Influence of Health and Economic-Material Conditions on Subjective Well-Being throughout the Adult Life Cycle

Influence of Health and Economic-Material Conditions on Subjective Well-Being throughout the Adult Life Cycle | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of Health and Economic-Material Conditions on Subjective Well-Being throughout the Adult Life Cycle Pilar Filgueira, Solange Sylvia Rodríguez Espínola This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7892672/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The present study explores the influence of health and economic-material conditions on subjective well-being (SWB) in different age groups in adulthood. Using a multidimensional perspective, SWB is conceptualized as a construct that combines cognitive and affective components reflecting individuals' evaluations of their lives. Using data collected from a representative sample, the research examines how different life circumstances, such as physical health, access to resources, and socioeconomic conditions, affect perceptions of SWB. The results reveal significant differences in SWB between age groups, highlighting the fundamental role of health and economic-material factors in shaping the well-being of individuals. Younger adults have higher levels of SWB, while middle and older adults show notable declines. The study underlines the need for policies aimed at reducing health and economic inequalities, taking into account the specific challenges faced by different age groups. These results contribute to a deeper understanding of the determinants of SWB at different stages of life and offer insights for developing interventions aimed at improving quality of life. subjective well-being health conditions economic-material factors adulthood age groups inequality Argentina INTRODUCTION Villatoro ( 2012 ) conceptualizes subjective well-being (SWB) as a multidimensional construct that encompasses the various evaluations that people make about their lives, their experiences and the circumstances that surround them. Complementarily, the Organization for Economic Cooperation and Development (OECD, 2013) defines it as a positive state of mind that integrates both positive and negative evaluations of life, as well as emotional reactions associated with life experiences. These definitions highlight the complex and dynamic nature of SWB, which is not limited to momentary emotional states, but also includes broader cognitive evaluations of life as a whole. SWB is composed of a combination of affective and cognitive components that reflect the subjective appraisals people make about their lives (Diener, 1994 ; Diener et al., 2017 ; Diener & Lucas, 1999 ; Glatzer, 2015 ; Sheldon, 2013 ). In this regard, Diener et al. ( 2018 ) and Tov ( 2018 ) emphasize that SWB involves both immediate estimations and more reflective appraisals. This approach highlights the need to analyze its dimensions separately to understand how different factors influence SWB. Diener ( 1984 ) proposes two main perspectives to explain variations in SWB: the top-down approach and the bottom-up approach. The former focuses on the subjective processing of circumstances, emphasizing that the impact of the environment on well-being is mediated by individual interpretation. In contrast, the bottom-up approach attributes greater importance to external circumstances, suggesting that specific life conditions directly influence SWB levels. According to this perspective, SWB is linked to factors such as personal, sociopolitical, community, economic, and environmental context (Diener et al., 2018 ; Luhmann, 2017 ). Helliwell et al. ( 2018 ) argue that material and health conditions play a crucial role in SWB. Although studies have found a consistent correlation between SWB and these life domains, a clear causal direction has not yet been established. This uncertainty is especially relevant for public policy design, as it directly influences recommendations for improving people's quality of life. Although the empirical study of SWB has advanced significantly in recent decades (Diener, 2013; Diener & Tov, 2012 ), the analysis of inequality in this domain remains limited. Most SWB research has been conducted in developed Western countries and in highly educated populations, leaving important questions about the applicability of these findings to cross-national contexts and diverse socioeconomic strata (Diener et al., 2018 ). Oishi and Gilbert ( 2016 ) highlight that both the concept of SWB and its predictors vary culturally. In this sense, their meaning and relevance may differ across countries and over time, underscoring the need to contextualize their study within the specific social, economic, and political conditions of each region. In Latin America, and specifically in Argentina, there is a significant theoretical gap in the study of SWB and its determinants (Ciocchini et al., 2010 ; Rojas & García Vega, 2017 ). Although there is research related to aspects such as quality of life, poverty or inequality, few studies comprehensively address the subjective assessment of well-being. This theoretical gap is exacerbated by the cultural, social and economic diversity of the region, which may influence how people perceive and evaluate their well-being (Rojas, 2018 ). In addition, inequalities in terms of access to basic services, economic development, and family structures also affect these perceptions. To advance in this field, it is necessary to develop interdisciplinary and contextualized research that integrates sociological, economic and cultural perspectives. The analysis of SWB throughout the life cycle has also generated multiple perspectives. On the one hand, some studies argue that SWB is relatively stable due to genetic predispositions and personality characteristics (Lykken & Tellegen, 1996 ). On the other hand, others suggest that SWB is influenced by specific experiences and circumstances, implying some plasticity in SWB levels over time (Lansford, 2018 ). This debate has led to the development of homeostatic adaptation theory, which posits that people tend to return to a baseline level of well-being even after significant positive or negative events. However, this return is not always uniform and may vary in magnitude and pace depending on circumstances (Luhman et al., 2012). Hernangómez et al. ( 2014 ) suggest that oscillations in SWB are associated with the adaptations required at each stage of life. Erikson ( 1985 ), for his part, describes eight stages of human development, each marked by specific crises that can influence well-being. Guardini ( 1997 ) reinforces this idea by stressing that well-being is intrinsically related to the ability to find meaning in each stage of the life cycle. Adulthood, which is divided into young, middle and late stages, is particularly relevant for SWB analysis (Griffa & Moreno, 2015 ). During young adulthood, the search for independence and the construction of a life project are priorities (Garcia-Alvarez et al., 2022). In middle adulthood, people usually experience greater emotional balance, although this stage is also marked by reevaluations of goals and challenges related to aging and responsibilities (Mroczek, 2004 ). Finally, in late adulthood, health and social support become determinants of SWB maintenance (Hansen & Blekesaune, 2022 ). The relationship between SWB and age does not follow a universal pattern. While some studies suggest a U-shaped curve, with a minimum in middle adulthood (Blanchflower & Oswald, 2019 ), others highlight that SWB declines steadily in less developed contexts (Bauer et al., 2016 ; Steptoe et al., 2015 ). Factors such as social relationships, aspirations and cultural context have a significant impact on these variations (Oishi et al., 1999 ; Rojas, 2018 ). In Latin America, the need for specific and contextualized studies is even more urgent due to the social and cultural particularities of the region. This perspective underscores the importance of considering both structural factors and individual and cultural dynamics to better understand SWB. The objective of this study is to explore how health conditions and economic-material circumstances influence SWB throughout the different stages of the life cycle. METHOD Research design A study was conducted with a cross-sectional quantitative approach, with an explanatory scope (Hernández-Sampieri & Mendoza Torres, 2018). Participants A sample of adults from households distributed in 960 sampling points in Argentina was used. The study domain consisted of twenty urban agglomerates of 80,000 inhabitants or more, grouped into three large conglomerates according to their size: (a) Greater Buenos Aires: Ciudad Autónoma de Buenos Aires and Conurbano Bonaerense (Conurbano Zona Norte, Conurbano Zona Oeste and Conurbano Zona Sur); (b) Other metropolitan areas: Greater Rosario, Greater Córdoba, San Miguel de Tucumán and Tafí Viejo, and Greater Mendoza; and (c) Rest of Urban: Mar del Plata, Greater Salta, Greater Paraná, Greater Resistencia, Greater San Juan, Neuquén, Plottier, Cipolletti, Zárate, La Rioja, Goya, San Rafael, Comodoro Rivadavia, Ushuaia and Río Grande. The sampling was probabilistic and multistage, with a first stage of clustering and a second stage of stratification. The selection of sample radii within each agglomerate and stratum was random and weighted by the number of households in each radius. The blocks within each sampling point and the households within each block were randomly selected through systematic sampling, while the individuals within each dwelling were selected through a system of sex and age quotas. A first stratification criterion defined the information analysis domains according to regional membership and population size of the agglomerates. A second criterion referred to a socioeconomic criterion of the households. This criterion was established in order to optimize the final distribution of the survey points. The sampling error was +/- 1.3%, with an assessment of a population proportion of 50% and a confidence level of 95%. The persons eligible to participate in the study were adults 18 years of age and older who met the established quotas for sex, age and activity status. The sample consisted of 5950 adults residing in urban areas of 80,000 inhabitants or more in Argentina. The 29.9% (n=1778) resided in Greater Buenos Aires, 40.3% (n=2398) in Other Metropolitan Areas, and the remaining 29.8% (n=1774) in the Rest of Urban Areas. The participants were 2720 (45.7%) men and 3230 (54.3%) women, whose ages ranged from 18 to 96 years (SD= 43.67; SD= 17.28). When divided into age groups, it was observed that 36.7% (n= 2183) of the participants were between 18 and 34 years old, 42.1% (n= 2506) between 35 and 59 years old, and the remaining 21.2% (n= 1261) were 60 years old or older. Data collection variables and techniques The Argentine Social Debt Survey (EDSA; Salvia et al., 2023) is a multipurpose questionnaire that addresses different dimensions and components of human and social development, and is the only one in Argentina that surveys the number of indicators necessary to carry out the proposed analysis. The survey is carried out between July and October of each year, on a probabilistic sample representative of households and individuals residing in twenty urban agglomerates in the country during the reference period. The protocol to be administered was approved by an Institutional Review Board of the Pontificia Universidad Católica Argentina, and all participating subjects gave their verbal informed consent. In 2021, the EDSA (Salvia et al., 2023) was composed of 344 items. The initial six items were aimed at inquiring information about the neighborhood and the housing quality of the surveyed dwelling. The next forty questions collected data on the composition of each household, gathering information on each of its members. The rest of the questions were distributed in three large modules. The first of these, the Household Module , was composed of 96 questions, divided into five sections: (a) Protection and shelter; (b) Economic situation of the household; (c) Access to food; (d) Eating; and (e) Safety and bodily integrity. In the second module, the Individual Module , 140 questions were grouped into seven sections: (a) Health; (b) Democracy and civic life; (c) Beliefs, practices and religious affiliations; (d) Work and income; (e) Relationship with others; (f) Attitudes and skills; and (g) Gender violence, a section aimed only at female respondents. The last module, the Childhood Module , was only administered to those persons who had said they lived with children under 18 years of age in the household. It consisted of 62 questions that the adult had to answer about each of the children and/or adolescents living in the household. The data included in this study were extracted from the survey described above. The data collection techniques used to measure each of the variables are detailed below. Age In the initial section of the EDSA (Salvia et al., 2023), participants were asked, as an open-ended question, to state their age in terms of years completed. Subjective well-being The SWB is an index that was constructed by calculating the sum of the scores obtained for the quality of life, life satisfaction and happiness items. These items are included in the Attitudes and Skills section of the Individual Module of the EDSA (Salvia et al., 2023). A positive significant correlation was found both between happiness and quality of life ( r = .525; p ≤ .01), and between happiness and satisfaction with life ( r = .531; p ≤ .01). A significant positive correlation was also demonstrated between quality of life and satisfaction with life ( r = .738; p ≤ .01). To assess the internal consistency of the SWB construct, consisting of happiness, quality of life and life satisfaction, an exploratory factor analysis was performed using the principal component analysis method ( KMO =.679; Bartlett's test of sphericity X 2 (3) = 6985.137; p ≤ .001). Thus, the null hypothesis was rejected, and it was found that 73.41% of the variance is explained by a single component. Happiness Data on happiness were collected by means of a question that asked the participant to answer how happy he/she considers him/herself to be on a four-choice Likert-type scale (4= Very happy , 3= Happy , 2= Not very happy , 1= Not at all happy ). Quality of life Quality of life was measured by a question that asked the participant to rate his or her quality of life in the last two weeks. The item contained a five-option Likert-type scale (5= Poor , 4= Fair , 3= Good , 2= Very good , 1= Excellent ). Satisfaction with life In a similar way, life satisfaction was assessed by asking the individual to indicate how satisfied he/she has been with his/her life in the last two weeks. This item also had a Likert scale with five options (5= Totally dissatisfied , 4= Fairly dissatisfied , 3= Satisfied , 2= Fairly satisfied , 1= Totally satisfied ). Health factors In line with the definition of health proposed by the World Health Organization as to "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity " (WHO, 2014, p. 1), the present study took into account the three dimensions of health: physical, mental and social. Physical health: Perceived state of health In the Health section of the EDSA (Salvia et al., 2023) Individual Module there is an item that refers to the perceived health status (PHS) of the participants. In it the subject was asked what his or her general health status is, and four response options were given (1= Has no health problems , 2= Has a few health problems , 3= Has quite a few health problems , 4= Has chronic or serious health problems ), from which the subject had to select one . Mental health The Kessler Psychological Distress Scale (KPDS-10; Kessler & Mrozeck, 1994), an instrument designed to measure the prevalence of nonspecific psychological distress, was used to assess mental health (MH). It consists of ten items that refer to how the person felt in the last month with respect to different symptoms related to depression (e.g., did you feel tired for no reason in the last month?) and anxiety (e.g., did you feel nervous in the last month? ), which are evaluated according to a five-point Likert-type scale, ranging from 1 ( always ) to 5 ( never ). In the Attitudes and Skills section of the Individual Module of the EDSA, the Argentinean adaptation conducted by Brenlla and Aranguren (2010) was administered. To demonstrate that the KPDS-10 is an appropriate instrument to efficiently measure the psychological distress presented by the country's inhabitants, in that study an initial exploratory factor analysis was carried out on a sample corresponding to the general population using the method of principal axes (KMO= .90; Bartlett's test of sphericity X 2 (45) = 11851.21; p < .001). The presence of a single factor accounted for 45.5% of the total variance. Next, they performed an exploratory factor analysis on a sample of patients using the same method ( KMO= .851; Bartlett's Test of Sphericity X 2 (45) = 396.325; p < .001). This time, they found two factors that explained 64.3% of the total variance: the first factor, which comprised 54.2% of the variance, grouped nine items; the second factor, which collected only one item, explained 10.1% of the variance. In addition, they