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Exploring the Patterns of Job Satisfaction for Individuals Aged 50 and over from Three Historical Regions of Romania. An Inductive Approach with Respect to Triangulation, Cross-Validation and Support for Replication of Results

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Department of Accounting, Business Information Systems and Statistics, Faculty of Economics and Business Administration (FEAA), Alexandru Ioan Cuza University of Iași (UAIC), Carol I Boulevard, No.22, 700505 Iași, Romania
2
Institute of Interdisciplinary Research, Department of Social Sciences and Humanities, UAIC, Lascar Catargi Street, No.54, 700107 Iași, Romania
3
Department of Management, Marketing and Business Administration, FEAA, UAIC, Carol I Boulevard, No.22, 700505 Iași, Romania
4
Department of Finance, Currency and Public Administration, FEAA, UAIC, Carol I Boulevard, No.22, 700505 Iași, Romania
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(7), 2573; https://doi.org/10.3390/app10072573
Received: 7 March 2020 / Revised: 26 March 2020 / Accepted: 7 April 2020 / Published: 9 April 2020
In this paper, we explore the determinants of being satisfied with a job, starting from a SHARE-ERIC dataset (Wave 7), including responses collected from Romania. To explore and discover reliable predictors in this large amount of data, mostly because of the staggeringly high number of dimensions, we considered the triangulation principle in science by using many different approaches, techniques and applications to study such a complex phenomenon. For merging the data, cleaning it and doing further derivations, we comparatively used many methods based on spreadsheets and their easy-to-use functions, custom filters and auto-fill options, DAX and Open Refine expressions, traditional SQL queries and also powerful 1:1 merge statements in Stata. For data mining, we used in three consecutive rounds: Microsoft SQL Server Analysis Services and SQL DMX queries on models built involving both decision trees and naive Bayes algorithms applied on raw and memory consuming text data, three LASSO variable selection techniques in Stata on recoded variables followed by logistic and Poisson regressions with average marginal effects and generation of corresponding prediction nomograms operating directly in probabilistic terms, and finally the WEKA tool for an additional validation. We obtained three Romanian regional models with an excellent accuracy of classification (AUROC > 0.9) and found several peculiarities in them. More, we discovered that a good atmosphere in the workplace and receiving recognition as deserved for work done are the top two most reliable predictors (dual-core) of career satisfaction, confirmed in this order of importance by many robustness checks. This type of meritocratic recognition has a more powerful influence on job satisfaction for male respondents rather than female ones and for married individuals rather unmarried ones. When testing the dual-core on respondents aged 50 and over from most of the European countries (more than 75,000 observations), the positive surprise was that it undoubtedly resisted, confirming most of our hypotheses and also the working principles of support for replication of results, triangulation and the golden rule of robustness using cross-validation. View Full-Text
Keywords: job satisfaction; data mining (DM); decision trees; naive Bayes and LASSO; logistic and Poisson regressions with average marginal effects; prediction nomograms job satisfaction; data mining (DM); decision trees; naive Bayes and LASSO; logistic and Poisson regressions with average marginal effects; prediction nomograms
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Homocianu, D.; Plopeanu, A.-P.; Florea, N.; Andrieș, A.M. Exploring the Patterns of Job Satisfaction for Individuals Aged 50 and over from Three Historical Regions of Romania. An Inductive Approach with Respect to Triangulation, Cross-Validation and Support for Replication of Results. Appl. Sci. 2020, 10, 2573.

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