Life Satisfaction: Insights from the World Values Survey

: This paper explores enduring influences on life satisfaction using empirical analysis of World Values Survey (WVS) data (four versions of the most comprehensive dataset, namely 1.6, 2.0, 3.0 and 4.0). Five significant values emerged—financial satisfaction, happiness, freedom of choice, health, and democracy. Through rigorous selection processes and various statistical techniques, a subset of three determinants resulted, along with consecrated socio-demographic variables such as age, gender, marital status, social class, and settlement size. Advanced methodologies such as feature selection, random and non-random cross-validations, overfitting removal, collinearity and reverse causality checks, and different regressions served to evaluate and validate robust models. Nomograms helped to predict life satisfaction probabilities. The findings contribute to understanding life satisfaction dynamics and offer practical insights for future research and policy.


Introduction
Life satisfaction seems to be a complex and subjective concept that can vary greatly from person to person and can depend on many factors such as personal values, relationships, health, financial situation and stability, and life experiences.As evidence of its subjective specificity, the related concept of "subjective well-being" was also considered and discussed as most dependent on perspective [1], the relationship between the desired and the real [2], and, ultimately, cultural differences [3] and how individuals understand concepts such as life meaning and subjective well-being [4].While some people find happiness in money [5] and material possessions [6][7][8], others find it in spiritual or emotional fulfillment [9].Life satisfaction does not seem to be under the governance of a single formula.Moreover, its attainment varies from person to person, and individuals must discern what brings them fulfillment and happiness and actively pursue those aspects in their personal lives.There could also be general and specific patterns for this satisfaction.As for the first category, a consistent amount of high levels of life satisfaction is reported (a share of almost 70%) as resulting from the answers of respondents from 108 countries in the latest version (v.4.0) of the most comprehensive dataset from the World Values Survey, covering almost the entire period from 1981 to 2022.
The study of life satisfaction is not something new.Historically, this research line can be rooted in the 18th century [10], associated with Enlightenment thought.From this point of view, the purpose of existence is life itself rather than serving the ruler or God.Therefore, self-improvement and happiness become central values in a society responsible for providing citizens with what is necessary for a good life.The same conviction manifested a century later in the form of the Utilitarian Creed that the best society is that which offers the greatest happiness for the highest number of people and inspired large-scale attempts to carry out social reform and influenced the development of the welfare state two centuries later [11,12].The overall progress started with creative efforts to build a better society, translated first into attempts to avoid ignorance, disease, hunger, and poverty, as well as increasing the level of literacy and controlling diseases and epidemics, and later into ways to ensure a good life for all, and a good material standard of living through monetary earnings, income security, and income equality.The latter has given rise to much social research on poverty and social inequality [13,14].Later, the term quality of life emerged in the context of new themes related to the limits to economic growth and post-materialism.
Regarding differences between life satisfaction and happiness [15], the latter is often described as a more momentary and emotional state [16][17][18][19], often influenced by external factors such as events, experiences, or possessions.It can be short-lived and fluctuate frequently.On the other hand, life satisfaction is a more enduring and cognitive evaluation of a particular life as a whole (overall happiness) [20][21][22][23].It encompasses many factors, including an individual's overall sense of purpose, relationships, financial stability, health, etc. Life satisfaction tends to be a more stable and long-term assessment of happiness.It is worth noting that while there is often an overlap between happiness and life satisfaction, they are not the same thing and can exist independently of each other.One can feel happy at a particular moment but still have low life satisfaction or vice versa.
Moreover, life satisfaction, as a key component of well-being, can be categorized into eudaimonic well-being, which emphasizes meaning, self-realization, or excellence, and hedonic well-being, which focuses on pleasure and the avoidance of pain [24].In addition, the Cybernetic Value Fulfillment Theory posits that well-being is the fulfillment of psychologically integrated, nonconflicting values unique to each individual [25].
Other studies also indicate the role of socio-demographic and individual features [26][27][28].They emphasize influences from this category, such as age, gender [29], psychological features, lifestyle, participation in leisure activity, and satisfaction related to spending free time or leisure satisfaction [30].
The disciplinary perspectives that this article takes can be identified as: A-Social Sciences (Psychology and Sociology): (1) Focus on life satisfaction as a psychological construct; (2) Analysis of values such as happiness, freedom of choice, health, and democracy as determinants of life satisfaction; (3) Use of World Values Survey (WVS) data to explore enduring influences on life satisfaction; (4) Incorporation of socio-demographic variables (age, gender, marital status, social class, settlement size) to understand variations in life satisfaction; (5) Contribution to understanding life satisfaction dynamics from a sociological perspective.
B-Economics: (1) Examination of financial satisfaction as a key determinant of life satisfaction; (2) Economic analysis of the impact of financial well-being on overall life satisfaction; (3) Statistical techniques and advanced methodologies (feature selection, regressions, nomograms) used to model and predict life satisfaction probabilities; (4) Practical insights for economic policy based on empirical findings.
C-Statistics and Data Science: (1) Application of advanced statistical techniques (feature selection, random and non-random cross-validation, overfitting removal, collinearity checks, reverse causality checks) to analyze World Values Survey data; (2) Utilization of different regression models to evaluate and validate robust models of life satisfaction; (3) Nomograms as a tool for visualizing and predicting life satisfaction probabilities; (4) Emphasis on rigorous data analysis and methodological approaches in social sciences research.
D-Policy and Governance: (1) Practical implications for policymaking based on insights into determinants of life satisfaction; (2) Implications for democratic governance and social policies related to happiness, freedom of choice, and health; (3) Contribution to evidencebased policy decisions regarding socio-demographic factors affecting life satisfaction.
These disciplinary perspectives collectively highlight the multidimensional approach taken in this article to understand life satisfaction dynamics, combining insights from psychology, sociology, economics, statistics, data science, and policy and governance implications.
The article further reviews the literature on the perceptions related to life satisfaction.Then, it describes the data and methodology used before presenting and discussing the main findings in a dedicated section.The latter captures the focus of the current study, namely the discovery of the most resilient influences related to life satisfaction, and this is achieved by eliminating redundancies after performing many robustness checks in advance.