obtained very satisfactory measures of reliability (α = .88). On the other hand, they conducted convergent validity analyses, in which significant positive correlations were found with the Spielberger Anxiety Scale (r = .849, p < .01; r = .736, p < .001) (Spielberger et al., 1983), the Beck Depression Inventory ( r = .898, p < .01; r = .624, p < .001) (Beck et al., 2006) and the SCL-90-R ( r = .764; p < .01) (adapted from Casullo, 2004). In turn, Brenlla and Aranguren (2010) evaluated the stability of the scores by means of a test-retest in two subsamples. The results found, in both cases, were satisfactory ( r = .365, p < .001; r = .445, p < .001). At the same time, it was found that men presented lower indices of psychological distress than women, and no significant differences were found when analyzing distress according to age. Finally, they tested the criterion validity by contrasting a patient group with a general population group, and observed that the mean scores of the participants belonging to the second group were significantly lower than those of the patient group ( t (197) = -9.316, p < .001). They performed a study of ROC curves to distinguish between patients and general population subjects. With respect to the administration of the KPDS-10 in the present study sample, very satisfactory measures of reliability were obtained (α = .91). Social health: Perceived social support The MOS Perceived Social Support Questionnaire (Sherbourne & Stewart, 1991) is a 20-item scale that assesses individuals' perception of their social support. The first item refers to structural social support, inquiring about the size of the social network by means of an open-ended question, and the remaining 19 items inquire about functional social support by means of questions with Likert-type response options. The adaptation of the MOS Questionnaire for the Argentine population was carried out by Rodríguez Espínola and Enrique (2007). An abbreviated version by Garofalo et al. (2023) was administered in the Attitudes and Skills section of the Individual Module of the EDSA. Structural social support The first item of the MOS Questionnaire (Sherbourne & Stewart, 1991) refers to the number of friends and family members that the person perceives to have with whom he/she feels at ease and can talk about everything; this item was kept unchanged, both in the Argentine adaptation and in the abbreviated version. Thus, the respondent was asked, as an open-ended question, to indicate an approximate number of friends and relatives. The total score of structural social support (SSS) was obtained by adding the number of friends and relatives the subject said he/she had. Functional social support The MOS Questionnaire (Sherbourne & Stewart, 1991) analyzes functional social support (FSS) by means of 19 items with five-point Likert-type response options. Each item refers to one of the four dimensions of functional social support: (a) emotional, (b) instrumental, (c) informational, and (d) affective. In their adaptation, Rodríguez Espínola and Enrique (2007) carried out a confirmatory factor analysis for the factor structure proposed by the original questionnaire in a general sample of 375 cases, which revealed the existence of three factors that explained 59.86% of the variance. To study the reliability and validity of the instrument, the authors of the adaptation conducted an exploratory factor analysis using the principal components method with Varimax rotation ( KMO= .935). The items presented a three-factor distribution: the first factor, corresponding to informational functional support, explained 45.48% of the total variance; the second, corresponding to affective functional support, 7.44%, and the third, corresponding to instrumental social support, 6.94%. To measure the internal consistency of the questionnaire, Rodríguez Espínola and Enrique (2007) used Cronbach's alpha coefficient. The instrument presented high reliability indices, both in the general measurement of functional social support (α = .919) and in each of its dimensions (α of informational social support= .876; α of affective social support= .843; α of instrumental social support= .802). In the abbreviated version (Garofalo et al., 2023), functional social support is measured in the MOS Questionnaire by a selection of nine of the 19 items (see Table 1), with four-choice Likert-type responses (4= Never , 3= A few times , 2= Many times , 1= All the time ). Table 1 Items of functional social support included in the abbreviated version of the MOS Questionnaire Functional social support dimension Item Instruments 1. How often do you have someone to prepare food for you if you are unable to do so? 2. How often do you have someone to help you with your household chores if you are sick? 3. How often do you have someone to help you with remedies if you get sick? Affective 4. How often do you have someone to love and make you feel loved? 5. How often do you have someone to show you love and affection? 6. How often do you have someone to hold you? Informational 7. How often do you have someone you can count on when you need to talk? 8. How often do you have someone to inform you and help you understand a situation? 9. How often do you have someone to advise you on how to solve your personal problems? Source : Own elaboration based on the study conducted by Garofalo et al. (2023). To validate the abbreviated questionnaire, Garofalo et al. (2023) conducted an exploratory factor analysis on a sample of 208 cases ( KMO= .80; Bartlett's test of sphericity X 2 (36) = 1382.10; p < .001), for which the extraction of the principal components was carried out with the Varimax extraction method. Three factors were found that explained 81.5% of the total variance; the first of them comprised 62.4%, the second 11.1%, and the third 8.05%. They then performed a confirmatory factor analysis on a sample of 222 participants. For this, they developed a structural equation model that achieved a satisfactory fit to the empirical data ( CFI= .966; NFI= .952; RMR= .037). In addition, they obtained very satisfactory measures of reliability, both in the assessment of total functional social support (α = .93) and in the affective (α = .90), instrumental (α = .85) and informational (α = .88) dimensions. Very satisfactory measures of reliability in the assessment of total functional social support (α = .95) were achieved in the administration of the abbreviated version of the Argentine adaptation of the MOS Questionnaire in the sample of participants in the present study. Economic and material factors Giarrizzo (2010) postulates that economic-material well-being can be measured by means of different indicators, which are divided into two large groups: (a) objective indicators , and (b) subjective indicators . Objective indicators, in turn, can be monetary , consisting of traditional economic measurements that include measures of income, or non-monetary , which include other factors that make up economic well-being. On the other hand, subjective indicators focus on the perception that each individual has of his or her economic-material well-being. This study addresses monetary income as an objective monetary indicator, social and economic rights dimensions as objective non-monetary indicators, and perceived income sufficiency as a subjective indicator. Monetary income To calculate monetary income (MI) we took the per capita household income, surveyed by asking two questions: (a) "How many people live in your household (including children, babies and domestic help with bed)?"; and (b) "In total, adding up what all the people living in the household contribute plus what you may receive from other people or from unemployment insurance or social plans, how much was the household's total income last month?" These questions are found in the Household Protection and Shelter and Household Economic Situation sections of the EDSA (Salvia et al., 2023) Household Module. Dimensions of social and economic rights The items referring to the measurement of the dimensions of social and economic rights can be found in the first section, the Household Module , the Work and Income section of the Individual Module, and the Childhood Module of the EDSA (Salvia et al., 2023). First, the basic services dimension (BSD) was observed through three indicators: (a) connection to a running water network, (b) connection to a sewage network, and (c) access to the energy network. These were measured by means of three questions with dichotomous response options (1= Yes , 2= No ). The housing dimension (HD) was evaluated by means of three indicators: (a) overcrowding, (b) precarious housing, and (c) sanitation. The first was recorded when a household had three or more people per room, dividing the number of household members by the number of rooms in the dwelling that could be used as bedrooms. Precarious housing was investigated by asking or observing, depending on the survey modality, the type of housing and the materials with which it was constructed. The items were multiple choice. To evaluate the sanitary service, multiple choice questions were used to determine the presence or absence of at least one toilet in the dwelling and the type of flush it had. On the other hand, the environment dimension (ED) was observed through the presence or absence, in the vicinity of the household, of garbage dumps, polluting factories and water bodies. The educational access dimension (EAD) was surveyed by means of three indicators, constructed on the basis of questions with a closed response option, and were categorized taking into account current education regulations: (a) non-attendance was recorded in the case of minors who did not attend school or those adults who had not attended high school; (b) middle school educational lag was considered in the cases of adults born between the years 1983 and 1989 who had not completed up to the second year of high school, in the case of those between 19 and 24 years old and attending formal educational institutions, educational lag deficit was not considered; and (c) educational backwardness in elementary school was recorded in the case of persons born before 1983 who had not completed elementary school. Finally, the employment and social security dimension (ESD) was observed through questions with closed response options that accounted for the income registered in the social security system from dependent or self-employed jobs or from pensions and/or contributory pensions. The EDSA also studies the food and health dimension (FHD), which was not included in this work due to its overlap with other study variables, being the only dimension studied by the ODSA to be excluded from the data analysis. Perceived income sufficiency In the Household Economic Situation section of the EDSA (Salvia et al., 2023) Household Module , perceived income sufficiency (PIS) was measured by a question that asks the participant to indicate whether the money they collect per month in their household is enough to live on. The item contains three response options: 1= The money is enough and they can save some , 2= It is enough, but they cannot save , 3= It is not enough . From this item, a new dichotomous variable was calculated in which the categories were: 0= Insufficient income , 1= Sufficient income . Data Analysis Procedure First, a data matrix was constructed with the data collected in the IBM SPSS ® 25.0 program. The data were preprocessed, discarding those cases that did not meet the inclusion criteria and those that were presented as missing cases . In order to proceed with the data analysis, the per capita family income variable was calculated and the variables of health status, mental health, functional social support, perceived sufficiency of income, basic services dimension, housing dimension, environment dimension, educational access dimension, and employment and social security dimension were fine-tuned. To calculate per capita family income, we calculated the ratio between total household income and the number of members living in the household. Since income is a variable that is not usually normally distributed normally, econometrics recommends that it be replaced by its logarithm in order to better comply with the assumptions of the regression model and achieve a better fit (Stock & Watson, 2012). Thus, per capita income was evaluated by calculating the logarithm of the mean of the total real household income divided by the number of members, expressed in constant pesos of the third quarter of 2021. Then, the order of the response categories of the scales corresponding to the variables of health status, mental health, functional social support, perceived adequacy of income, basic services dimension, housing dimension, environment dimension, educational access dimension, and employment and social security dimension was reversed so that all variables had the same meaning: the higher the score, the better the perception of health and economic-material conditions. Next, the quality of life and life satisfaction variables were recoded so that they had the same number of response options as the happiness variable and the order of the response options was reversed. For quality of life the categories were as follows: 1= Bad , 2= Fair , 3= Good , 4= Very good/Excellent . On the other hand, for satisfaction with life, the categories were as follows: 1= Totally dissatisfied , 2= Fairly dissatisfied , 3= Satisfied , 4= Fairly/Totally satisfied . Based on the central limit theorem (Alvarado & Batanero, 2006), a normal distribution of these variables was assumed for the sample due to the considerable sample size (n= 5950). To compare and relate the variables of happiness, quality of life and life satisfaction, a Pearson's r correlation analysis was performed. A confirmatory factor analysis was performed to evaluate the internal consistency of the SWB construct, and Cronbach's alpha was calculated to measure its reliability. Finally, the scores of the happiness, quality of life and life satisfaction variables were summed to obtain the SWB index, and the z-score corresponding to the total score of the index was calculated, translating it to a scale from 0 to 100. Finally, the sample was segmented into three groups according to the age of the participants, and a stepwise linear regression model was carried out to determine the effects of inequalities in health and economic-material conditions on SWB in each age group. RESULTS In order to study the effects that inequalities in health, social and economic-material conditions generate in the SWB at each stage of adult life, the sample was segmented into three groups according to the life cycle stages of the participants, following the aforementioned criteria: (a) Young adulthood , composed of 2182 individuals aged 18 to 34 years; (b) Middle adulthood , composed of 2504 participants aged 35 to 59 years; and (c) Late adulthood , comprising 1261 people aged 60 years or older. Then, a stepwise linear regression analysis was performed for each group with SWB as the dependent variable, and with MH, PSH, SSS, FSS, MI, BSD, HD, ED, EAD, ESD, ESD, and PIS as predictor variables. The results obtained are detailed in the following subsections. Factors Affecting Subjective Well-Being In Young Adulthood First, the correlation between the variables of interest was analyzed using Pearson's r coefficient (see Table 2). SWB presented significant correlations with all variables, but the strongest were with MH (r= .401; p ≤ .01), FSS (r= .279; p ≤ .01), and PIS ( r= .278; p ≤ .01). The vast majority of correlations were significant, with the exception of that of the MH with the BSD ( p= .086); the PSH with the SSS ( p= .012), the FSS ( p= .015), MI ( p= .023), BSD ( p= .401), EAD ( p= .075), ESD ( p= .029), and PIS ( p= .003); and that of FSS with MI ( p= .013) and ED ( p= .043). Table 2 Correlation analysis between the variables of interest with Pearson's r coefficient. Young adulthood. SWB MH PSH SSS FSS MI BSD HD ED EAD ESD PIS SWB 1 .401** .152** .224** .279** .208** .108** .189** .169** .212** .178** .278** MH .401** 1 .256** .233** .191** .141** .029 .112** .191** .135** .114** .212** PSH .152** .256** 1 .049 .047 .043 .005 .052** .051** .031 .041 .059** SSS .224** .233** .049 1 .178** .129** .064** .095** .101** .053** .124** .133** FSS .279** .196** .047 .178** 1 .048 .068** .080** .037 .092** .108** .099** MI .208** .141** .043 .129** .048 1 .271** .323** .230** .366** .372** .449** BSD .108** .029 .005 .064** .068** .271** 1 .370** .320** .272** .244** .192** HD .189** .112** .052** .095** .080** .323** .370** 1 .315** .340** .275** .242** ED .169** .191** .051** .101** .037 .230** .320** .315** 1 .236** .166** .186** EAD .212** .135** .031 .053** .092** .366** .272** .340** .236** 1 .307** .324** ESD .178** .114** .041 .124** .108** .372** .244** .275** .166** .307** 1 .324** PIS .278** .212** .059 .133** .099** .449** .192** .242** .186** .324** .324** 1 Note. ** Correlation is significant at the .01 level (bilateral). For the 18-34 age group, seven models were generated from the regression (see Table 3). The final model obtained an acceptable level of determination ( F (1,2174) = 109.897; R= .511; R 2 = .261; Adjusted R 2 = .259; EER= 14.783), and its coefficient of variance explained was 26.1%. The main predictor variable for SWB turned out to be MH ( β= .282; t= 13.956; p ≤ .001), followed by FSS ( β= .178; t= 9.348; p ≤ .001), PIS ( β= .142; t= 7.074; p ≤ .001), HD ( β= .069; t= 3.494; p ≤ .001), SSS ( β= .094; t= 4.910; p ≤ .001), EAD ( β= .081; t = 3.969; p ≤ .001), and PSH ( β= .052; t= 2.757; p ≤ .05). The model excluded MI ( t= 1.917; p = .055), BSD ( t= 0.393; p = .694), ED ( t= 1.680; p= .093), and ESD ( t= 1.368; p= .171), as their predictive values on SWB were not shown to be significant. The equation summarizing the model would be the following: SWB = 12.353 + 0.740*MH + 0.543*FSS + 5.068*PIS + 2.897*HD + 0.257*SSS + 2.992*EAD + 1.762*PSH. Table 3 Stepwise linear regression model on SWB. Young adulthood. Predictor variables B β t R 2 F p MH .740 .282*** 13.956 .261 109.897 .000 FSS .543 .178*** 9.348 PIS 5.068 .142*** 7.074 HD 2.897 .069*** 3.494 SSS .257 .094*** 4.910 EAD 2.992 .081*** 3.969 PSH 1.762 .053* 2.757 Note. * p ≤ .05; ** p ≤ .01; *** p ≤ .001; β = standardized beta. Factors Affecting Subjective Well-Being In Middle Adulthood When analyzing the correlation between the variables of interest using Pearson's r coefficient (see Table 4), it was observed that SWB presented significant correlations with all variables, but the strongest were with MH (r= .479; p ≤ .01), PIS ( r= .373; p ≤ .01), and FSS ( r= .338; p ≤ .01). All correlations were significant, except that of the PSH with the BSD ( p= .026), and that of the FSS with the EAD ( p= .012). Table 4 Correlation analysis between the variables of interest with Pearson's r coefficient. Middle adulthood. SWB MH PSH SSS FSS MI BSD HD ED EAD ESD PIS SWB 1 .479** .277** .280** .338** .271** .125** .203** .210** .197** .255** .373** MH .479** 1 .312** .218** .234** .221** .098** .096** .242** .146** .163** .293** PSH .277** .312** 1 .109** .105** .104** .039 .053** .083** .066** .134** .193** SSS .280** .218** .109** 1 .222** .166** .128** .109** .137** .088** .143** .191** FSS .338** .234** .105** .222** 1 .061** .060** .088** .053** .045 .177** .177** MI .271** .221** .104** .166** .061** 1 .256** .289** .264** .318** .383** .456** BSD .125** .098** .039 .128** .060** .256** 1 .333** .263** .245** .264** .208** HD .203** .096** .053** .109** .088** .289** .333** 1 .258** .288** .261** .245** ED .210** .242** .083** .137** .053** .264** .263** .258** 1 .179** .177** .218** EAD .197** .146** .066** .088** .045 .318** .245** .288** .179** 1 .268** .267** ESD .255** .163** .134** .143** .177** .383** .264** .261** 177** .268** 1 .383** PIS .373** .293** .193** .191** .177** .456** .208** .245** .218** .267** .383** 1 Note. ** Correlation is significant at the .01 level (bilateral). From the regression, nine models were created for the 35-59 age group (see Table 5). The final model presented a satisfactory level of determination ( F (1,2494) = 161.799; R= .607; R 2 = .369; Adjusted R 2 = .366; EER= 14.743), and an explained variance coefficient of 36.9%. This model yielded MH ( β= .307; t= 17.188; p ≤ .001) as the main predictor variable for SWB, followed by PIS ( β= .144; t= 7.488; p ≤ .001), FSS ( β= .188; t= 11.149; p ≤ .001), SSS ( β= .108; t= 6.411; p ≤ .001), the PSH ( β= .105; t= 6.411; p ≤ .001), the HD ( β= .067; t = 3.885; p ≤ .001), the MI ( β= .049; t= 2.543; p ≤ .05), the HD ( β= .044; t= 2.541; p ≤ .05), and the BSD ( β= .039; t= 2.117; p ≤ .05). This model excluded BSD ( t= -1.412; p= .158) and ED ( t= 1.744; p= .081), since their predictive values on SWB were not significant. The model equation would be: SWB= -9.997 + 0.850*MH + 5.469*PIS + 0.589*FSS + 0.298*SSS + 2.534*PSH + 3.327*HD + 2.472*MI + 2.014*EAD + 1.540*ESD. Table 5 Stepwise linear regression model on SWB. Middle adulthood. Predictor variables B β t R 2 F p MH .850 .307*** 17.188 .369 161.799 .000 PIS 5.469 .144*** 7.488 FSS .589 .188*** 11.149 SSS .298 .108*** 6.411 PSH 2.534 .105*** 6.215 HD 3.327 .067*** 3.885 MI 2.472 .049* 2.543 EAD 2.014 .044* 2.541 ESD 1.540 .039* 2.117 Note. * p ≤ .05; ** p ≤ .005; *** p ≤ .001; β = standardized beta. Factors Affecting Subjective Well-Being In Late Adulthood First, the correlation between the variables of interest was studied using Pearson's r coefficient (see Table 6). It was observed that SWB presented significant correlations with all variables, except, with DE ( p= .084). The strongest relationships shown by SWB were with MH (r= .468; p ≤ .01), FSS (r= .373; p ≤ .01), and PSH ( r= .351; p ≤ .01). Most of the remaining correlations were significant, with the exception of that of PSH with HD ( p= .040) and ESD ( p= .043); SSS with HD ( p= .056) and ESD ( p= .133); FSS with MI ( p= .067), ED ( p= .368), HD ( p= .059), and ESD ( p= .012); and that of ED with ESD ( p= .070). Table 6 Correlation analysis between the variables of interest with Pearson's r coefficient. Late adulthood. SWB MH PSH SSS FSS MI BSD HD ED EAD ESD PIS SWB 1 .468** .351** .194** .373** .105** .103** .106** .039 .112** .093** .258** MH .468** 1 .318** .157** .230** .113** .094** .079** .080** .104** .097** .251** PSH .351** .318** 1 .134** .136** .095** .081** .049 .116** .138 .048 .210** SSS .194** .157** .134** 1 .214** .068** .125** .088** .097** .045 .031 .118** FSS .373** .230** .136** .214** 1 -.042 .074** .076** .010 .044 .063 .161** MI .105** .113** .095** .068** -.042 1 .193** .193** .154** .259** .268** .374** BSD .103** .094** .081** .125** .074** .214** 1 .295** .201** .214** .153** .155** HD .106** .079** .049 .088** .076** .193** .295** 1 .146** .174** .144** .148** ED .039 .080** .116** .097** .010 .154** .201** .146** 1 .084** .042 .073** EAD .112** .104** .138** .045 .044 .259** .214** .174** .084** 1 .115** .149** ESD .093** .097** .048 .031 .063 .268** .153** .144** .042 .115** 1 .221** PIS .258** .251** .210** .118** .161** .374** .155** .148** .073** .149** .221** 1 Note: ** Correlation is significant at the .01 level (bilateral). Finally, from the regression, five models were created for the 60 and older age group (see Table 7). The final model showed an acceptable level of determination ( F (1,1255) = 130.361; R= .585; R 2 = .342; Adjusted R 2 = .339; EER= 15.688), and an explained variance coefficient of 33.9%. As with the models developed for the other groups, the final model yielded MH as the main predictor variable for SWB ( β= .319; t= 12.711; p ≤ .001), but this time it was followed by FSS ( β= .247; t= 10.262; p ≤ .001), PSH ( β= .189; t= 7.698; p ≤ .001), PIS ( β= .092; t = 3.825; p ≤ .001), and SSS ( β= .55; t= 2.307; p ≤ .05). The model excluded I ( t= 1.109; p= .268), BSD ( t= 0.802; p= .423), HD ( t= 1.506; p= .132), ED ( t= -1.009; p= .313), EAD ( t= 1.129; p= .259), and SSS ( t= 0.700; p= .484), because they did not show significant predictive values on SWB. The model equation would be conformed as follows: SWB= -6.713 + 0.952*MH + 0.768*FSS + 4.446*PSH + 3.681*PIS + 0.149*SSS. Table 7 Linear stepwise regression model on BS. Late adulthood. Predictor variables B β t R 2 F p MH .952 .319*** 12.711 .342 130.361 .000 FSS .768 .247*** 10.262 PSH 4.446 .189*** 7.698 PIS 3.681 .092*** 3.825 SSS .149 .055* 2.307 Note. * p ≤ .05; ** p ≤ .005; *** p ≤ .001; β = standardized beta. DISCUSSION AND CONCLUSIONS Taking into account both what Lansford (2018) postulated regarding SWB and its associated factors being linked to evolutionary development, and repairing on the results of the present study that found that the level of SWB oscillates significantly according to age, the sixth specific objective explored the effects generated by health and economic-material conditions on SWB according to age groups. The analysis of the data from the present study indicated that the factors affecting SWB are not the same, nor do they have the same weight, in young adulthood as in middle or late adulthood. These findings would agree with what is postulated by Boarini et al. (2012) and Lansford (2018), who argue that people's perceptions of what is important to their well-being change significantly across the lifespan. It could be noted that MH is the condition that most contributes to SWB in all stages of the adult life cycle. As explained in a previous subsection, this would confirm the important influence that anxious and depressive symptomatology exerts on people's evaluation of their lives. In turn, it was found that the HSP and social support also influence the level of SWB throughout adulthood. These results would seem to be in agreement with the importance that both WHO (2014) and other scholars attach to physical well-being (Boehm, 2018; Diener et al., 2017; Razo González et al., 2018; Veenhoven, 2008), mental (Greenspoon & Saklofske 2001) and social (Chu et al., 2010; Lakey & Tanner, 2013; Liu et al., 2016; Melrose et al, 2015; Nguyen et al. 2022; Rojas, 2018; San Martín & Barra, 2013; Vera Noriega et al., 2016) as necessary conditions to achieve a fulfilling life. On the other hand, although the results showed that PSH has an effect on SWB throughout the adult life cycle, its significance was lower in young adulthood than in other age groups, which would reflect that the individual's perception of the importance of physical health for his or her well-being changes over time, becoming more relevant the older the person is (Cuadra-Peralta et al., 2016). This could be explained taking into account that aging is a natural process that spans a lifetime and that, over the years, the body suffers a gradual wear and tear, causing diseases or limitations in organic functionality (De Juanas Oliva et al., 2013). That is why younger people, whose body is fully functional and does not yet show signs of deterioration, tend to have fewer concerns about their physical health (Griffa & Moreno, 2015); then, the PSH, despite denoting some incidence on SWB, would not prove to be such a predominant factor as it is in later stages. With the passage of time, individuals become aware of the diseases and physical limitations that occur with age, which would explain that the HSP acquires greater explanatory power on SWB over other conditions the older the age (Erikson, 1985; Griffa & Moreno, 2015; Guardini, 1997; Razo González et al., 2018). In turn, we tested the postulate that, in explaining SWB in adulthood, the quality of ties is more important than the size of the support network (Ryan & Willits, 2007; Srivastava & Muhammad, 2021). This occurs at all stages, but is notable in late adulthood, where the weight of SSS on SWB is less significant than in the other age groups. This could be explained by two factors. First, Griffa and Moreno (2015) explain that at this stage the social support network is reduced due to a notable loss of significant ties; people, as they age, must face both the absence or death of family and friends, as well as the limitation in their own social capabilities, caused by the physical and mental deterioration characteristic of natural aging. Secondly, late adults tend to become more selective when establishing ties due to their life experience, which would generate a reduction in the size of their social support network (Hernángomez et al., 2014). Thus, the relevance that people give to the number of relationships that make up their network is diminished, but the satisfaction of the affective, instrumental and informational functions continues to be as important as in previous stages. In turn, the results showed that objective economic-material factors have a greater incidence on SWB in middle adulthood than in the other periods. This could be related to the fact that this is the stage in which people assume a commitment to their life project, and take on family, professional and personal responsibilities (Griffa & Moreno, 2015). The main concern of middle adults is to be productive by engaging in activities that have to do, mainly, with professional and family development (Erikson, 1985). It makes sense, then, that MI acquires special relevance in the explanation of SWB in this period of life, since it presents a double function, turning out to be both an indicator of productivity and a means to obtain the necessary resources to carry out the chosen life project (Diener et al., 2018; Rojas, 2019). At the same time, the results revealed that ESD is also important for SWB at this stage of life. Employment -understood as paid work- is a central pillar in middle adulthood, since it functions as a means both for obtaining the necessary income to carry out the life project, and for the development of an activity through which the individual feels useful to society (Erikson, 1985; Griffa & Moreno, 2015; Jebb et al., 2020). Calvo and González (2013) argue that job quality is strongly related to life satisfaction and happiness, which would explain the explanatory power that this dimension acquires on SWB in this period of life. HD and EAD appeared as explanatory factors of SWB in both young and middle adulthood, which would suggest that these dimensions take relevance in the evaluation that people make about their lives during the stages in which life projects are defined and carried out (Arnett, 2007a; Arnett, 2007b; Arslan & Zincirli, 2020; Burke, 2017; Erikson, 1985; García- Álvarez et al., 2022; Griffa & Moreno, 2015). At these stages, then, access to decent housing and high education would acquire value in that they are considered necessary dimensions for personal development and the economic, social and cultural integration of individuals (Bonfiglio, 2023; Panico, 2019; Vinces Sandoval et al., 2022; Wang & Sohail, 2022). Finally, it should be noted that EAP is the only economic-material condition that influences the level of SWB at all stages of adult life. This finding would be consistent with the literature that argues that how individuals value their income and how they spend their income, as well as personal expectations, have a greater weight on SWB than objective economic-material resources (Easterlin, 2015; Kahneman & Deaton, 2010; Kasser & Sheldon, 2009; Mogilner et al., 2018). In summary, both the identification of the factors that influence SWB in each of the stages of adult life and the analysis of the interplay that occurs between them allow us to incorporate a more discerning view when carrying out interventions, public policies, programs, projects and social actions that improve the well-being of the population and, consequently, human development. Thus, it becomes possible to effectively direct efforts towards each of the population groups in order to achieve an improvement in society as a whole. Declarations Ethical Approval The data for Argentina was collected by the Argentine Social Debt Observatory-UCA (Pontifical Catholic University of Argentina). All procedures followed the ethical guidelines recommended for research involving humans by the Declaration of Helsinki and National Law 25326 on the Protection of Personal Data in accordance with Argentine legislation. For more information, visit http://www.uca.edu.ar/index.php/site/index/es/uca/observatorio-de-la-deuda-social-argentina/encuesta-de-la-deuda-social/anexo-metodologico-encuesta-deuda-social/ Funding This work was supported by a doctoral fellowship from the National Scientific and Technical Research Council (CONICET), Argentina. Author Contribution P.F. wrote the manuscript, S.S.R.E. reviewed it. Both authors analyzed data. References Alvarado, H., & Batanero, C. (2006). El significado del teorema central del límite: Evolución histórica a partir de sus campos de problemas. In Á. Contreras de la Fuente, L. 