What Are the Most Important Predictors of Subjective Well-Being? Insights From Machine Learning and Linear Regression Approaches on the MIDUS Datasets

2021 ◽  
Author(s):  
Seth Margolis ◽  
Jacob Elder ◽  
Brent Hughes ◽  
Sonja Lyubomirsky
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Models ◽  
Stress Reactivity ◽  
A Priori ◽  
Life Quality ◽  
Well Being ◽  
The United States ◽  
Learning Approaches ◽  
Subjective Well Being

What are the most important predictors of subjective well-being? Using a nationally representative publicly available dataset from the Midlife in the United States project (N = 4,378), we applied linear regression, which often relies on assumptions of linearity and a priori interactions, and advanced machine learning approaches, which maximize prediction by thoroughly exploring nonlinear effects and higher-order interactions, to determine the ordering and characteristics of predictors of well-being. Advanced machine learning models generally did not predict well-being more accurately than did regression models, suggesting that many predictors of well-being may be linear and non-interactive. Consistent with this implication, the introduction of product and squared terms in regression models improved prediction, but only nominally. Our findings replicated previous research, with sociability, physical health, disengagement from goals, sex life quality, wealth, and religious activity emerging as the strongest predictors of well-being, and demographic factors emerging as relatively weak predictors. Furthermore, self-reported “aches” (the strongest “objective” predictor of well-being), stress reactivity, and disengagement negatively predicted well-being, reinforcing the role of stress in psychological maladjustment. Finally, unlike prior research, control over one’s life—and control over financial and work matters in particular—strongly predicted well-being.

scholarly journals Teasing Apart Silvopasture System Components Using Machine Learning for Optimization

Soil Systems ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 41
Author(s):  
Tulsi P. Kharel ◽  
Amanda J. Ashworth ◽  
Phillip R. Owens ◽  
Dirk Philipp ◽  
Andrew L. Thomas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Tree ◽  
Plant Production ◽  
System Level ◽  
Learning Approaches ◽  
Nutrient Distribution ◽  
Wetness Index ◽  
Variable Approach ◽  
Animal Grazing

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels <0.04 mg kg−1 (126% greater grazing hours per AU), soil Cr <0.098 mg kg−1 (108%), and a SAGA wetness index of <2.7 (57%). Cattle also preferred grazing (88%) native grasses compared to orchardgrass (Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

scholarly journals Searching for improvements in predicting human eye colour from DNA

2021 ◽  
Author(s):  
Magdalena Kukla-Bartoszek ◽  
Paweł Teisseyre ◽  
Ewelina Pośpiech ◽  
Joanna Karłowska-Pik ◽  
Piotr Zieliński ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sequencing Analysis ◽  
Learning Approaches ◽  
Human Eye ◽  
Software Analysis ◽  
Whole Exome ◽  
Eye Colour

AbstractIncreasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

scholarly journals Integration of shared-pathogen networks and machine learning reveals the key aspects of zoonoses and predicts mammalian reservoirs

2020 ◽  
Vol 287 (1920) ◽  
pp. 20192882 ◽  
Author(s):  
Maya Wardeh ◽  
Kieran J. Sharkey ◽  
Matthew Baylis
Keyword(s):  
Machine Learning ◽  
Human Health ◽  
Well Being ◽  
Pathogen Transmission ◽  
Learning Approaches ◽  
Economic Stability ◽  
Ensemble Models ◽  
Network Characteristics ◽  
Key Factor ◽  
Key Aspects

Diseases that spread to humans from animals, zoonoses, pose major threats to human health. Identifying animal reservoirs of zoonoses and predicting future outbreaks are increasingly important to human health and well-being and economic stability, particularly where research and resources are limited. Here, we integrate complex networks and machine learning approaches to develop a new approach to identifying reservoirs. An exhaustive dataset of mammal–pathogen interactions was transformed into networks where hosts are linked via their shared pathogens. We present a methodology for identifying important and influential hosts in these networks. Ensemble models linking network characteristics with phylogeny and life-history traits are then employed to predict those key hosts and quantify the roles they undertake in pathogen transmission. Our models reveal drivers explaining host importance and demonstrate how these drivers vary by pathogen taxa. Host importance is further integrated into ensemble models to predict reservoirs of zoonoses of various pathogen taxa and quantify the extent of pathogen sharing between humans and mammals. We establish predictors of reservoirs of zoonoses, showcasing host influence to be a key factor in determining these reservoirs. Finally, we provide new insight into the determinants of zoonosis-sharing, and contrast these determinants across major pathogen taxa.

Do National Service Programs Improve Subjective Well-Being in Communities?