Related Work
Life satisfaction is more liable to shifts in aspiration level [31] when compared to happiness, thus reducing the comparability of the resulting indices.Moreover, life satisfaction is the evaluation of personal life as a whole, not simply the current level of happiness [32].
Among other scholars, refs.[33,34] emphasized that health is usually significantly correlated with life satisfaction.According to some other authors [35,36], higher levels of freedom of choice and control are usually strongly associated with life satisfaction.
From other perspectives [37,38], those with higher levels of financial satisfaction are also more inclined to show higher levels of life satisfaction.
According to other scholars, those more inclined and exposed to democracy as an expression of the will of the people [39] and also of the subjectivity of society [40] or as a crucial way to realize human rights [41], are also more likely to be satisfied with their lives [42,43].
The consecrated socio-demographic features [44] are also significant influences associated with this type of satisfaction.For instance, some researchers [45][46][47][48][49] invoke the U-shape when it comes to the graphical representation of the influence of age on life satisfaction, with high levels of life satisfaction in young adulthood, a gradual decline in middle age with a minimum of being satisfied with life between 40 and 60 years of age, and then an increase in later life.Other studies [50] have found a more complex relationship between age and life satisfaction, with multiple peaks and valleys throughout life.Concerning the relation between gender and this type of satisfaction, it seems the latter also depends on the stability or transitions in marital status [51].Other studies revealed significant correlations between personality, self-esteem, and life satisfaction [52] or between optimism-related variables, goal orientation, and the same type of satisfaction [53].
A salient and succinct point from this literature review is that life satisfaction is determined by multiple factors, including happiness, health, autonomy, economic contentment, democracy, and democratic values, Big Five personality traits, and socio-demographic characteristics, with significant correlations observed between these variables and overall life satisfaction.
Consequently, the main hypotheses of this paper are: Happiness is closely related to life satisfaction, even if it is far from acting as synonymous with it [54].

H2.
Good health [55] and freedom of choice and control [56,57] are strongly related to well-being, happiness, and this type of satisfaction [58].
H3. Financial choice and satisfaction are closely associated with well-being, the latter being considered more than just happiness and life satisfaction [59].Therefore, the first two are also related to being satisfied with life.
In terms of identified research gaps, most of the existing quantitative studies use data limited in terms of time (a questionnaire at a precise moment in time, a questionnaire applied within a given period/wave) or space (only one country or continent, or at most comparisons between several countries or between a limited number of regions).Moreover, most of the existing papers do not have the stated purpose of identifying the core intersecting predictors of life satisfaction starting from a dataset so varied in time, space, and consecrated socio-demographic features of respondents.Added to this is the fact that most studies do not use simultaneous cross-validation according to random (10-fold) and non-random criteria (both socio-economic criteria and different versions of the dataset).

Materials and Methods
This article started from one of the most comprehensive World Values Survey (WVS) datasets.The latter (version 4.0, WVS_TimeSeries_4_0.dta)includes 1045 variables and 450,869 raw observations.It served all selection rounds.Three other versions have been used just in the first selection round (Adaptive Boosting in Rattle), namely: version 3.0 (WVS_TimeSeries_1981_2022_Stata_v3_0.dta,1,041 variables and 440,055 observations, available online on the WVS site until the end of 2022), version 2.0 (WVS_TimeSeries_1981_2020_ stata_v2_0.dta,1072 variables, and 432,482 records) and 1.6 (WVS_TimeSeries_stata_v1_6.dta,1045 variables, and 426,452 observations), the latter two still available on the WVS site, namely https://www.worldvaluessurvey.org[accessed on 9 January 2023].Their .csvexports 1 were preceded by designing, testing, and running a script sequence 2 responsible for removing the DK/NA [64] values (Do not Know/No Answer/Not Applicable coded by WVS as negative ones, artificially increasing the scales, and not beneficial for selections, Figure 1 and Table ??, Appendix A) of all variables and also by a simple binary derivation 3 (A170bin) of the original variable to analyze (A170, Satisfaction with your life).And this applies considering the two symmetric halves of its original scale (1-5 for 0, and 6-10 for 1, Table ??, Appendix A).Moreover, the option to generate numerical values for labeled variables (instead of the text) was enabled when exporting (e.g., export delimited using "F:\data\WVS-TS4_A170bin.csv", nolabel replace).Figure 1 visually depicts the frequency counts of variables, including the target variable A170 (life satisfaction), before and after correcting an artificial scale increase caused by initially encoding "Don't Know" (DK) or "Not Applicable" (NA) responses as negative numbers in the dataset.This correction process is applied uniformly across all variables, illustrating how it impacts the distribution patterns and ensuring that subsequent analyses are based on accurately scaled data.The figure plays a critical role in demonstrating the methodological step taken to enhance the reliability and validity of analyses related to life satisfaction and other variables in the dataset.
The next step was to load these .csvexports into the Rattle interface (version 5.5.1-started using two commands in R, namely library(rattle) and rattle()), then set A170bin as the target, ignore its source (A170) from the list of inputs, and apply the Adaptive Boosting technique for the decision tree classifiers [65].This step ran [66,67] for four versions of this most comprehensive dataset of WVS (v4.0, 3.0, 2.0, and 1.6) using default settings (online available at https://tinyurl.com/2wrd3ju6[accessed on 19 June 2024]).The purpose was to discover the most resilient related variables at the intersection of those four versions (cross-validation considerations).The latter was the 1st selection round (9 resulting variables-Figure 2).
Other alternative selections applied only to the most recent and comprehensive version (4.0) and starting after the same DK/NA treatments considered: (a) The use of the Naïve Bayes classification algorithm inside the Microsoft DM add-in for spreadsheets (Figure 3) that works together with  4) inside the same VM.First, they meant a minimum threshold of 0.1 [68] for the absolute values of pairwise correlation coefficients [69] between each recoded variable from the previous step and the one to analyze.In addition, a maximum accepted p-value (max p = 0.001) and a minimum support afferent to a minimum number of valid observations for the target variable (at least half the total corresponding number-444,917/2, Figure 4) for each pair.The next step was to load these .csvexports into the Rattle interface (version 5.5.1started using two commands in R, namely library(rattle) and rattle()), then set A170bin as the target, ignore its source (A170) from the list of inputs, and apply the Adaptive Boosting technique for the decision tree classifiers [65].This step ran [66,67] for four versions of this most comprehensive dataset of WVS (v4.0, 3.0, 2.0, and 1.6) using default settings (online available at https://tinyurl.com/2wrd3ju6[accessed on 19 June 2024]).The purpose was to discover the most resilient related variables at the intersection of those four versions (cross-validation considerations).The latter was the 1st selection round (9 resulting variables-Figure 2).Only seven (7) variables proved to be the most resilient at the intersection of Adaptive Boosting (Rattle in R), Naïve Bayes (Analysis Services), and PCDM (Stata).These are A008, A009, A173, C006, E235, E236, and X047_WVS.From these seven, only the first six were confirmed (successive invocations until no loss in selection) when using CVLASSO (for performing random cross-validation) and RLASSO (for removing overfitting) available after installing the LASSO package [70], and the BMA (Bayesian Model Averaging) command in Stata 17 for both forms of the target variable (S002VS set as an auxiliary influence in BMA).
Additionally, some consecrated socio-demographic variables served non-random cross-validation and later as control.For the first (non-random cross-validation), these variables helped mixed-effects models [71][72][73] in .Such models included both fixed effects (the remaining six variables after the previous selection phases) and random ones (clusters on gender, age, marital status, number of children, education level, income level, professional situation, settlement size, country, and survey year, all as socio-demographic variables, bottom of Table A1, Appendix A).
The immediate selection phase measured the existing collinearity between the remaining influences (those six above).First, a matrix with correlation coefficients augmented with intensity bars has been generated only for these six remaining influences [68].In addition, ordinary least squares (OLS) regressions served the same purpose by measuring the computed VIF (Variance Inflation Factor) against (Equation (1)) the maximum accepted VIF threshold of the model [74,75] for all combinations of two influences of those 6 (combinations of n = 6 taken by k = 2, meaning 15 possibilities-Equation (2)).E235 and E236 emerged as being collinear at this point.