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Journal of Business Ethics , 84 (2), 243-255. https://doi.org/10.1007/s10551-008-9696-1 Kessler, R., & Mroczek, D. (1994). Final version of our non-specific Psychological Distress Scale. Ann Arbor, MI: Survey Research Center of the Institute for Social Research, University of Michigan . Lakey, B., & Tanner, S. M. (2013). Social Influences in Negative Thinking and Affect . Cognitive Therapy and Research, 37 (1), 160-172 . https://doi.org/10.1007/s10608-012- 9444-9 Lansford, J. E. (2018). A lifespan perspective on subjective well-being. In E. Diener, S. Oishi, & L. Tay (Eds.), Handbook of well-being (pp. 344-357). DEF Publishers. https://nobascholar.com/books/1 Liu, B., Floud, S., Pirie, K., Green, J., Peto, R., & Beral, V. (2016). Does happiness itself directly affect mortality? The prospective UK Million Women Study. The Lancet , 387 , 874-881. https://doi.org/10.1016/S0140-6736(15)01087-9 Luhmann, M. (2017). Development of subjective well-being. In J. Specht (Ed.), Personality development across the life span (pp. 197-218). Elsevier. Luhmann, M., Hawkley, L. C., Eid, M., & Cacioppo, J. T. (2012). Time frames and the distinction between affective and cognitive well-being. Journal of Research in Personality, 46 (4), 431-441. https://doi.org/10.1016/j.jrp.2012.04.004 Lykken, D., & Tellegen, A. (1996). Happiness Is a Stochastic Phenomenon. Psychological Science , 7 (3), 186-189. https://doi.org/10.1111/j.1467-9280.1996.tb00355.x Melrose, K. L., Brown, G. D. A., & Wood, A. M. (2015). When is received social support related to perceived support and well-being? When it is needed. Personality and Individual Differences, 77 , 97-105 . https://doi.org/10.1016/j.paid.2014.12.047 Mogilner, C., Whillans, A., & Norton, M. I. (2018). Time, money, and subjective well-being. In E. Diener, S. Oishi, & L. Tay (Eds.), Handbook of Well-being (pp. 491-506). DEF Publishers. https://nobascholar.com/books/1 Mroczek, D. K. (2004). Positive and negative affect at midlife. In O. G. Brim, C. D. Ryff & R. C. Kessler (Eds.), How healthy are we? A national study of well-being at midlife (pp. 205-225). The University of Chicago Press. Nguyen, L., Phillips, C. V., Rodríguez, A., Young, A. R., & Ramdass, J. V. (2022). Relationships matter! Social safeness and self‐disclosure may influence the relationship between perceived social support and well‐being for in‐person and online relationships. Journal of Applied Social Psychology, 52 (12), 1211-1220. https://doi.org/10.1111/jasp.12921 Oishi, S., Diener, E., Suh, E., & Lucas, R. E. (1999). Value as a Moderator in Subjective Well- Being. Journal of Personality, 67 (1), 157-184 . https://doi.org/10.1111/1467- 6494.00051 Oishi, S., & Gilbert, E. A. (2016). Current and future directions in culture and happiness research . Current Opinion in Psychology, 8, 54-58 . https://doi.org/10.1016/j.copsyc.2015.10.005 Organisation for Economic Co-operation and Development [OECD] (2013). OECD Guidelines on Measuring Subjective Well-being . OECD Publishing. https://doi.org/10.1787/9789264191655-en Panico, A. F. (2019). Enfoque multidimensional del bienestar: Análisis de la dimensión vivienda. Atlantic Review of Economics (ARoEc) , 2 (1). https://dialnet.unirioja.es/servlet/articulo?codigo=6990034 Razo González, A. M., Díaz Castillo, R., & López González, M. P. (2018). Percepción del estado de salud y la calidad de vida en personas jóvenes, maduras y mayores. Revista CONAMED, 23 (2), 58-65. https://dialnet.unirioja.es/servlet/articulo?codigo=6508622 Rodríguez Espínola, S.S., & Enrique, H. C. (2007). Validación Argentina del Cuestionario MOS de Apoyo Social Percibido. Psicodebate , 7 , 155-168. https://doi.org/10.18682/pd.v7i0.433 Rojas, M. (2018). Happiness In Latin America Has Social Foundations. In J. F. Helliwell, R. Layard & J. D. Sachs (Eds.), World Happiness Report 2018 (pp. 115-145). UN Sustainable Development Solutions Network. http://worldhappiness.report/ed/2018 Rojas, M. (2019). The Relevance of Richard A. Easterlin’s Groundbreaking Work. A Historical Perspective. In M. Rojas (Ed.), The Economics of Happiness (pp. 3-24). Springer. https://doi.org/10.1007/978-3-030-15835-4_1 Rojas, M., & García Vega, J. J. (2017). Well-Being in Latin America. In R. Estes & M. Sirgy (Eds), The Pursuit of Human Well-Being. International Handbooks of Quality-of-Life (pp. 217-255). Springer. https://doi.org/10.1007/978-3-319-39101-4_8 Ryan A. K., & Willits F. K. (2007). Family ties, physical health, and psychological well- being. J Aging Health, 19 (6), 907-920. https://doi.org/10.1177/0898264307308340 Salvia, H. A., Tuñon, I., Donza, E. R., Vera, J., Bonfiglio, J. I., Rodríguez Espínola, S. S., Poy Piñeiro, S., & Tinoboras, C. P. (2023). Encuesta de la Deuda Social Argentina 2017-2023- Serie Agenda para la Equidad. Repositorio CONICET. https://ri.conicet.gov.ar/handle/11336/244989 San Martín, J. L., & Barra, E. (2013). Autoestima, Apoyo Social y Satisfacción Vital en Adolescentes. Terapia Psicológica, 31 (3), 287-291. https://doi.org/10.4067/S0718- 48082013000300003 Sheldon, K. M. (2013). Individual daimon, universal needs, and subjective well-being: Happiness as the natural consequence of a life well lived. In A.S. Waterman (Ed.), The best within us: Positive psychology perspectives on eudaimonia . American Psychological Association. https://doi.org/10.1037/14092-007 Sherbourne, C. D., & Stewart, A. L. (1991). The MOS social support survey. Social Science & Medicine, 32 (6), 705-714. https://doi.org/10.1016/0277-9536(91)90150-B Spielberger, Ch., Gorsuch, R. L., & Lushene, R. E. (1983). Manual for the State-Trait Anxiety Inventory. STAI (Form Y), Self evaluation questionnaire . Consulting Psychologists Press. Srivastava, S., & Muhammad, T. (2021). In Pursuit of Happiness: Changes in Living Arrangement and Subjective Well-Being among Older Adults in India. Journal of Population Ageing, 16 (1), 459-475 . https://doi.org/10.1007/s12062-021-09327-5 Steptoe, A., Deaton, A., & Stone, A. A. (2015). Subjective wellbeing, health, and ageing. Lancet, 385 (9968), 640-648. https://doi.org/10.1016/S0140-6736(13)61489-0 Stock, J. H., & Watson, M. M. (2012). Introducción a la Econometría . Pearson Educación. Tov, W. (2018). Well-being concepts and components. In E. Diener, S. Oishi, & L. Tay (Eds.), Handbook of Well-being (pp. 30-44). DEF Publishers. https://nobascholar.com/books/1 Veenhoven, R. (2008). Healthy happiness: effects of happiness on physical health and the consequences for preventive health care . Journal of Happiness Studies, 9 (3), 449–469 . https://doi.org/10.1007/s10902-006-9042-1 Vera Noriega, J. Á., Bautista Hernández, G., & Tánori Quintana, J. (2017). Condicionantes de la satisfacción con la vida de los pueblos originarios en México . Acta de Investigación Psicológica, 7 (3), 2783-2792. https://doi.org/10.1016/j.aipprr.2017.11.007 Villatoro, P. (2012). La medición del bienestar a través de indicadores subjetivos: una revisión . Serie estudios Estadísticos y Prospectivos N° 79 (CEPAL). Organización de las Naciones Unidas. https://cepal.org/sites/default/files/publication/files/4783/S1200595_es.pdf Vinces Sandoval, M, J., Rey Siquilanda, C. F, Vinueza Arroyo, G, F., & Peñafiel Palacios, A., J., (2022). Derecho al bienestar: política pública ecuatoriana relativo a la vivienda. Universidad Y Sociedad, 14 (4), 513-521. https://rus.ucf.edu.cu/index.php/rus/article/view/3162/3104 Wang, Z., y Sohail, M. T. (2022). Short- and Long-Run Influence of Education on Subjective Well-Being: The Role of Information and Communication Technology in China. Frontiers in Psychology, 14 (13), 927562. https://doi.org/10.3389/fpsyg.2022.927562 World Health Organization [WHO] (2014). Documentos Básicos . 48a edición. WHO. https://apps.who.int/gb/bd/PDF/bd48/basic-documents-48th-edition- sp.pdf?ua=1#page=7 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7892672","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542797203,"identity":"0efd74d7-1f28-4a45-b3e7-01355b104960","order_by":0,"name":"Pilar 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Complementarily, the Organization for Economic Cooperation and Development (OECD, 2013) defines it as a positive state of mind that integrates both positive and negative evaluations of life, as well as emotional reactions associated with life experiences. These definitions highlight the complex and dynamic nature of SWB, which is not limited to momentary emotional states, but also includes broader cognitive evaluations of life as a whole.\u003c/p\u003e\u003cp\u003eSWB is composed of a combination of affective and cognitive components that reflect the subjective appraisals people make about their lives (Diener, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Diener et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Diener \u0026amp; Lucas, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Glatzer, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sheldon, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this regard, Diener et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Tov (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasize that SWB involves both immediate estimations and more reflective appraisals. This approach highlights the need to analyze its dimensions separately to understand how different factors influence SWB.\u003c/p\u003e\u003cp\u003eDiener (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) proposes two main perspectives to explain variations in SWB: the top-down approach and the bottom-up approach. The former focuses on the subjective processing of circumstances, emphasizing that the impact of the environment on well-being is mediated by individual interpretation. In contrast, the bottom-up approach attributes greater importance to external circumstances, suggesting that specific life conditions directly influence SWB levels. According to this perspective, SWB is linked to factors such as personal, sociopolitical, community, economic, and environmental context (Diener et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Luhmann, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHelliwell et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) argue that material and health conditions play a crucial role in SWB. Although studies have found a consistent correlation between SWB and these life domains, a clear causal direction has not yet been established. This uncertainty is especially relevant for public policy design, as it directly influences recommendations for improving people's quality of life.\u003c/p\u003e\u003cp\u003eAlthough the empirical study of SWB has advanced significantly in recent decades (Diener, 2013; Diener \u0026amp; Tov, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the analysis of inequality in this domain remains limited. Most SWB research has been conducted in developed Western countries and in highly educated populations, leaving important questions about the applicability of these findings to cross-national contexts and diverse socioeconomic strata (Diener et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Oishi and Gilbert (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) highlight that both the concept of SWB and its predictors vary culturally. In this sense, their meaning and relevance may differ across countries and over time, underscoring the need to contextualize their study within the specific social, economic, and political conditions of each region.\u003c/p\u003e\u003cp\u003eIn Latin America, and specifically in Argentina, there is a significant theoretical gap in the study of SWB and its determinants (Ciocchini et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rojas \u0026amp; Garc\u0026iacute;a Vega, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although there is research related to aspects such as quality of life, poverty or inequality, few studies comprehensively address the subjective assessment of well-being. This theoretical gap is exacerbated by the cultural, social and economic diversity of the region, which may influence how people perceive and evaluate their well-being (Rojas, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, inequalities in terms of access to basic services, economic development, and family structures also affect these perceptions. To advance in this field, it is necessary to develop interdisciplinary and contextualized research that integrates sociological, economic and cultural perspectives.\u003c/p\u003e\u003cp\u003eThe analysis of SWB throughout the life cycle has also generated multiple perspectives. On the one hand, some studies argue that SWB is relatively stable due to genetic predispositions and personality characteristics (Lykken \u0026amp; Tellegen, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). On the other hand, others suggest that SWB is influenced by specific experiences and circumstances, implying some plasticity in SWB levels over time (Lansford, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This debate has led to the development of homeostatic adaptation theory, which posits that people tend to return to a baseline level of well-being even after significant positive or negative events. However, this return is not always uniform and may vary in magnitude and pace depending on circumstances (Luhman et al., 2012).\u003c/p\u003e\u003cp\u003eHernang\u0026oacute;mez et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) suggest that oscillations in SWB are associated with the adaptations required at each stage of life. Erikson (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), for his part, describes eight stages of human development, each marked by specific crises that can influence well-being. Guardini (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) reinforces this idea by stressing that well-being is intrinsically related to the ability to find meaning in each stage of the life cycle.\u003c/p\u003e\u003cp\u003eAdulthood, which is divided into young, middle and late stages, is particularly relevant for SWB analysis (Griffa \u0026amp; Moreno, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). During young adulthood, the search for independence and the construction of a life project are priorities (Garcia-Alvarez et al., 2022). In middle adulthood, people usually experience greater emotional balance, although this stage is also marked by reevaluations of goals and challenges related to aging and responsibilities (Mroczek, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Finally, in late adulthood, health and social support become determinants of SWB maintenance (Hansen \u0026amp; Blekesaune, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe relationship between SWB and age does not follow a universal pattern. While some studies suggest a U-shaped curve, with a minimum in middle adulthood (Blanchflower \u0026amp; Oswald, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), others highlight that SWB declines steadily in less developed contexts (Bauer et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Steptoe et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Factors such as social relationships, aspirations and cultural context have a significant impact on these variations (Oishi et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Rojas, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In Latin America, the need for specific and contextualized studies is even more urgent due to the social and cultural particularities of the region. This perspective underscores the importance of considering both structural factors and individual and cultural dynamics to better understand SWB.\u003c/p\u003e\u003cp\u003eThe objective of this study is to explore how health conditions and economic-material circumstances influence SWB throughout the different stages of the life cycle.\u003c/p\u003e"},{"header":"METHOD","content":"\u003ch2\u003eResearch design\u003c/h2\u003e\n\u003cp\u003eA study was conducted with a cross-sectional quantitative approach, with an explanatory scope (Hern\u0026aacute;ndez-Sampieri \u0026amp; Mendoza Torres, 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA sample of adults from households distributed in 960 sampling points in Argentina was used. The study domain consisted of twenty urban agglomerates of 80,000 inhabitants or more, grouped into three large conglomerates according to their size: (a) Greater Buenos Aires: Ciudad Aut\u0026oacute;noma de Buenos Aires and Conurbano Bonaerense (Conurbano Zona Norte, Conurbano Zona Oeste and Conurbano Zona Sur); (b) Other metropolitan areas: Greater Rosario, Greater C\u0026oacute;rdoba, San Miguel de Tucum\u0026aacute;n and Taf\u0026iacute; Viejo, and Greater Mendoza; and (c) Rest of Urban: Mar del Plata, Greater Salta, Greater Paran\u0026aacute;, Greater Resistencia, Greater San Juan, Neuqu\u0026eacute;n, Plottier, Cipolletti, Z\u0026aacute;rate, La Rioja, Goya, San Rafael, Comodoro Rivadavia, Ushuaia and R\u0026iacute;o Grande.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sampling was probabilistic and multistage, with a first stage of clustering and a second stage of stratification. The selection of sample radii within each agglomerate and stratum was random and weighted by the number of households in each radius. The blocks within each sampling point and the households within each block were randomly selected through systematic sampling, while the individuals within each dwelling were selected through a system of sex and age quotas. A first stratification criterion defined the information analysis domains according to regional membership and population size of the agglomerates. A second criterion referred to a socioeconomic criterion of the households. This criterion was established in order to optimize the final distribution of the survey points.\u0026nbsp;The sampling error was +/- 1.3%, with an assessment of a population proportion of 50% and a confidence level of 95%. The persons eligible to participate in the study were adults 18 years of age and older who met the established quotas for sex, age and activity status.\u003c/p\u003e\n\u003cp\u003eThe sample consisted of 5950 adults residing in urban areas of 80,000 inhabitants or more in Argentina. The 29.9% (n=1778) resided in Greater Buenos Aires, 40.3% (n=2398) in Other Metropolitan Areas, and the remaining 29.8% (n=1774) in the Rest of Urban Areas. The participants were 2720 (45.7%) men and 3230 (54.3%) women, whose ages ranged from 18 to 96 years (SD= 43.67; SD= 17.28). When divided into age groups, it was observed that 36.7% (n= 2183) of the participants were between 18 and 34 years old, 42.1%\u0026nbsp;(n= 2506) between 35 and 59 years old, and the remaining 21.2%\u0026nbsp;(n= 1261) were 60 years old or older.