## 4 Results and Discussion  
In terms of merging the original vertically divided data subsets, the first method (based on SQL queries, followed by exports) was apparently the handiest one, except for the limitation of 255 columns per table. This was the reason of finally choosing 1:1 merge statements in Stata 16. For cleaning the data and performing additional derivations, in most cases, the spreadsheets with their immediate visual feedback and insight, powerful built-in functions (e.g., UNIQUE in Google Sheets to check uniqueness of values for a field, cascade calls of IFs to find the region of the last residence, COUNTA to get the number of non-blank distinct jobs) and user-defined ones, customisable filters using the CONTAINS option for identifying particular text patterns, and fast auto-fill and split into columns (text to columns) facilities, seemed enough to perform manual cleaning tasks. First, we filtered the resulting dataset, corresponding to Wave 7 (2017), by considering the original field _W7-ac_country_ set with the value of \"Romania\", for which we obtained 2144 unique records. Then, we set the variable _dn003_ (_birth year_) on less than or equal to 1967, in order to indicate only those responses of Romanian people aged fifty or over (2056 records). Next, in order to identify the specific subsets corresponding to those three large regions in terms of last declared residence, we started from those 2056 filtered records above and used a cascade of IFs to generate the last non-blank residence, by considering only the first 17 related fields (from _ra025c_1_ to _ra025c_17_) from a total of 30 and excluding the last 13 (due to containing only blank values): e.g., =IF(SH2=\"\",IF(SG2=\"\",..IF(RS2=\"\",\"RR2),RS2),...,SG2),SH2). Thus, we have obtained 2052 records corresponding to 330 observations for MB, 793 observations for TBCM and 929 for OMD. The difference to the previous filtered amount of 2056 records consisted in only four observations with unspecified last residence, which therefore have been dropped. After the first round DM in SSAS, we successfully extracted 18 variables (Table 11, Appendix A and Figure 1) considered to be possible important predictors for our chosen outcome, namely job satisfaction. The slider used to adjust the strongest links level and show the corresponding results was captured and included in Figure 1. As seen, the algorithm based on naive Bayes has automatically identified especially the variables from the same category to which the outcome belongs to (questions on work quality--wq), as the most important predictors of job satisfaction. Moreover, we have tested the one related to our first hypothesis (H1) using DMX queries on persistent DM models trained differently (100% in Figure 1, 85% and 75% of all data). The DMX query's result in Figure A1, Appendix B indicates a powerful influence of the variable associated with a question asking about the recognition of work done (Table A1, Appendix A), meaning that the individuals who have received recognition considered as deserved for the effort done are more satisfied with their own job. This variable's overwhelming influence on job satisfaction is explainable when admitting the holistic nature of the underlying question. Still, we cannot state that our first hypothesis, concerning the deserved recognition for the work done, is validated until performing further statistical analysis. Figure 1: The most important predictor of job satisfaction (Decision Trees—first split) and top 10 strongest ones (naive Bayes—the shorter the arrow, the stronger the relation according to the vertical slider), all identified in the first round DM in SSAS, when analysing job satisfaction.