Model's maximum accepted
where: C(n,k) is the number of combinations of n taken by k; In addition, to choose between these two, logistic regressions have been used.The variable that is responsible for generating models with more explanatory power/larger R-squared [76] and more information gain/smaller values for both AIC and BIC [77] was preserved (e.g., E236).
Additionally, two prediction nomograms [78] resulted (one simple and another one augmented with additional details to become self-explanatory) when using the nomolog command (after its previous installation using a specific installation syntax, namely net install st0391, replace from (http://www.stata-journal.com/software/sj15-2)[accessed on 19 June 2024] and considering the most stalwart remaining influences.
Moreover, each consecrated socio-demographic variable previously used for crossvalidation (except S003-country code, which consists of numbers not corresponding to a particular intensity scale) served controlling purposes (new models).The latter meant adding them one by one on top of the existing most robust models (the most resilient influences emerging after the previous selection round or the core models) and separately (one per model).
In addition, for each variable in the core and socio-demographic category above, a twoway graphical representation (scatter chart) was automatically generated by considering each corresponding relationship with the outcome variable (life satisfaction in its scale format tabulated on average by peculiar criteria using the tabstat command in Stata).
Finally, reverse causality checks were performed using ordinal logit (ologit) regressions and the scale form of the target variable corresponding to life satisfaction (A170osc).In each of these regressions that considered only one of the remaining input variables, the latter served both as input and outcome, interchanging these roles with A170osc (regression pairs).A larger R-squared (representing smaller differences between the observed data and the fitted values/theoretical model) and/or a lower AIC and BIC (better fit and smaller information loss) for the resulting models are an indication that each of the remaining variables to further select are more likely to be determinants of A170osc rather than vice versa (determined by it).
The reporting of results mainly benefited from the estout prerequisite package (ssc install estout, replace) with support for both the eststo and esttab commands [79,80], allowing the direct generation of tables (in the console and as external files, respectively) with default performance metrics and some additional ones [81] for well-known statistical models.
A persistent Google Drive online container 4 keeps all processing and analysis script sequences together with all intermediary results necessary for this study and demos acting as short tutorials [82] able to capture and show at least the dynamics of some selections and supporting this research.Moreover, due to the unavailability of a preview after sharing, the URL for each script has been altered to allow a one-step download (the specific syntax ending with <<&export=download>>).The latter means that no further confirmation is required.
This paper also relied on several multimedia elements [83][84][85][86].The latter meant combining text, tables, script sequences, graphs or charts including scatter plots and magnitude lines or bars, video captures, and, in addition, visual synthesis and emphasis methods.