\u003c/p\u003e\n\u003ch2\u003eData collection variables and techniques\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe Argentine Social Debt Survey (EDSA; Salvia et al., 2023) is a multipurpose questionnaire that addresses different dimensions and components of human and social development, and is the only one in Argentina that surveys the number of indicators necessary to carry out the proposed analysis. The survey is carried out between July and October of each year, on a probabilistic sample representative of households and individuals residing in twenty urban agglomerates in the country during the reference period. The protocol to be administered was approved by an Institutional Review Board of the Pontificia Universidad Cat\u0026oacute;lica Argentina, and all participating subjects gave their verbal informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn 2021, the EDSA (Salvia et al., 2023) was composed of 344 items. The initial six items were aimed at inquiring information about the neighborhood and the housing quality of the surveyed dwelling. The next forty questions collected data on the composition of each household, gathering information on each of its members. The rest of the questions were distributed in three large modules. The first of these, the \u003cem\u003eHousehold Module\u003c/em\u003e, was composed of 96 questions, divided into five sections: (a) Protection and shelter; (b) Economic situation of the household; (c) Access to food; (d) Eating; and (e) Safety and bodily integrity. In the second module, the \u003cem\u003eIndividual Module\u003c/em\u003e, 140 questions were grouped into seven sections: (a) Health; (b) Democracy and civic life; (c) Beliefs, practices and religious affiliations; (d) Work and income; (e) Relationship with others; (f) Attitudes and skills; and (g) Gender violence, a section aimed only at female respondents. The last module, the \u003cem\u003eChildhood Module\u003c/em\u003e, was only administered to those persons who had said they lived with children under 18 years of age in the household. It consisted of 62 questions that the adult had to answer about each of the children and/or adolescents living in the household.\u003c/p\u003e\n\u003cp\u003eThe data included in this study were extracted from the survey described above. The data collection techniques used to measure each of the variables are detailed below.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAge\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the initial section of the EDSA (Salvia et al., 2023), participants were asked, as an open-ended question, to state their age in terms of years completed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubjective well-being\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SWB is an index that was constructed by calculating the sum of the scores obtained for the quality of life, life satisfaction and happiness items. These items are included in the Attitudes and Skills section of the \u003cem\u003eIndividual Module\u0026nbsp;\u003c/em\u003eof the EDSA (Salvia et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA positive significant correlation was found both between happiness and quality of life (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= .525; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01), and between happiness and satisfaction with life (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= .531; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01). A significant positive correlation was also demonstrated between quality of life and satisfaction with life (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= .738; \u003cem\u003ep\u003c/em\u003e \u0026le; .01). To assess the internal consistency of the SWB construct, consisting of happiness, quality of life and life satisfaction, an exploratory factor analysis was performed using the principal component analysis method (\u003cem\u003eKMO\u0026nbsp;\u003c/em\u003e=.679; \u003cem\u003eBartlett\u0026apos;s test of sphericity X\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e(3)\u0026nbsp;\u003c/sub\u003e = 6985.137; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001). Thus, the null hypothesis was rejected, and it was found that 73.41% of the variance is explained by a single component.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHappiness\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData on happiness were collected by means of a question that asked the participant to answer how happy he/she considers him/herself to be on a four-choice Likert-type scale (4= \u003cem\u003eVery happy\u003c/em\u003e, 3= \u003cem\u003eHappy\u003c/em\u003e, 2= \u003cem\u003eNot very happy\u003c/em\u003e, 1= \u003cem\u003eNot at all happy\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuality of life\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eQuality of life was measured by a question that asked the participant to rate his or her quality of life in the last two weeks. The item contained a five-option Likert-type scale (5= \u003cem\u003ePoor\u003c/em\u003e, 4= \u003cem\u003eFair\u003c/em\u003e, 3= \u003cem\u003eGood\u003c/em\u003e, 2= \u003cem\u003eVery good\u003c/em\u003e, 1= \u003cem\u003eExcellent\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSatisfaction with life\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn a similar way, life satisfaction was assessed by asking the individual to indicate how satisfied he/she has been with his/her life in the last two weeks. This item also had a Likert scale with five options (5= \u003cem\u003eTotally dissatisfied\u003c/em\u003e, 4= \u003cem\u003eFairly dissatisfied\u003c/em\u003e, 3= \u003cem\u003eSatisfied\u003c/em\u003e, 2= \u003cem\u003eFairly satisfied\u003c/em\u003e, 1= \u003cem\u003eTotally satisfied\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHealth factors\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;In line with the definition of health proposed by the World Health Organization as to \u0026quot;a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity \u0026quot; (WHO, 2014, p. 1), the present study took into account the three dimensions of health: physical, mental and social.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePhysical health: Perceived state of health\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the Health section of the EDSA (Salvia et al., 2023) \u003cem\u003eIndividual Module\u0026nbsp;\u003c/em\u003ethere is an item that refers to the \u003cem\u003eperceived health status\u0026nbsp;\u003c/em\u003e(PHS) of the participants. In it the subject was asked what his or her general health status is, and four response options were given (1= \u003cem\u003eHas no health problems\u003c/em\u003e, 2= \u003cem\u003eHas a few health problems\u003c/em\u003e, 3= \u003cem\u003eHas quite a few health problems\u003c/em\u003e, 4= \u003cem\u003eHas chronic or serious health problems\u003c/em\u003e), from which the subject had to select one\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMental health\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Kessler Psychological Distress Scale (KPDS-10; Kessler \u0026amp; Mrozeck, 1994), an instrument designed to measure the prevalence of nonspecific psychological distress, was used to assess \u003cem\u003emental health\u0026nbsp;\u003c/em\u003e(MH). It consists of ten items that refer to how the person felt in the last month with respect to different symptoms related to depression (e.g., \u003cem\u003edid you feel tired for no reason in the last month?)\u0026nbsp;\u003c/em\u003eand anxiety (e.g., \u003cem\u003edid you feel nervous in the last month?\u003c/em\u003e), which are evaluated according to a five-point Likert-type scale, ranging from 1 (\u003cem\u003ealways\u003c/em\u003e) to 5 (\u003cem\u003enever\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the Attitudes and Skills section of the \u003cem\u003eIndividual Module\u0026nbsp;\u003c/em\u003eof the EDSA, the Argentinean adaptation conducted by Brenlla and Aranguren (2010) was administered. To demonstrate that the KPDS-10 is an appropriate instrument to efficiently measure the psychological distress presented by the country\u0026apos;s inhabitants, in that study an initial exploratory factor analysis was carried out on a sample corresponding to the general population using the method of principal axes \u003cem\u003e(KMO=\u003c/em\u003e.90; \u003cem\u003eBartlett\u0026apos;s test of sphericity X\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csub\u003e(45)\u003c/sub\u003e = 11851.21; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001). The presence of a single factor accounted for 45.5% of the total variance. Next, they performed an exploratory factor analysis on a sample of patients using the same method (\u003cem\u003eKMO=\u003c/em\u003e.851; \u003cem\u003eBartlett\u0026apos;s Test of Sphericity X\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csub\u003e(45)\u003c/sub\u003e = 396.325; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001). This time, they found two factors that explained 64.3% of the total variance: the first factor, which comprised 54.2% of the variance, grouped nine items; the second factor, which collected only one item, explained 10.1% of the variance. In addition, they obtained very satisfactory measures of reliability (\u0026alpha; = .88). On the other hand, they conducted convergent validity analyses, in which significant positive correlations were found with the Spielberger Anxiety Scale (r = .849, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .01; \u003cem\u003er\u0026nbsp;\u003c/em\u003e= .736, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001) (Spielberger et al., 1983), the Beck Depression Inventory (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= .898, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .01; \u003cem\u003er\u0026nbsp;\u003c/em\u003e= .624, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001) (Beck et al., 2006) and the SCL-90-R (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= .764; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .01) (adapted from Casullo, 2004).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn turn, Brenlla and Aranguren (2010) evaluated the stability of the scores by means of a \u003cem\u003etest-retest\u0026nbsp;\u003c/em\u003ein two subsamples. The results found, in both cases, were satisfactory (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= .365, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001; \u003cem\u003er\u0026nbsp;\u003c/em\u003e= .445, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001). At the same time, it was found that men presented lower indices of psychological distress than women, and no significant differences were found when analyzing distress according to age.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, they tested the criterion validity by contrasting a patient group with a general population group, and observed that the mean scores of the participants belonging to the second group were significantly lower than those of the patient group (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(197)\u003c/sub\u003e = -9.316, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001). They performed a study of ROC curves to distinguish between patients and general population subjects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith respect to the administration of the KPDS-10 in the present study sample, very satisfactory measures of reliability were obtained (\u0026alpha; = .91).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSocial health: Perceived social support\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe MOS Perceived Social Support Questionnaire (Sherbourne \u0026amp; Stewart, 1991) is a 20-item scale that assesses individuals\u0026apos; perception of their social support. The first item refers to structural social support, inquiring about the size of the social network by means of an open-ended question, and the remaining 19 items inquire about functional social support by means of questions with Likert-type response options. The adaptation of the MOS Questionnaire for the Argentine population was carried out by Rodr\u0026iacute;guez Esp\u0026iacute;nola and Enrique (2007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn abbreviated version by Garofalo et al. (2023) was administered in the Attitudes and Skills section of the \u003cem\u003eIndividual Module\u0026nbsp;\u003c/em\u003eof the EDSA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStructural social support\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe first item of the MOS Questionnaire (Sherbourne \u0026amp; Stewart, 1991) refers to the number of friends and family members that the person perceives to have with whom he/she feels at ease and can talk about everything; this item was kept unchanged, both in the Argentine adaptation and in the abbreviated version. Thus, the respondent was asked, as an open-ended question, to indicate an approximate number of friends and relatives. The total score of \u003cem\u003estructural social support\u0026nbsp;\u003c/em\u003e(SSS) was obtained by adding the number of friends and relatives the subject said he/she had.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunctional social support\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe MOS Questionnaire (Sherbourne \u0026amp; Stewart, 1991) analyzes \u003cem\u003efunctional social support\u0026nbsp;\u003c/em\u003e(FSS) by means of 19 items with five-point Likert-type response options. Each item refers to one of the four dimensions of functional social support: (a) emotional, (b) instrumental, (c) informational, and (d) affective.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn their adaptation, Rodr\u0026iacute;guez Esp\u0026iacute;nola and Enrique (2007) carried out a confirmatory factor analysis for the factor structure proposed by the original questionnaire in a general sample of 375 cases, which revealed the existence of three factors that explained 59.86% of the variance. To study the reliability and validity of the instrument, the authors of the adaptation conducted an exploratory factor analysis using the principal components method with Varimax rotation (\u003cem\u003eKMO=\u0026nbsp;\u003c/em\u003e.935). The items presented a three-factor distribution: the first factor, corresponding to informational functional support, explained 45.48% of the total variance; the second, corresponding to affective functional support, 7.44%, and the third, corresponding to instrumental social support, 6.94%. To measure the internal consistency of the questionnaire, Rodr\u0026iacute;guez Esp\u0026iacute;nola and Enrique (2007) used Cronbach\u0026apos;s alpha coefficient. The instrument presented high reliability indices, both in the general measurement of functional social support (\u0026alpha; = .919) and in each of its dimensions (\u0026alpha; of informational social support= .876; \u0026alpha; of affective social support= .843; \u0026alpha; of instrumental social support= .802).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the abbreviated version (Garofalo et al., 2023), functional social support is measured in the MOS Questionnaire by a selection of nine of the 19 items (see Table 1), with four-choice Likert-type responses (4= \u003cem\u003eNever\u003c/em\u003e, 3= \u003cem\u003eA few times\u003c/em\u003e, 2= \u003cem\u003eMany times\u003c/em\u003e, 1= \u003cem\u003eAll the time\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eItems of functional social support included in the abbreviated version of the MOS Questionnaire\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eFunctional social support dimension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003eItem\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 143px;\"\u003e\n \u003cp\u003eInstruments\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003e1. How often do you have someone to prepare food for you if you are unable to do so?\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003e2. How often do you have someone to help you with your household chores if you are sick?\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003e3. How often do you have someone to help you with remedies if you get sick?\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 143px;\"\u003e\n \u003cp\u003eAffective\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003e4. How often do you have someone to love and make you feel loved?\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003e5. How often do you have someone to show you love and affection?\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003e6. How often do you have someone to hold you?\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 143px;\"\u003e\n \u003cp\u003eInformational\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003e7. How often do you have someone you can count on when you need to talk?\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003e8. How often do you have someone to inform you and help you understand a situation?\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 458px;\"\u003e\n \u003cp\u003e9. How often do you have someone to advise you on how to solve your personal problems?