## 5 Conclusions  
The careful treatment of data, the principle of support for replication of the results through transparent approaches and methods, the triangulation principle in terms of the many approaches, techniques and tools used as a basic principle in research, together with the golden rule of cross-validation, as a reliable way of testing for data randomly and non-randomly divided in subsets, all these allowed us to better explore this complex phenomenon. We also extended the conclusions regarding the first two most powerful predictors (which composed the already identified dual-core of the Romanian regional models) to a higher level, namely for the individuals aged 50 and over from most of the European countries. In addition, in this large-scale example, we have demonstrated that different ways of treating missing data may lead to major differences in terms of the magnitude of influences, collinearity, accuracy of classification and explanatory power of the resulting models. We found that a good atmosphere in the workplace, because of a healthy relationship with colleagues, together with the desire to receive the recognition deserved for work done, are the most important predictors of job satisfaction in the case of Romanians aged 50 and over. We revealed these findings when we analyzed both full and/or partial job satisfaction. We have also identified some peculiarities for the three Romanian historical regions analyzed and found that, in all cases, the atmosphere in the workplace seems decisive and counts more than the deserved recognition for the work done there. Moreover, this type of recognition has a more powerful influence on job satisfaction in the case of individuals who graduated at least high school, for married persons and for male respondents. **Author Contributions:** Conceptualization, A.-P.P., D.H., N.F. and AM.A. Data curation, A.-P.P. and D.H. Formal analysis, A.-P.P. and D.H. Investigation, A.-P.P. and D.H. Methodology, A.-P.P. and D.H. Project administration, A.-P., D.H. and A.M.A. Resources, A.-P.P. and A.M.A. Software, A.-P.P. and D.H. Supervision, A.M.A. Validation, A.-P., D.H., N.F. and A.M.A. Visualization, D.H. Writing--original draft, A.-P.P. and D.H. Writing--review and editing, A.-P.P., D.H., N.F. and A.M.A. All authors have read and agree to the published version of the manuscript. **Funding:** This project is funded by the Ministry of Research and Innovation from Romania within Program 1 - Development of the national RD system, Subprogram 1.2 - Institutional Performance - RDI excellence funding projects, Contract No.34 PFE/19.10.2018. **Acknowledgments:** For allowing us to explore the dataset for Romania (doi:10.6103/SHARE.w7.70) and publish this paper, we would like to thank the SHARE-ERIC consortium and supporting projects financed by the European Commission through: the 5 th framework programme (project QLK6-CT-2001-00360, Quality of Life thematic programme); the 6 th framework programme (projects: SHARE-I3, RII-CT-2006-062193, as an Integrated Infrastructure Initiative; COMPARE, CIT5-CT-2005-028857, as a project in Priority 7, namely Citizens and Governance in a Knowledge Based Society, and SHARE-LIFE, CIT4-CT-2006-028812); the 7 th framework programme (SHARE-PREP (No 211909), SHARE-LEAP (No 227822) and M4 (No 261982)); the Horizon 2020 (SHAREDEV3 (No 676536), SERISS (No 654221), SSHOC (No 823782), RItrain (No 654156) and ERIC Forum (No 823798)). We also thank the Ministry of Research and Innovation from Romania for funding (contract above). **Conflicts of Interest:** The authors declare no conflict of interest. \begin{table}
\begin{tabular}{l l l l} \hline
**VARIABLE 1 ST** & \multicolumn{2}{c}{**VARIABLE NAME AFTER**} & \multicolumn{1}{c}{**QUESTIONNAIRE ITEM**} & \multicolumn{1}{c}{**CODING**} \\
**NAME** & \multicolumn{2}{c}{**PROCESING**} & \\ \hline  & TOP 10 FROM THE JOB CATECORY IDENTIFIED USING THE NAVE BATES TECHIQUE IN MICROSOFT SSAS & \\ \hline \(\mu\)002\_ & _phys\_demant\_job_ & Was your job physically demanding?