Results
After performing the first selection step using Adaptive Boosting (in the Rattle libraryhttps://rattle.togaware.com of R) on four versions of the WVS dataset, a set of 10 intersecting variables resulted (Figure 2).
As seen in all four sources (online at https://tinyurl.com/2wrd3ju6) of Figure 2, one way to look at the relevance of the resulting variables is by considering their corresponding frequencies of use in the tree construction behind the Adaptive Boosting technique.
Moreover, this was also the first selection round based on cross-validation considering different (increasing) numbers of observations for those four versions of the source dataset with more and more data and the intersecting set of influences found for them (raw individual results online at https://tinyurl.com/2wrd3ju6[accessed on 19 June 2024] and synthesis in Figure 2).
The results of applying the first alternative selection based on Naïve Bayes classification in Microsoft DM and Analysis Services on version 4.0 (the most comprehensive one) of the WVS dataset (after removing DK/NA values) was a dependency network (Figure 3).Only eight of those nine influences above (all at the intersection of those four columns in Figure 2, except for D002) are present in this network.
Next, some filters served the selections when performing correlations using the PCDM custom command in Stata [69] on the same WVS dataset (the most recent and comprehensive version, 4.0).For instance, min.abs.correl.coeff.= 0.10, min.N = 222,459 (=round of 444,917/2, where 444,917 is the number of valid observations for the target variable, as seen on the top-right and bottom of Figure 4), and maximum p-value of 0.001.The results (Figure 4) indicate only seven of those nine remaining variables above (all bolded in Figure 2 except for D002-low support, meaning just 26,459 observations as seen in the description of variables and general statistics, Tables A1 and A2, Appendix A, and S002VS-low correlation coefficients below the threshold value of 0.10).
The next concern was to start from the same nine robust common influences (Figure 2) and perform random cross-validation (cvlasso), selections based on removing overfitting (rlasso), and BMA selections (which report posterior inclusion probabilities-PIP, preferably as close to 1 as possible), all three 5 until convergence (no loss) and considering both forms (binary and scale) of the target variable (A170 and A170bin).Cvlasso used both the lse option (largest lambda for which MSPE or the Mean Squared Prediction Error is within one standard error of the minimal MSPE) and the lopt one (the lambda that minimizes MSPE).After this stage, those seven variables above persisted (all in Figure 2 except for D002 and S002VS).The next concern was to start from the same nine robust common influences (Figure 2) and perform random cross-validation (cvlasso), selections based on removing overfitting (rlasso), and BMA selections (which report posterior inclusion probabilities-PIP, preferably as close to 1 as possible), all three 5 until convergence (no loss) and considering both forms (binary and scale) of the target variable (A170 and A170bin).Cvlasso used both the lse option (largest lambda for which MSPE or the Mean Squared Prediction Error is within one standard error of the minimal MSPE) and the lopt one (the lambda that minimizes MSPE).After this stage, those seven variables above persisted (all in Figure 2 except for D002 and S002VS).Next, three rounds 6 of non-random cross-validation run using mixed-effects modeling.
For the first such round 7 , just one variable, namely X047_WVS (scale of incomes) of the remaining seven (the ones bolded in Figure 4) acting as fixed effects lost significance (nine from eleven models/scenarios with A170 set as target).And this was observed because of considering many clustering criteria/random effects (the consecrated sociodemographic variables mentioned in the previous section) and two mixed-effects regression types (both melogit for the binary form of the response variable and meologit for the one having values on a scale).If considering only the remaining six as fixed effects 8 (all bolded in Figure 4 except for X047_WVS-2nd round of non-random cross-validation), there was no loss in significance no matter the clustering criteria.
To additionally validate the simultaneous removal of both X047_WVS and S002VS at the previous steps (D002-no longer considered due to its low number of valid observations), an additional set of non-random cross-validation (3rd round of non-random cross-validation) based on both melogit and meologit has been performed (8 fixed effects and other ten clustering variables-Table A3, Appendix A).Those six remaining influences above proved to be robust (in terms of no loss of significance) in this additional round, namely A008, A009, A173, C006, E235, and E236.The other two failed at least in one scenario: X047_WVS when cross-validating using most consecrated socio-demographic variables as cluster criteria except for age (X003) and the number of children (X011) and considering the scale form of the target variable (A170), while S002VS (chronology of EVS-WVS waves) when cross-validating using the highest educational level attained (X025), the country code (S003), and the survey year (S020) as cluster criteria and considering both forms of the target variable (A170bin and A170).Next, three rounds 6 of non-random cross-validation run using mixed-effects modeling.For the first such round 7 , just one variable, namely X047_WVS (scale of incomes) of the remaining seven (the ones bolded in Figure 4) acting as fixed effects lost significance (nine from eleven models/scenarios with A170 set as target).And this was observed because of considering many clustering criteria/random effects (the consecrated socio-demographic variables mentioned in the previous section) and two mixed-effects regression types (both melogit for the binary form of the response variable and meologit for the one having values on a scale).If considering only the remaining six as fixed effects 8 (all bolded in Figure 4 except for X047_WVS-2nd round of non-random cross-validation), there was no loss in significance no matter the clustering criteria.
To additionally validate the simultaneous removal of both X047_WVS and S002VS at the previous steps (D002-no longer considered due to its low number of valid observations), an additional set of non-random cross-validation (3rd round of non-random crossvalidation) based on both melogit and meologit has been performed (8 fixed effects and other ten clustering variables-Table A3, Appendix A).Those six remaining influences above proved to be robust (in terms of no loss of significance) in this additional round, namely A008, A009, A173, C006, E235, and E236.The other two failed at least in one scenario: X047_WVS when cross-validating using most consecrated socio-demographic variables as cluster criteria except for age (X003) and the number of children (X011) and considering the scale form of the target variable (A170), while S002VS (chronology of EVS-WVS waves) when cross-validating using the highest educational level attained (X025), the country code (S003), and the survey year (S020) as cluster criteria and considering both forms of the target variable (A170bin and A170).
Next, when verifying the existing collinearity using the first method, a matrix with correlation coefficients and a minimum visual augmentation using intensity bars for the remaining six influences emerged (Figure 5-all Pearson correlation coefficients are sig- Next, when verifying the existing collinearity using the first method, a matrix with correlation coefficients and a minimum visual augmentation using intensity bars for the remaining six influences emerged (Figure 5-all Pearson correlation coefficients are significant at 0.1‰).The latter (as absolute values) shows no evidence of collinearity if considering 0.1 and 0.39 as the lower and upper limits for weak correlation, while 0 and 0.1 as the ones for negligible correlation [68].
In addition, OLS max.Comput.VIF against OLS max.Accept.VIF (Equation ( 1)) for models with all six previously tested influences (Figure 5) at once (model 1 in Table A4, Appendix A) and additionally taken each two (models 2-16 in Table A4, Appendix A) in all 15 combinations (Equation ( 2)) served to discover further evidence of collinearity.The removal decision considered one of the two variables.These are E235 and E236, namely the importance of democracy as own value and democracy as perceived in own country, respectively (model 16 in Table A4, Appendix A, namely the only one for which OLSmaxComputVIF > OLSmaxAcceptVIF) [81].
After performing additional logit regressions, E236 brought higher accuracies (AUC-ROC of 0.8350 and 0.8351) and R-squared values (0.2645 and 0.2649) together with better fit due to lower AIC and BIC values than E235 (AUC-ROC of 0.8340 and 0.8345, R-squared of 0.2624 and 0.2638).And this was recorded when considering the binary form of the target variable (model 3 vs.model 4 for comparable support due to the same number of observations using a filtering condition on the variable dropped, and model 5 vs. 6 for all but different numbers of available responses and no filtering condition-Table A5, Appendix A).
In the case of additional ologit regressions, the models keeping E235 and dropping E236 had a better R-squared than those keeping E236 and dropping E235.However, the same did not apply in terms of information gain.Consequently, the balance is inclined towards keeping E236 at the expense of removing E235 (the other democracy-related variable).nificant at 0.1‰).The latter (as absolute values) shows no evidence of collinearity if considering 0.1 and 0.39 as the lower and upper limits for weak correlation, while 0 and 0.1 as the ones for negligible correlation [68].In addition, OLS max.Comput.VIF against OLS max.Accept.VIF (Equation ( 1)) for models with all six previously tested influences (Figure 5) at once (model 1 in Table A4, Appendix A) and additionally taken each two (models 2-16 in Table A4, Appendix A) in all 15 combinations (Equation ( 2)) served to discover further evidence of collinearity.The removal decision considered one of the two variables.These are E235 and E236, namely the importance of democracy as own value and democracy as perceived in own country, respectively (model 16 in Table A4, Appendix A, namely the only one for which OLSmax-ComputVIF > OLSmaxAcceptVIF) [81].
After performing additional logit regressions, E236 brought higher accuracies (AUC-ROC of 0.8350 and 0.8351) and R-squared values (0.2645 and 0.2649) together with better fit due to lower AIC and BIC values than E235 (AUC-ROC of 0.8340 and 0.8345, R-squared of 0.2624 and 0.2638).And this was recorded when considering the binary form of the target variable (model 3 vs.model 4 for comparable support due to the same number of observations using a filtering condition on the variable dropped, and model 5 vs. 6 for all but different numbers of available responses and no filtering condition-Table A5, Appendix A).In the case of additional ologit regressions, the models keeping E235 and dropping E236 had a better R-squared than those keeping E236 and dropping E235.However, the same did not apply in terms of information gain.Consequently, the balance is inclined towards keeping E236 at the expense of removing E235 (the other democracy-related variable).
As support, 234,223 valid intersecting observations (meaning 51.95% of the total number of records for the entire dataset) corresponding to the last three waves were behind the first core model (model 5, Table A5, Appendix A).And this is because all five most resilient influences and the response variable were considered simultaneously only in these three waves (2005-2009, 2010-2014, and 2017-2022), E236 having no observations 9  for the first four.The same happened if removing E236 and preserving E235 with a slight increase in the number of responses (more than 245,000 valid intersecting observationsmodel 6, Table A5, Appendix A) and a slight decrease in terms of accuracy of classification (AUC-ROC = 0.8345).If removing both E235 and E236 (model 2, Table A5, Appendix A), the support increases to 410,513 non-null intersecting observations (meaning 91.05% from the total number, namely 450,869, and also 92.26% from those 444,917 valid for the target variable) while covering all seven waves and increasing the accuracy of classification (AUC-ROC = 0.8458).Furthermore, the four remaining influences are now fully included in the list of the mightiest links in the Naïve Bayes dependency network (Figure 3).As support, 234,223 valid intersecting observations (meaning 51.95% of the total number of records for the entire dataset) corresponding to the last three waves were behind the first core model (model 5, Table A5, Appendix A).And this is because all five most resilient influences and the response variable were considered simultaneously only in these three waves (2005-2009, 2010-2014, and 2017-2022), E236 having no observations 9 for the first four.The same happened if removing E236 and preserving E235 with a slight increase in the number of responses (more than 245,000 valid intersecting observations-model 6, Table A5, Appendix A) and a slight decrease in terms of accuracy of classification (AUC-ROC = 0.8345).If removing both E235 and E236 (model 2, Table A5, Appendix A), the support increases to 410,513 non-null intersecting observations (meaning 91.05% from the total number, namely 450,869, and also 92.26% from those 444,917 valid for the target variable) while covering all seven waves and increasing the accuracy of classification (AUC-ROC = 0.8458).Furthermore, the four remaining influences are now fully included in the list of the mightiest links in the Naïve Bayes dependency network (Figure 3).
Next, a simple Stata script design (Table ??, Appendix A) supports the alignment of the scales to 0 for the target variable and those corresponding to some solid influences on it.Another purpose of the latter was to optimize the following two prediction nomograms (Figure 6, nomolog command in Stata) for better readability.Both are based on binary logistic regressions.The corresponding two models are identical to those numbered 2 and 5 (Table A5, Appendix A) in terms of performance metrics and values of coefficients and errors for the top five influences except for the sign of the first two, namely A008 and A009, due to reversed scales (Table ??, Appendix A).These serve the visual interpretation of all remaining most potent influences.The first nomogram is simple (meaning the exact way it results after generating it using the nomolog command).It corresponds to a model with five resilient influences, with lower support (51.95% of the total number of observations because of E236osc) but still generating a considerable R 2 (0.2649) and good accuracy of classification (AUC-ROC of 0.8351).The second one corresponds to a model with only those four most resilient influences and high support (91.05% of the total observations of the WVS dataset, version 4.0), generating an R 2 of 0.2884 and good accuracy of classification (AUC-ROC of 0.8458).This second nomogram is augmented with metadata about the individual score at the intersection with the X-axis (perpendicular lines drawn next to each possible value of the associated influences), respectively, with suggestions for interpreting the input values, their corresponding scores, and the resulting total score and afferent likelihood, so that the nomogram is self-explanatory.The maximum theoretical probability for the most advantageous combination of variable values (extreme right) in both nomograms is high.It indicates a value of more than 0.95 (95%-middle and bottom of Figure 6).These nomograms also reflect the magnitude of marginal effects (better comparability than with raw coefficients) for the corresponding variables.In addition, they serve to understand the cumulated effect size by considering the amplitude of any scale easily noticeable in these visual representations.
Additional controls (Tables A6 and A7, Appendix A) obtained by adding consecrated socio-demographic variables one by one to the already validated core models with 4 and 5 influences (Figure 6) successfully demonstrated again the robustness of the latter.Moreover, they confirmed or rejected the role of these additional variables.For instance, some of such variables have dramatically lost their significance (X011, Tables A6 and A7, Appendix A, model 4; X028, Table A6, Appendix A, model 17), while others just changed the sign when considering those two forms of the target variables (X011, X025, X047_WVS, S02VS, and S020).By contrast, the persistence (both in sign and significance) of X001, X003, X007, X045, and X049 (Tables A6 and A7, Appendix A) indicated a potential role of gender, age, marital status, social class, and settlement size, respectively, in overall models even if they did not pass the previously described selection stages.In addition to the tests performed by including each of the two robust core models above (Figure 6), the remaining five consecrated socio-demographic influences are tested again, this time separately and considering just one per model (Tables A8 and A9, Appendix A).Under such circumstances, it is noticeable that only the last three (X007, X045, and X049) persisted, while the first two (X001 and X003) changed their sign, lost significance, or both.Additional controls (Tables A6 and A7, Appendix A) obtained by adding consecrated socio-demographic variables one by one to the already validated core models with 4 and 5 influences (Figure 6) successfully demonstrated again the robustness of the latter.Moreover, they confirmed or rejected the role of these additional variables.For instance, some of such variables have dramatically lost their significance (X011, Tables A6 and A7, Appendix A, model 4; X028, Table A6, Appendix A, model 17), while others just changed the sign when considering those two forms of the target variables (X011, X025, X047_WVS, S02VS, and S020).By contrast, the persistence (both in sign and significance) of X001, X003, X007, X045, and X049 (Tables A6 and A7, Appendix A) indicated a potential role of gender, age, marital status, social class, and settlement size, respectively, in overall models even if they did not pass the previously described selection stages.In addition to the tests performed by including each of the two robust core models above (Figure 6), the remaining five consecrated socio-demographic influences are tested again, this time separately and considering just one per model (Tables A8 and A9, Appendix A).Under such circumstances, it is noticeable that only the last three (X007, X045, and X049) persisted, while the first two (X001 and X003) changed their sign, lost significance, or both.
The tabulations by mean supporting the two-way graphical representations between the target variable and each of the core variables (top five in Figure 7 from upper-left to lower-right) and also from the socio-demographic category (last eleven on the bottomright of Figure 7) are also available.The tabulations by mean supporting the two-way graphical representations between the target variable and each of the core variables (top five in Figure 7 from upper-left to lower-right) and also from the socio-demographic category (last eleven on the bottom-right of Figure 7) are also available.
For the specific case of average life satisfaction (Mean_A170osc) against age (X003), the number of non-null responses was also considered (Count_A170osc_byX003-Figure 8).The purpose of the latter was to assess age limits for outliers in terms of frequency (outside the range between 15 to 90 years, meaning at least hundreds of valid records/observations-Table A10, Appendix A) for this specific case of Mean_A170osc versus X003 (middle-right of Figure 7).The relation between average life satisfaction and age shows a much more pronounced U-shape after removing these low-frequency outliers (Figure 9) than earlier (a somewhat flatter "U"-X003, middle-right of Figure 7).
Final cross-validations 10 considered models with seven (the quad-core plus marital status/X007osc, social class/X045osc, and settlement size/X049osc) or eight influences (the penta-core plus the same three above) and a reasonable number of criteria for cross-validations.They refuted the last three influences added to those two cores when considering cross-validation criteria such as gender, employment status, the chronology of waves, country, and survey year in the case of the last two of those three (social class and settlement size) or the number of children in the case of marital status, even though the two overall models with 7 and 8 influences did not show multi-collinearity and recorded significance for all corresponding variables and accuracy and Rˆ2 scores better than the two core models with four and five components that already passed all the cross-validation tests.For the specific case of average life satisfaction (Mean_A170osc) against age (X003), the number of non-null responses was also considered (Count_A170osc_byX003-Figure 8).The purpose of the latter was to assess age limits for outliers in terms of frequency (outside the range between 15 to 90 years, meaning at least hundreds of valid records/observations-Table A10, Appendix A) for this specific case of Mean_A170osc versus X003 (middle-right of Figure 7).The relation between average life satisfaction and age shows a much more pronounced U-shape after removing these low-frequency outliers (Figure 9) than earlier (a somewhat flatter "U"-X003, middle-right of Figure 7).After performing some reverse causality checks (Table A11, Appendix A section), only three variables from those five most robust influences (model 5, Table A5, Appendix A section) were confirmed as determinants (A173osc, C006osc, and E236osc).Both a separate binary logistic model and a corresponding prediction nomogram (Figure 10) were generated for this triad of predictors (Stata script at https://tinyurl.com/3zenxed6[accessed on 19 June 2024]).The performance metrics of this model with only three components indicated an AUC-ROC of 0.814 (lower than that of model 5, Table A5, Appendix A, but still indicating good accuracy of classification) and an R-squared of 0.2205.Moreover, the maximum theoretical probability for the most advantageous combination of those three determinant values (right edge of each line in Figure 10) still indicates a value of more than 95% (18 or the sum of 5.75, 10, and 2.25 on the score axis corresponds to much more than 0.95).Final cross-validations 10 considered models with seven (the quad-core plus marital status/X007osc, social class/X045osc, and settlement size/X049osc) or eight influences (the penta-core plus the same three above) and a reasonable number of criteria for cross-validations.They refuted the last three influences added to those two cores when considering cross-validation criteria such as gender, employment status, the chronology of waves,   Final cross-validations 10 considered models with seven (the quad-core plus marital status/X007osc, social class/X045osc, and settlement size/X049osc) or eight influences (the penta-core plus the same three above) and a reasonable number of criteria for cross-validations.They refuted the last three influences added to those two cores when considering cross-validation criteria such as gender, employment status, the chronology of waves, cessed on 19 June 2024]).The performance metrics of this model with only three compo-nents indicated an AUC-ROC of 0.814 (lower than that of model 5, Table A5, Appendix A, but still indicating good accuracy of classification) and an R-squared of 0.2205.Moreover, the maximum theoretical probability for the most advantageous combination of those three determinant values (right edge of each line in Figure 10) still indicates a value of more than 95% (18 or the sum of 5.75, 10, and 2.25 on the score axis corresponds to much more than 0.95).