\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource\u003c/em\u003e: Own elaboration based on the study conducted by Garofalo et al. (2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo validate the abbreviated questionnaire, Garofalo et al. (2023) conducted an exploratory factor analysis on a sample of 208 cases (\u003cem\u003eKMO=\u003c/em\u003e.80; \u003cem\u003eBartlett\u0026apos;s test of sphericity X\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003csub\u003e(36)\u003c/sub\u003e = 1382.10; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001), for which the extraction of the principal components was carried out with the Varimax extraction method. Three factors were found that explained 81.5% of the total variance; the first of them comprised 62.4%, the second 11.1%, and the third 8.05%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThey then performed a confirmatory factor analysis on a sample of 222 participants. For this, they developed a structural equation model that achieved a satisfactory fit to the empirical data (\u003cem\u003eCFI=\u003c/em\u003e.966; \u003cem\u003eNFI=\u0026nbsp;\u003c/em\u003e.952; \u003cem\u003eRMR=\u0026nbsp;\u003c/em\u003e.037). In addition, they obtained very satisfactory measures of reliability, both in the assessment of total functional social support (\u0026alpha; = .93) and in the affective (\u0026alpha; = .90), instrumental (\u0026alpha; = .85) and informational (\u0026alpha; = .88) dimensions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVery satisfactory measures of reliability in the assessment of total functional social support (\u0026alpha; = .95) were achieved in the administration of the abbreviated version of the Argentine adaptation of the MOS Questionnaire in the sample of participants in the present study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic and material factors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiarrizzo (2010) postulates that economic-material well-being can be measured by means of different indicators, which are divided into two large groups: (a) \u003cem\u003eobjective indicators\u003c/em\u003e, and (b) \u003cem\u003esubjective indicators\u003c/em\u003e. Objective indicators, in turn, can be \u003cem\u003emonetary\u003c/em\u003e, consisting of traditional economic measurements that include measures of income, or \u003cem\u003enon-monetary\u003c/em\u003e, which include other factors that make up economic well-being. On the other hand, subjective indicators focus on the perception that each individual has of his or her economic-material well-being.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study addresses monetary income as an objective monetary indicator, social and economic rights dimensions as objective non-monetary indicators, and perceived income sufficiency as a subjective indicator.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMonetary income\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo calculate \u003cem\u003emonetary income\u0026nbsp;\u003c/em\u003e(MI) we took the \u003cem\u003eper capita\u0026nbsp;\u003c/em\u003ehousehold income, surveyed by asking two questions: (a) \u0026quot;How many people live in your household (including children, babies and domestic help with bed)?\u0026quot;; and (b) \u0026quot;In total, adding up what all the people living in the household contribute plus what you may receive from other people or from unemployment insurance or social plans, how much was the household\u0026apos;s total income last month?\u0026quot; These questions are found in the Household Protection and Shelter and Household Economic Situation sections of the EDSA (Salvia et al., 2023) \u003cem\u003eHousehold Module.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDimensions of social and economic rights\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe items referring to the measurement of the dimensions of social and economic rights can be found in the first section, the \u003cem\u003eHousehold Module\u003c/em\u003e, the Work and Income section of the \u003cem\u003eIndividual Module,\u0026nbsp;\u003c/em\u003eand the \u003cem\u003eChildhood Module\u0026nbsp;\u003c/em\u003eof the EDSA (Salvia et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst, the \u003cem\u003ebasic services dimension\u0026nbsp;\u003c/em\u003e(BSD) was observed through three indicators: (a) connection to a running water network, (b) connection to a sewage network, and (c) access to the energy network. These were measured by means of three questions with dichotomous response options (1= \u003cem\u003eYes\u003c/em\u003e, 2= \u003cem\u003eNo\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003ehousing dimension\u0026nbsp;\u003c/em\u003e(HD) was evaluated by means of three indicators: (a) overcrowding, (b) precarious housing, and (c) sanitation. The first was recorded when a household had three or more people per room, dividing the number of household members by the number of rooms in the dwelling that could be used as bedrooms. Precarious housing was investigated by asking or observing, depending on the survey modality, the type of housing and the materials with which it was constructed. The items were multiple choice. To evaluate the sanitary service, multiple choice questions were used to determine the presence or absence of at least one toilet in the dwelling and the type of flush it had.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, \u003cem\u003ethe environment dimension\u0026nbsp;\u003c/em\u003e(ED) was observed through the presence or absence, in the vicinity of the household, of garbage dumps, polluting factories and water bodies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eeducational access dimension\u0026nbsp;\u003c/em\u003e(EAD) was surveyed by means of three indicators, constructed on the basis of questions with a closed response option, and were categorized taking into account current education regulations: (a) non-attendance was recorded in the case of minors who did not attend school or those adults who had not attended high school; (b) middle school educational lag was considered in the cases of adults born between the years 1983 and 1989 who had not completed up to the second year of high school, in the case of those between 19 and 24 years old and attending formal educational institutions, educational lag deficit was not considered; and (c) educational backwardness in elementary school was recorded in the case of persons born before 1983 who had not completed elementary school.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, the \u003cem\u003eemployment and social security dimension\u0026nbsp;\u003c/em\u003e(ESD) was observed through questions with closed response options that accounted for the income registered in the social security system from dependent or self-employed jobs or from pensions and/or contributory pensions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe EDSA also studies the \u003cem\u003efood and health dimension\u0026nbsp;\u003c/em\u003e(FHD), which was not included in this work due to its overlap with other study variables, being the only dimension studied by the ODSA to be excluded from the data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePerceived income sufficiency\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the Household Economic Situation section of the EDSA (Salvia et al., 2023) \u003cem\u003eHousehold Module\u003c/em\u003e, \u003cem\u003eperceived income sufficiency\u0026nbsp;\u003c/em\u003e(PIS) was measured by a question that asks the participant to indicate whether the money they collect per month in their household is enough to live on. The item contains three response options: 1= \u003cem\u003eThe money is enough and they can save some\u003c/em\u003e, 2= \u003cem\u003eIt is enough, but they cannot save\u003c/em\u003e, 3= \u003cem\u003eIt is not enough\u003c/em\u003e. From this item, a new dichotomous variable was calculated in which the categories were: 0= \u003cem\u003eInsufficient income\u003c/em\u003e, 1= \u003cem\u003eSufficient income\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis Procedure \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, a data matrix was constructed with the data collected in the\u0026nbsp;IBM SPSS\u003csup\u003e\u0026reg;\u0026nbsp;\u003c/sup\u003e 25.0 program. The data were preprocessed, discarding those cases that did not meet the inclusion criteria and those that were presented as \u003cem\u003emissing cases\u003c/em\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to proceed with the data analysis, the \u003cem\u003eper capita\u0026nbsp;\u003c/em\u003efamily income variable was calculated and the variables of health status, mental health, functional social support, perceived sufficiency of income, basic services dimension, housing dimension, environment dimension, educational access dimension, and employment and social security dimension were fine-tuned. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo calculate \u003cem\u003eper capita\u0026nbsp;\u003c/em\u003efamily income, we calculated the ratio between total household income and the number of members living in the household. Since income is a variable that is not usually normally distributed normally, econometrics recommends that it be replaced by its logarithm in order to better comply with the assumptions of the regression model and achieve a better fit (Stock \u0026amp; Watson, 2012). Thus, \u003cem\u003eper capita\u0026nbsp;\u003c/em\u003eincome was evaluated by calculating the logarithm of the mean\u0026nbsp;of\u0026nbsp;the total real household income divided by the number of members, expressed in constant pesos of the third quarter of 2021.\u003c/p\u003e\n\u003cp\u003eThen, the order of the response categories of the scales corresponding to the variables of health status, mental health, functional social support, perceived adequacy of income, basic services dimension, housing dimension, environment dimension, educational access dimension, and employment and social security dimension was reversed so that all variables had the same meaning: the higher the score, the better the perception of health and economic-material conditions. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, the quality of life and life satisfaction variables were recoded so that they had the same number of response options as the happiness variable and the order of the response options was reversed. For quality of life the categories were as follows: 1= \u003cem\u003eBad\u003c/em\u003e, 2= \u003cem\u003eFair\u003c/em\u003e, 3= \u003cem\u003eGood\u003c/em\u003e, 4= \u003cem\u003eVery good/Excellent\u003c/em\u003e. On the other hand, for satisfaction with life, the categories were as follows: 1= \u003cem\u003eTotally dissatisfied\u003c/em\u003e, 2= \u003cem\u003eFairly dissatisfied\u003c/em\u003e, 3= \u003cem\u003eSatisfied\u003c/em\u003e, 4= \u003cem\u003eFairly/Totally satisfied\u003c/em\u003e. Based on the central limit theorem (Alvarado \u0026amp; Batanero, 2006), a normal distribution of these variables was assumed for the sample due to the considerable sample size (n= 5950).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo compare and relate the variables of happiness, quality of life and life satisfaction, a Pearson\u0026apos;s r correlation analysis was performed. A confirmatory factor analysis was performed to evaluate the internal consistency of the SWB construct, and Cronbach\u0026apos;s alpha was calculated to measure its reliability. Finally, the scores of the happiness, quality of life and life satisfaction variables were summed to obtain the SWB index, and the z-score corresponding to the total score of the index was calculated, translating it to a scale from 0 to 100.\u003c/p\u003e\n\u003cp\u003eFinally, the sample was segmented into three groups according to the age of the participants, and a stepwise linear regression model was carried out to determine the effects of inequalities in health and economic-material conditions on SWB in each age group.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn order to study the effects that inequalities in health, social and economic-material conditions generate in the SWB at each stage of adult life, the sample was segmented into three groups according to the life cycle stages of the participants, following the aforementioned criteria: (a) \u003cem\u003eYoung adulthood\u003c/em\u003e, composed of 2182 individuals aged 18 to 34 years; (b) \u003cem\u003eMiddle adulthood\u003c/em\u003e, composed of 2504 participants aged 35 to 59 years; and (c) \u003cem\u003eLate adulthood\u003c/em\u003e, comprising 1261 people aged 60 years or older.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThen, a stepwise linear regression analysis was performed for each group with SWB as the dependent variable, and with MH, PSH, SSS, FSS, MI, BSD, HD, ED, EAD, ESD, ESD, and PIS as predictor variables. The results obtained are detailed in the following subsections.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors Affecting Subjective Well-Being In Young Adulthood\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, the correlation between the variables of interest was analyzed using Pearson\u0026apos;s r coefficient (see Table 2). SWB presented significant correlations with all variables, but the strongest were with MH \u003cem\u003e(r=\u0026nbsp;\u003c/em\u003e.401; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01), FSS \u003cem\u003e(r=\u0026nbsp;\u003c/em\u003e.279; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01), and PIS (\u003cem\u003er=\u0026nbsp;\u003c/em\u003e.278; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01). The vast majority of correlations were significant, with the exception of that of the MH with the BSD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.086); the PSH with the SSS (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.012), the FSS (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.015), MI (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.023), BSD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.401), EAD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.075), ESD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.029), and PIS (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.003); and that of FSS with MI (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.013) and ED (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.043).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eCorrelation analysis between the variables of interest with Pearson\u0026apos;s r coefficient.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eYoung adulthood.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.401**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.152**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.224**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.279**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.208**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.108**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.189**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.169**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.212**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.178**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.278**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.401**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.256**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.233**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.191**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.141**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.112**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.191**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.135**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.114**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.212**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.152**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.256**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.052**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.051**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.059**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.224**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.233**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.178**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.129**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.064**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.095**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.101**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.053**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.124**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.133**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.279**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.196**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.178**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.068**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.080**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.092**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.108**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.099**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.208**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.141**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.129**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.271**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.323**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.230**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.366**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.372**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.449**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.108**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.064**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.068**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.271**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.370**