approach based on cross-validation; we have used it with 10 folds, elastic net with alpha = 1 and two options for post-estimation: lopt--lambda that minimizes the mean squared prediction error/MSPE and lse--largest lambda for which MSPE is within one standard error of the minimal MSPE);
* LASSO 2 (a faster approach centred on information criteria; we have used it with alpha = 1, followed by a post-estimation to select the best value of lambda when considering the Extended Bayesian Information Criteria--EBIC);
* LASSO (an even faster but more rigorous/penalizing approach focused on controlling over-fitting; we have used it on default settings). In order to analyze the determinant factors that influence the probability of being satisfied with a job in our proposed models, we have started from the sets of variables obtained from the second round DM and selection (based on LASSO), which had as the input the set resulting from the first round (based on naive Bayes in SSAS), only after some processing steps described in Table 11, Appendix A (questionnaire item and coding columns). Next, we used a well-known generic econometric model (Equation (1)), namely the binary logistic regression model [98] as the particular case of multinomial logistic regression [99]. \[\text{Logit}(\text{p})=\text{ln}\bigg{(}\frac{\text{p}}{1-\text{p}}\bigg{)}= \beta_{0}\ +\sum_{i=1}^{n}\beta_{i}\ast\text{X}_{i}+\varepsilon \tag{1}\]  
where:  
* p is the probability of being satisfied with the job;
* \((1-\text{p})\) is the probability of not being satisfied with it;
* p/(\(1-\text{p}\)) represents the odds of being satisfied with the job;
* i = 2,.., n and n is the total number of independent variables;
* \(\delta_{0}\) is the bias (intercept) term;
* \(\delta_{1}\) measures the effect of a change in variable Xi on the probability of being satisfied with the job;
* X\({}_{i}\) is one explanatory variable from the array (\(\sum\)) of features selected after using LASSO;
* \(\varepsilon\) represents the error term. The entire statistical analysis, including all regressions and post-estimations, was performed using the Stata 16.0 64 bit MP. The binary logistic regressions served both as support for the robustness checks of the dual-core and the confirmation of particular regional influences. After performing these regressions, we filtered the remaining influences, resulting after applying LASSO, with respect to: p values, as the results of comparing errors and coefficients; VIF (Variance Inflation Factor) ones, by considering the lower, the better and proofs of collinearity for values larger than 10 [100]; results of goodness-of-fit (GOF) tests [101], by taking into account the higher, the better both for p values (to reject the null hypothesis) and chi square; values for the area under the curve of receiver operating characteristic, known as AUCROC, AUROC or, shortly, ROC [102], by taking into account the higher, the better in terms of accuracy of classification for a scenario/model; R-square values, by applying the same simple rule, as in the case of AUCROC type. For these three regions, we have also generated Zlotnik probability prediction nomograms [103], starting from the results when applying the binary logistic regressions--Equation (1). Moreover, we have tested the previous regional results using modified Poisson regressions for the binary data [104]with robust error variance. Such regressions support interpretations directly in terms of relative risks and not odds ratios treated as relative risks which, except for rare phenomena, may lead to undesirable exaggerations [105]. Additionally (third round DM), we have confirmed the results obtained using Microsoft SSAS (the core influences) and Stata (the core influences and the additional ones for all three regional models) also by involving many techniques in WEKA (Waikato Environment for Knowledge Analysis), version 3.8.3 x64.