Main Findings
The main findings highlight the significant influences on life satisfaction: financial satisfaction, happiness, freedom of choice, health, and democracy, each validated through robust empirical analysis of World Values Survey data and consistent with prior research in the scientific literature.
In terms of magnitude (descending order of scale amplitudes), the first and most important of these five influences corresponds to satisfaction with the household financial situation.It indicates that people who are more satisfied in such terms are more likely to show more contentment with their lives (positive influence or the maximum recoded value of 9 for C006osc-the right side of Figure 6).The latter means that this type of financial satisfaction (household-related) is among the best associated with life satisfaction according to WVS data (complete validation of H3).This finding is in line with the already documented relationship between both financial costs and benefits and their well-being implications, as mentioned in the scientific literature [94][95][96][97].
The second most important influence (considering the same magnitude criterion) seems to correspond to the feeling of happiness.The latter belongs to a peculiar variable sub-category defined as <<Important in Life>>.As expected, this shows that those who reported a higher level of happiness are also more likely (positive influence or the maximum recoded value of 3 for A008osc-the right side of Figures 6 and 7) to be satisfied with their lives (complete validation of H1).Although this finding seems close to being obvious, the relationship between happiness and life satisfaction is reciprocal and well-studied [98][99][100].
The third most potent influence found is related to the level of freedom of choice and control.It means that people with a higher level of this type of freedom are also more likely (positive influence or the maximum recoded value of 9 for A173osc-the right side of Figure 6) to be satisfied with their lives.The latter is in line with the findings of other scholars [36, 101,102] and contributes to the validation of the second part of H2.
The fourth strongest influence corresponds to the individual state of health, subjectively assessed.That is also positively correlated with the response variable (the maximum recoded value of 4 for A009osc-the right side of Figure 6).The latter means that people with a better state of health (even if subjectively assessed) are also more likely to be satisfied with their lives.This finding also stands when considering the existing scientific literature on how health influences life satisfaction not only directly but also indirectly [103][104][105] and contributes to validating the first part of H2.
These four influences above are the strongest both in terms of the magnitude of the marginal and cumulative effects (bottom of Figure 6), the accuracy of classification (the quad-core model is more accurate than the penta-core one), and also in terms of support as the number of valid observations in the data set (more than 90%-Figure 4), number of countries (107 out of 108), and WVS waves covered (all).
If accepting a tiny compromise of support (slightly more than 50% of the total number of valid observations of the target variable-Figure 4), the fifth overpowering influence emerges, and this relates to considerations about democracy.E236osc corresponds to the perceived level of democracy in one's own country, and it is a positive influence (the maximum recoded value of 9-the right side of Figure 6).E235 (model 4 in Table A5, Appendix A) also indicates the importance of democracy, as reflected in the WVS survey responses.The latter is also positively correlated with the response variable and shows that people who are more inclined to declare the overall importance of democracy are also more likely to be satisfied with their lives.These two findings are compatible with other similar discoveries from the scientific literature [106,107].The specific way these two variables act means a complete validation of H4.