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.320**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.272**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.244**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.192**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.189**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.112**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.052**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.095**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.080**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.323**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.370**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.315**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.340**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.275**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.242**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.169**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.191**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.051**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.101**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.230**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.320**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.315**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.236**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.166**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.186**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.212**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.135**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.053**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.092**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.366**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.272**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.340**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.236**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.307**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.324**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.178**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.114**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.124**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.108**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.372**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.244**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.275**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.166**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.307**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.324**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.278**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.212**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.133**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.099**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.449**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.192**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.242**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.186**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.324**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.324**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u0026nbsp;\u003c/strong\u003e** Correlation is significant at the .01 level (bilateral).\u003c/p\u003e\n\u003cp\u003eFor the 18-34 age group, seven models were generated from the regression (see Table 3). The final model obtained an acceptable level of determination (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,2174)\u003c/sub\u003e = 109.897; \u003cem\u003eR=\u0026nbsp;\u003c/em\u003e.511; \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = .261; \u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u0026nbsp;=\u0026nbsp;\u003c/em\u003e.259; \u003cem\u003eEER=\u0026nbsp;\u003c/em\u003e14.783), and its coefficient of variance explained was 26.1%. The main predictor variable for SWB turned out to be MH (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.282; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e13.956; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), followed by FSS (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.178; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e9.348; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), PIS (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.142; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e7.074; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), HD (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.069; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e3.494; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), SSS (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.094; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e4.910; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), EAD (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.081; \u003cem\u003et\u0026nbsp;\u003c/em\u003e= 3.969; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), and PSH (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.052; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e2.757; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.05). The model excluded MI (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e1.917; \u003cem\u003ep =\u003c/em\u003e.055), BSD (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e0.393; \u003cem\u003ep =\u003c/em\u003e.694), ED (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e1.680; \u003cem\u003ep=\u003c/em\u003e.093), and ESD (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e1.368; \u003cem\u003ep=\u003c/em\u003e.171), as their predictive values on SWB were not shown to be significant. The equation summarizing the model would be the following: SWB = 12.353 + 0.740*MH + 0.543*FSS + 5.068*PIS + 2.897*HD + 0.257*SSS + 2.992*EAD + 1.762*PSH.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eStepwise linear regression model on SWB.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eYoung adulthood.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eMH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.282***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e13.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 88px;\"\u003e\n \u003cp\u003e109.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" style=\"width: 88px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eFSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.178***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e9.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003ePIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e5.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.142***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e7.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.069***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e3.494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eSSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.094***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e4.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eEAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.081***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e3.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003ePSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e.053*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e2.757\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u0026nbsp;\u003c/strong\u003e* \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.05; ** \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01; *** \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001; \u0026beta; = standardized beta.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors Affecting Subjective Well-Being In Middle Adulthood\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen analyzing the correlation between the variables of interest using Pearson\u0026apos;s r coefficient (see Table 4), it was observed that SWB presented significant correlations with all variables, but the strongest were with MH \u003cem\u003e(r=\u0026nbsp;\u003c/em\u003e.479; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01), PIS (\u003cem\u003er=\u0026nbsp;\u003c/em\u003e.373; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01), and FSS (\u003cem\u003er=\u0026nbsp;\u003c/em\u003e.338; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01). All correlations were significant, except that of the PSH with the BSD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.026), and that of the FSS with the EAD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cem\u003eCorrelation analysis between the variables of interest with Pearson\u0026apos;s r coefficient.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMiddle adulthood.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.479**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.277**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.280**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.338**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.271**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.125**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.203**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.210**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.197**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.255**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.373**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.479**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.312**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.218**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.234**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.221**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.098**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.096**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.242**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.146**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.163**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.293**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.277**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.312**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.109**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.105**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.104**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.053**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.083**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.066**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.134**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.193**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.280**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.218**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.109**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.222**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.166**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.128**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.109**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.137**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.088**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.143**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.191**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.338**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.234**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.105**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.222**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.061**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.060**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.088**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.053**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.177**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.177**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.271**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.221**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.104**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.166**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.061**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.256**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.289**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.264**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.318**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.383**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.456**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.125**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.098**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.128**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.060**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.256**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.333**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.263**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.245**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.264**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.208**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.203**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.096**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.053**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.109**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.088**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.289**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.333**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.258**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.288**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.261**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.245**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.210**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.242**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.083**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.137**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.053**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.264**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.263**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.258**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.179**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.177**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.218**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.197**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.146**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.066**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.088**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.318**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.245**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.288**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.179**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.268**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.267**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.255**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.163**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.134**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.143**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.177**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.383**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.264**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.261**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e177**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.268**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.383**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.373**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.293**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.193**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.191**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.177**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.456**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.208**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.245**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.218**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.267**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e.383**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u0026nbsp;\u003c/strong\u003e** Correlation is significant at the .01 level (bilateral).