## 3 Data and Methods  
This research started from the questions of the SHARE-ERIC (Survey of Health, Ageing and Retirement in Europe, www.share-project.org/home0.html), located at MEA (Munich Center for the Economics of Aging), Max-Planck-Institute for Social Law and Social Policy, Munich, Germany, also considered as the first ever (2006) European Research Infrastructure Consortium (ERIC). In 2017, SHARE-ERIC collected for this project 76,520 observations, including more than 2000 for Romanian citizens aged 50 or more, from which we were interested in a selection of almost 1700 questions (from a total of 7700), originally grouped in eight categories, namely: AC--Activities; CC--Childhood Circumstances; DN--Demographics; GV_BIG5--Personality traits; RA--Retrospective Accommodation; RE--Retrospective Employment; WQ--Work Quality; GV_ISCED--ISCED standards for classifying education (doi:10.6103/SHARE.w7.700, Stata version, the _sharev7_rel7-0-0_ALL_datasets_stata.zip_ file of 69.82 MB, uploaded by SHARE-ERIC on 3 rd of April 2019). Therefore, our mission to explore and discover reliable predictors in such a large amount of data, mostly because of the high number of afferent dimensions, was a challenging one. We considered several scientific principles in order to foster the robustness of our research inquiry for better credibility and trustworthiness. One of them, the triangulation principle, means to rely on many different, but convergent approaches, techniques and tools in order to properly investigate any complex phenomenon. A summary of the methodology used was synthesized below:  
* SHARE-ERIC Wave 7 dataset (only 8 categories of variables in 8 different files merged and used; only responses of respondents aged 50 or more, from Romania and with the declared country region for their residence);
* Both decision trees and naive Bayes (NB) approaches have been used in Microsoft SQL Server Analysis Services (only the primary outcome was pre-processed: wq727_ became wq727 bin34--Table A1, Appendix A); NB's dependency network in this first round mining acted as support for variables pre-selection;
* Variables selected at previous steps and some others of interest (mostly individual traits) have been processed (Table A1, Appendix A) based on a balanced approach of treating missing responses;
* Different regional splits and some meant for robustness checks have been done;
* Second round mining was applied (LASSOPACK package in Stata) on the processed variables (previous steps);* Logistic and Poisson regressions with robust standard errors have been used and further robustness confirmations of the dual-core and additional selections for regional models have been performed based on p, VIF, R-squared, ROC and GOF test values;
* Average marginal effects have been computed and Zlotnik probability prediction nomograms were generated for the regional models;
* Third round mining using WEKA was performed only for an additional validation;
* Additional regressions to confirm the already identified dual-core at a higher level (almost all European countries) have been performed by following an inductive approach. For merging the divided sources (the eight aforementioned categories), we have used comparatively SQL queries involving the INNER JOIN clause in Access from Microsoft - [PERSON], Washington, USA (after previously importing the.csv exports of Stata.dta files), join statements in Stata 16 64 bit Multiprocessing/Parallel Edition (MP), the Open Refine tool and Cell. Cross GREL (The Refine Expression Language of Google - Mountain View, California, USA) statements and also the DAX (Data Analysis eXpression) language function extension for spreadsheets, available after installing the Power Pivot add-in from Microsoft (more precisely, the RELATED function, simpler and easier to use than the traditional time consuming VLOOKUPs). For cleaning the data and performing further derivations, we also relied on all the tools above.