Socio-Demographic Findings
The socio-demographic findings highlight significant influences on life satisfaction, including gender, age, marital status, social class, settlement size, and regional variations, validated through comprehensive controls using World Values Survey data.
All the most resilient influences previously found (Figure 6) stood as a strong base for further controls (Tables A6 and A7, Appendix A).The latter means using the entire list of consecrated socio-demographic criteria involved in cross-validations.Only five of those eleven criteria indicated significance (partial validation of H5), even though they did not pass the cross-validation tests like the five core influences.Moreover, the first two (gender and age) of these five changed their sign, lost significance, or both when taken separately (one influence per model).
First, the influence of gender (the control variable X001osc_fem having the value of 1 for female respondents and 0 for male ones) proves to be significant only when considered together with both forms of the core models (penta-and quad-core) and not when taken separately in a binary logit model (Table A8, Appendix A, model 6).It indicates that women are more prone to report slightly better life satisfaction [108] than men (Tables A6 and A7, Appendix A, models 1 and 12).The latter seems in line with [29].These authors consider that, on average, women have higher life satisfaction than men, though, they are also more likely to report being depressed.The same authors above explain this paradox by considering that women are more sensitive and feel a wider array of emotions.
The control variable corresponding to age (X003) also proves to be significant, but not when taken separately in an ordinal logit model (Table A9, Appendix A, model 7), only when considered together with both forms of the core models and with a low coefficient in the regression model.Separately, its specific relation with life satisfaction indicates that beyond certain points (age between 40 and 60), as people grow older, there is some chance of greater life satisfaction (Tables A6 and A7, Appendix A, models 2 and 13, and Figure 9).This finding is expected when considering that the corresponding variable also positively correlates with improvements in the standard of living and the progress of science and technology, which are also strongly related to the level of development corresponding to the origin country of respondents.Other studies consider creating life satisfaction models as a real challenge-models in which the pure effect of age on this type of satisfaction is determinable while being the subject of many controversies [47, 49,109], including a so-called <<U-shaped>> relationship between age and life satisfaction, with an overall upward trend (rightward lift).The latter is confirmed in this article (middle-right of Figures 7 and 9) based on WVS data.
Third, the influence of marital status (X007osc, with higher values when living as a couple and lower ones when living alone) indicates by its positive sign that married people or those living as a couple are more likely to show life satisfaction (Tables A6 and A7, Appendix A, models 3 and 14) than others (divorced, separated, widowed, or single/never married).This finding is in line with other evidence from the scientific literature [110,111], suggesting a strong relationship between marital success and life satisfaction.
Another consecrated socio-demographic variable found to be significant is social class (X045osc, with larger values for upper classes and vice versa for lower ones).By its positive sign, the latter indicates that those earning more and better positioned as a social class are also more likely (Tables A6 and A7, Appendix A, models 7 and 18) to exhibit life satisfaction.This idea also stands in light of some other findings from the scientific literature [112][113][114].
Another significant control variable corresponds to settlement size (X049osc, with higher values for larger communities or cities).Due to its positive sign, the latter shows that people from larger communities seem more satisfied with their lives than those from smaller settlements.This finding is confirmed by similar discoveries [106,115,116].
Due to its nature (nominal numerical codes unrelated to a specific intensity scale), the variable corresponding to the country code in the given form (S003, as the interview took place) was not considered a control variable.Still, it has proven to be an extremely important cross-validation criterion.However, the specific features of some countries will be the object of future research on the same topic.For instance, a dummy variable referring to ex-communist countries or not [117], some country-dependent measures of economic activity such as GDP or the ratio between Stock Market Capitalization and GDP defined in The World Bank Data Catalog, or even the Worldwide Governance Indicators defined by [118] and used in many other studies including recent ones [119,120].
The reverse causality checks indicated only three determinants (a triad) from the penta-core model, namely satisfaction with the household financial situation (C006osc), the level of freedom of choice and control (A173osc), and the perceived level of democracy in one's own country (E236osc), in this specific order given by the descending order of magnitude of effects corresponding to these three (Figure 10).