2018 ◽  
Vol 49 (3) ◽  
pp. 275-291 ◽  
Author(s):  
Kristopher Velasco ◽  
Pamela Paxton ◽  
Robert W. Ressler ◽  
Inbar Weiss ◽  
Lilla Pivnick
Keyword(s):  
United States ◽  
Well Being ◽  
The United States ◽  
Change Score ◽  
National Service ◽  
Subjective Well Being ◽  
Community Effects ◽  
Service Programs ◽  
Open Question ◽  
New Evidence

Since the creation of Volunteers in Service to America (VISTA) in 1964 and AmeriCorps in 1993, a stated goal of national service programs has been to strengthen the overall health of communities across the United States. But whether national service programs have such community effects remains an open question. Using longitudinal cross-lagged panel and change-score models from 2005 to 2013, this study explores whether communities with national service programs exhibit greater subjective well-being. We use novel measures of subjective well-being derived from tweeted expressions of emotions, engagement, and relationships in 1,347 U.S. counties. Results show that national service programs improve subjective well-being primarily by mitigating threats to well-being and communities that exhibit more engagement are better able to attract national service programs. Although limited in size, these persistent effects are robust to multiple threats to inference and provide important new evidence on how national service improves communities in the United States.

scholarly journals Application of Machine Learning Techniques to Predict Mechanical Properties for Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress and elongation at break for polyamide 2200 (also known as PA12). EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models have prediction accuracy higher than 80% only for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about material properties, these models need to be improved in the future based on additional experimental work.

scholarly journals Individual-Level Determinants of Employee Shirking

2005 ◽  
Vol 51 (3) ◽  
pp. 468-487 ◽  
Author(s):  
Timothy A. Judge ◽  
Timothy D. Chandler
Keyword(s):  
United States ◽  
Job Satisfaction ◽  
Health Care ◽  
Work Group ◽  
Well Being ◽  
The United States ◽  
Subjective Well Being ◽  
Negative Effects ◽  
Individual Level ◽  
Organization Level

Employee shirking, where workers give less than full effort on the job, has typically been investigated as a construct subject to organization-level influences. Neglected are individual differences that could explain why employees in the same organization or work-group might shirk. Using a sample of workers from the health care profession in the United States, the present study sought to address these limitations by investigating subjective well-being (a dispositional construct), job satisfaction, as well as other indiuidual-level determinants of shirking. Results indicate that whites shirk significantly more than nonwhites, and that subjective well-being, job satisfaction, and age have significant, negative effects on shirking. The implications of these results are discussed.

Why Do Easterners Have Lower Well-Being Than Westerners? The Role of Others’ Approval Contingencies of Self-Worth in the Cross-Cultural Differences in Subjective Well-Being

2016 ◽  
Vol 48 (2) ◽  
pp. 217-224 ◽  
Author(s):  
Cheng-Hong Liu ◽  
Yi-Hsing Claire Chiu ◽  
Jen-Ho Chang
Keyword(s):  
Cultural Differences ◽  
Well Being ◽  
The United States ◽  
Cross Cultural ◽  
Subjective Well Being ◽  
Cross Cultural Differences ◽  
Two Samples ◽  
Self Worth ◽  
The Cross

Previous studies have shown that Easterners generally perceive themselves as having lower subjective well-being compared with Westerners, and several mechanisms causing such differences have been identified. However, few studies have analyzed the causes of such differences from the perspective of the cross-cultural differences in the meanings of important life events such as whether people receive approval from others. Specifically, events regarding others’ approval might have different meanings to and influences on Easterners and Westerners. Thus, the degree of fluctuation of people’s views of self-worth in response to these events (i.e., others’ approval contingencies of self-worth [CSW]) probably differs between Easterners and Westerners. This may be a reason for cross-cultural differences in subjective well-being. We investigated two samples of undergraduate students from Taiwan and the United States to examine the mediating role of others’ approval CSW in forming cross-cultural differences in subjective well-being. The results revealed that Taiwanese participants exhibited lower subjective well-being and higher others’ approval CSW than American participants. In addition, others’ approval CSW partially mediated the cross-cultural differences in subjective well-being. Thus, one reason for lower subjective well-being among Easterners was likely that their self-esteem was more prone to larger fluctuations depending on whether they receive approval from others in everyday life.

scholarly journals The influence of daily spiritual experiences and gender on subjective well-being over time in cancer survivors

2019 ◽  
Vol 41 (2) ◽  
pp. 159-171
Author(s):  
Myriam Rudaz ◽  
Thomas Ledermann ◽  
Joseph G. Grzywacz
Keyword(s):  
Life Satisfaction ◽  
Cancer Survivors ◽  
Well Being ◽  
The United States ◽  
Moderating Effect ◽  
Subjective Well Being ◽  
Spiritual Experiences ◽  
Daily Spiritual Experiences ◽  
Over Time

Cancer survivors are at risk for poor subjective well-being, but the potential beneficial effect of daily spiritual experiences is unknown. Using data from the second and third wave of the Midlife in the United States (MIDUS) study, we examined the extent to which daily spiritual experiences at baseline moderate the association between subjective well-being at baseline and approximately 10 years later in cancer survivors ( n = 288). Regression analyses, controlled for age, educational attainment, and religious/spiritual coping, showed that daily spiritual experiences moderated the association between life satisfaction at baseline and follow-up. Specifically, high spiritual experiences enhanced life satisfaction over time in cancer survivors with low life satisfaction at baseline. Also, daily spiritual experiences moderated the association between positive affect at baseline and follow-up, though this moderating effect was different for women and men. No moderating effect emerged for negative affect.

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