Limitations and Future Research Directions
The main limitations identified for this study are: (a) Dataset Constraints: The study uses data from the World Values Survey (WVS), which, while comprehensive, may have limitations in terms of geographic and cultural coverage.Certain regions or cultures might be underrepresented, affecting the generalizability of the findings.Moreover, there is the impossibility of applying the obtained models to a specific list of countries.For instance, the quad-core model does not apply to respondents from Israel (no responses for variables A009, A173, and C006).The same happens for the penta-core model in the case of 16 countries out of a total of 108, namely Albania, Bosnia-Herzegovina, Croatia, Dominican Republic, El Salvador, Israel, Kuwait, Latvia, Lithuania, Montenegro, Qatar, Saudi Arabia, Uganda, North Macedonia, Tanzania, and Uzbekistan (no responses also for E236); (b) Temporal Limitations: The data spans several versions of the WVS, but the temporal changes and trends over time might not be fully captured or addressed, limiting insights into how life satisfaction determinants evolve; (c) Self-Reported Measures: The reliance on self-reported data for variables like financial satisfaction, happiness, and health can introduce biases, such as social desirability bias or inaccuracies in self-assessment; (d) Omitted Variables: Despite rigorous selection processes, there might be other relevant determinants of life satisfaction that were not included in the analysis, leading to omitted variable bias; (e) Cross-Sectional Nature: The study is based on cross-sectional data; therefore, it limits the ability to draw causal inferences.Longitudinal studies would be more robust in establishing cause-and-effect relationships; (f) Complex Interactions: The interactions between variables (e.g., how financial satisfaction and health together influence life satisfaction) might be complex and not fully explored in the study.
The future directions of research, considering the previously identified limitations, mainly refer to: (I) Cultural and Regional Specificity: More region-specific or culture-specific studies could help identify unique determinants of life satisfaction that are relevant to specific populations, providing a more nuanced understanding; By addressing all these limitations and exploring the above-mentioned future research directions, the understanding of life satisfaction and its determinants can be significantly enhanced, providing more targeted and effective interventions for improving overall well-being.

Conclusions
This study starts with WVS data and makes a significant novel contribution by pinpointing five robust influences associated with life satisfaction: financial satisfaction, happiness, autonomy as freedom of choice and control, health, and democracy.Through rigorous statistical analysis and advanced methodologies including feature selection and various types of validations, it identified a subset of three key determinants-financial satisfaction, autonomy, and democratic values-that consistently influence life satisfaction across diverse socio-demographic contexts.These findings not only underscore the enduring impact of these factors on personal well-being but also highlight their resilience against different types of cross-validations (both random and non-random, the latter on various socioeconomic criteria and different dataset versions), reverse causality checks, and overfitting tests, ensuring robustness in predictive models.All conclusions related to these influences and determinants identified as the most robust are based on models with good classification accuracy.By offering nomograms for visual interpretation and probability prediction, this study provides practical tools for policymakers and researchers to understand and enhance life satisfaction dynamics effectively.
Moreover, additional checks generally emphasized the secondary role of some consecrated socio-demographic variables for being satisfied with life.It is about age, female gender, and settlement size (all three as positive influences); marital status in terms of being closer to single/independent; and social class in terms of being closer to a lower class (both as negative influences).
The implications of this research are profound for societal well-being, emphasizing specific factors that significantly contribute to life satisfaction.Individuals who report higher levels of financial satisfaction, happiness, autonomy, good health, and exposure to democratic values are more likely to experience greater life satisfaction.Conversely, consecrated socio-demographic variables such as age, gender, marital status, social class, and settlement size, while traditionally considered influential, play secondary roles compared to these core influences.This insight suggests that policies aimed at improving societal well-being should prioritize enhancing economic stability, individual freedoms, health care access, and democratic governance.By focusing on these key areas, policymakers can foster environments conducive to higher life satisfaction among diverse populations, thereby promoting overall societal prosperity and stability.
Funding: This research received no external funding.The APC was supported by review vouchers.

Institutional Review Board Statement:
The data used in this study belongs to the World Values Survey, which conducted surveys following the Declaration of Helsinki.The WVS also follows good academic practice and abides to ethical norms in line with its mission, as declared at: https://www.worldvaluessurvey.org/WVSContents.jsp?CMSID=PaperSeries&CMSID=PaperSeries.[accessed on 19 June 2024].

Informed Consent Statement:
The World Values Survey obtained informed consent from all subjects involved in the study.

Conflicts of Interest:
The author declares no conflict of interest.

List of Abbreviations
Table A3.The results of the 3rd round of non-random cross-validations on some consecrated socio-demographic variables using mixed-effects binary (first ten models) and ordered Logit (Ologit-last ten ones).

Figure 1 .Figure 2 .
Figure 1.Frequency counts status before and after removing the artificial increase of scales due to the initial encoding of DK/NA values as negative numbers-example regarding the target variable (A170-life satisfaction) and applicable to all remaining variables in the dataset.Societies 2024, 14, x FOR PEER REVIEW 7 of 47

Figure 2 .
Figure 2. A synthetic view of the set of 9 intersecting influences found in the first selection round using Adaptive Boosting in Rattle and four versions of the WVS dataset.

Societies 2024 , 47 Figure 3 .
Figure 3. Dependency network based on Naïve Bayes classification in Microsoft Analysis Services showing all identified influences together with the strongest ones for the WVS dataset, version 4.0.Source: The transition from the mightiest links to all links is available in a short video available at https://tinyurl.com/4vjzdz8p[accessed on 19 June 2024].

Figure 3 .
Figure 3. Dependency network based on Naïve Bayes classification in Microsoft Analysis Services showing all identified influences together with the strongest ones for the WVS dataset, version 4.0.Source: The transition from the mightiest links to all links is available in a short video available at https://tinyurl.com/4vjzdz8p[accessed on 19 June 2024].

Figure 5 .
Figure 5. Improved collinearity view using a matrix with correlation coefficients augmented with intensity bars from visual formatting in spreadsheet tools only for the remaining six influences and the pwcorr command in Stata (pwcorr A008 A009 A173 C006 E235 E236, sig obs).

Figure 5 .
Figure 5. Improved collinearity view using a matrix with correlation coefficients augmented with intensity bars from visual formatting in spreadsheet tools only for the remaining six influences and the pwcorr command in Stata (pwcorr A008 A009 A173 C006 E235 E236, sig obs).

Figure 6 .
Figure 6.Simple vs. augmented prediction nomograms corresponding to the best models in terms of accuracy and resilience generated using the nomolog command in Stata immediately after performing the final recoding in Listing A3, Appendix A and obtaining two logit models similar to models 5 and 2 (Table A5, Appendix A) (Stata script at https://tinyurl.com/3mh48syw[accessed on 19 June 2024]).

Figure 6 .
Figure 6.Simple vs. augmented prediction nomograms corresponding to the best models in terms of accuracy and resilience generated using the nomolog command in Stata immediately after performing the final recoding in Table ??, Appendix A and obtaining two logit models similar to models 5 and 2 (Table A5, Appendix A) (Stata script at https://tinyurl.com/3mh48syw[accessed on 19 June 2024]).

Figure 7 .
Figure 7. Two-way graphical representations of the relations between each variable from the core models considered in this study or the socio-demographic category and the target one (on average, starting from its scale format) corresponding to life satisfaction (Stata script at https://drive.google.com/u/0/uc?id=1RhqABlTswnOUfvZH7vHqfVr6st_gant9&export=download [accessed on 19 June 2024]).

Figure 9 .
Figure 9. Second two-way graphical representation of the relation between the age variable (X003Re-mOutl-after removing the outliers) and the target one (A170osc-on average, starting from its scale format) corresponding to life satisfaction (Stata script at https://drive.google.com/u/0/uc?id=1ejfZcpfCpKPck099eZqJ-Mqgtv0X7u_q&export=download[accessed on 19 June 2024]).

Figure 9 .
Figure 9. Second two-way graphical representation of the relation between the age variable (X003Re-mOutl-after removing the outliers) and the target one (A170osc-on average, starting from its scale format) corresponding to life satisfaction (Stata script at https://drive.google.com/u/0/uc?id=1ejfZcpfCpKPck099eZqJ-Mqgtv0X7u_q&export=download[accessed on 19 June 2024]).

Figure 9 .
Figure 9. Second two-way graphical representation of the relation between the age variable (X003RemOutl-after removing the outliers) and the target one (A170osc-on average, starting from its scale format) corresponding to life satisfaction (Stata script at https://drive.google.com/u/0/uc?id=1ejfZcpfCpKPck099eZqJ-Mqgtv0X7u_q&export=download [accessed on 19 June 2024]).

Figure 10 .
Figure 10.Simple prediction nomogram corresponding to a robust model with only three determinants.Figure 10.Simple prediction nomogram corresponding to a robust model with only three determinants.

Figure 10 .
Figure 10.Simple prediction nomogram corresponding to a robust model with only three determinants.Figure 10.Simple prediction nomogram corresponding to a robust model with only three determinants.
(II) Considering Additional Variables: Expanding the range of variables to include factors like environmental quality, social networks, work-life balance, and country-level indices could provide a more comprehensive view of life satisfaction determinants; (III) Methodological Innovations: Employing newer statistical and machine learning techniques could enhance the robustness and predictive power of the models.Techniques such as deep learning or more sophisticated related models could be explored; (IV) Qualitative Research: Integrating qualitative research methods, such as interviews or focus groups, can provide deeper insights into the subjective aspects of life satisfaction

Table A2 .
Descriptive statistics for the most relevant WVS items (version 4.0) used in this study after removing their DK/NA values.

Table A4 .
Identifying collinearity issues in an additional round of collinearity checks using OLS regressions and the binary form of the outcome variable (A170bin).

Table A5 .
Collinearity removal based on comparative results in each pair of columns.
Source: Own calculation in Stata (Stata script at https://drive.google.com/u/0/uc?id=1quMO28SHFyi1XnOxSQQRmB6hwTICmm7W&export=download[accessed on 19 June 2024]).Notes: Robust standard errors are between round parentheses.The raw coefficients emphasized using *** are significant at 1‰. Green vs. Red indicates better vs. worse models in terms of performance metrics.

Table A6 .
Controlling using the most relevant remaining five influences (penta-core) and most of the consecrated socio-demographic variables in Logit (first 11) and Ologit models (last 11).

Table A7 .
Controlling using the most relevant and supported four influences (quad-core) and most of the consecrated socio-demographic variables in Logit (first 11) and Ologit models (last 11).

Table A8 .
Controlling each of the most relevant influences (penta-core) and each consecrated socio-demographic variable in Logit models (one per model).