\u003c/p\u003e\n\u003cp\u003eFrom the regression, nine models were created for the 35-59 age group (see Table 5). The final model presented a satisfactory level of determination (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,2494)\u003c/sub\u003e = 161.799; \u003cem\u003eR=\u0026nbsp;\u003c/em\u003e.607; \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = .369; \u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u0026nbsp;=\u0026nbsp;\u003c/em\u003e.366; \u003cem\u003eEER=\u0026nbsp;\u003c/em\u003e14.743), and an explained variance coefficient of 36.9%. This model yielded MH (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.307; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e17.188; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001) as the main predictor variable for SWB, followed by PIS (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.144; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e7.488; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), FSS (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.188; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e11.149; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), SSS (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.108; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e6.411; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), the PSH (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.105; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e6.411; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), the HD (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.067; \u003cem\u003et\u0026nbsp;\u003c/em\u003e= 3.885; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), the MI (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.049; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e2.543; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.05), the HD (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.044; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e2.541; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.05), and the BSD (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.039; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e2.117; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.05). This model excluded BSD (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e-1.412; \u003cem\u003ep=\u003c/em\u003e.158) and ED (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e1.744; \u003cem\u003ep=\u003c/em\u003e.081), since their predictive values on SWB were not significant. The model equation would be: SWB= -9.997 + 0.850*MH + 5.469*PIS + 0.589*FSS + 0.298*SSS + 2.534*PSH + 3.327*HD + 2.472*MI + 2.014*EAD + 1.540*ESD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003e\u003cem\u003eStepwise linear regression model on SWB.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMiddle adulthood.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eMH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.307***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e17.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\" style=\"width: 87px;\"\u003e\n \u003cp\u003e.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\" style=\"width: 87px;\"\u003e\n \u003cp\u003e161.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\" style=\"width: 87px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003ePIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.144***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eFSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.188***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e11.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eSSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.108***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003ePSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.105***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e6.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e3.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.067***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e3.885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.049*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eEAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eESD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e.039*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u0026nbsp;\u003c/strong\u003e* \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.05; ** \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.005; *** \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001; \u0026beta; = standardized beta.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors Affecting Subjective Well-Being In Late Adulthood\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, the correlation between the variables of interest was studied using Pearson\u0026apos;s r coefficient (see Table 6). It was observed that SWB presented significant correlations with all variables, except, with DE (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.084). The strongest relationships shown by SWB were with MH \u003cem\u003e(r=\u0026nbsp;\u003c/em\u003e.468; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01), FSS \u003cem\u003e(r=\u0026nbsp;\u003c/em\u003e.373; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01), and PSH (\u003cem\u003er=\u0026nbsp;\u003c/em\u003e.351; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.01). Most of the remaining correlations were significant, with the exception of that of PSH with HD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.040) and ESD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.043); SSS with HD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.056) and ESD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.133); FSS with MI (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.067), ED (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.368), HD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.059), and ESD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.012); and that of ED with ESD (\u003cem\u003ep=\u0026nbsp;\u003c/em\u003e.070).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003e\u003cem\u003eCorrelation analysis between the variables of interest with Pearson\u0026apos;s r coefficient.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLate adulthood.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSWB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.468**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.351**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.194**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.373**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.105**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.103**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.106**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.112**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.093**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.258**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.468**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.318**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.157**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.230**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.113**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.094**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.079**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.080**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.104**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.097**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.251**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.351**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.318**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.134**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.136**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.095**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.081**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.116**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.210**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.194**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.157**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.134**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.214**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.068**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.125**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.088**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.097**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.118**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.373**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.230**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.136**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.214**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.074**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.076**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.161**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.105**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.113**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.095**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.068**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.193**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.193**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.154**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.259**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.268**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.374**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.103**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.094**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.081**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.125**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.074**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.214**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.295**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.201**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.214**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.153**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.155**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.106**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.079**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.088**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.076**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.193**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.295**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.146**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.174**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.144**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.148**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.080**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.116**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.097**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.154**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.201**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.146**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.084**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.073**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.112**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.104**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.138**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.259**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.214**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.174**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.084**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.115**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.149**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.093**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.097**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.268**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.153**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.144**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.115**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.221**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.258**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.251**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.210**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.118**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.161**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.374**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.155**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.148**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.073**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.149**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e.221**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003e** Correlation is significant at the .01 level (bilateral).\u003c/p\u003e\n\u003cp\u003eFinally, from the regression, five models were created for the 60 and older age group (see Table 7). The final model showed an acceptable level of determination (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1,1255)\u003c/sub\u003e = 130.361; \u003cem\u003eR=\u0026nbsp;\u003c/em\u003e.585; \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = .342; \u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e\u0026nbsp;=\u0026nbsp;\u003c/em\u003e.339; \u003cem\u003eEER=\u0026nbsp;\u003c/em\u003e15.688), and an explained variance coefficient of 33.9%. As with the models developed for the other groups, the final model yielded MH as the main predictor variable for SWB (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.319; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e12.711; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), but this time it was followed by FSS (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.247; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e10.262; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), PSH (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.189; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e7.698; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), PIS (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.092; \u003cem\u003et\u0026nbsp;\u003c/em\u003e= 3.825; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001), and SSS (\u003cem\u003e\u0026beta;=\u0026nbsp;\u003c/em\u003e.55; \u003cem\u003et=\u0026nbsp;\u003c/em\u003e2.307; \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.05). The model excluded I (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e1.109; \u003cem\u003ep=\u003c/em\u003e.268), BSD (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e0.802; \u003cem\u003ep=\u003c/em\u003e.423), HD (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e1.506; \u003cem\u003ep=\u003c/em\u003e.132), ED (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e-1.009; \u003cem\u003ep=\u003c/em\u003e.313), EAD (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e1.129; \u003cem\u003ep=\u003c/em\u003e.259), and SSS (\u003cem\u003et=\u0026nbsp;\u003c/em\u003e0.700; \u003cem\u003ep=\u003c/em\u003e.484), because they did not show significant predictive values on SWB. The model equation would be conformed as follows: SWB= -6.713 + 0.952*MH + 0.768*FSS + 4.446*PSH + 3.681*PIS + 0.149*SSS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u0026nbsp;\u003c/strong\u003e\u003cem\u003eLinear stepwise regression model on BS.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLate adulthood.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eMH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.319***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 85px;\"\u003e\n \u003cp\u003e130.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 85px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eFSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.247***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.189***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.092***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eSSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e.055*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote.\u0026nbsp;\u003c/strong\u003e* \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.05; ** \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.005; *** \u003cem\u003ep \u0026le;\u0026nbsp;\u003c/em\u003e.001; \u0026beta; = standardized beta.\u003c/p\u003e"},{"header":"DISCUSSION AND CONCLUSIONS","content":"\u003cp\u003eTaking into account both what Lansford (2018) postulated regarding SWB and its associated factors being linked to evolutionary development, and repairing on the results of the present study that found that the level of SWB oscillates significantly according to age, the sixth specific objective explored the effects generated by health and economic-material conditions on SWB according to age groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe analysis of the data from the present study indicated that the factors affecting SWB are not the same, nor do they have the same weight, in young adulthood as in middle or late adulthood. These findings would agree with what is postulated by Boarini et al. (2012) and Lansford (2018), who argue that people\u0026apos;s perceptions of what is important to their well-being change significantly across the lifespan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt could be noted that MH is the condition that most contributes to SWB in all stages of the adult life cycle. As explained in a previous subsection, this would confirm the important influence that anxious and depressive symptomatology exerts on people\u0026apos;s evaluation of their lives. In turn, it was found that the HSP and social support also influence the level of SWB throughout adulthood. These results would seem to be in agreement with the importance that both WHO (2014) and other scholars attach to physical well-being (Boehm, 2018; Diener et al., 2017; Razo Gonz\u0026aacute;lez et al., 2018; Veenhoven, 2008), mental (Greenspoon \u0026amp; Saklofske 2001) and social (Chu et al., 2010; Lakey \u0026amp; Tanner, 2013; Liu et al., 2016; Melrose et al, 2015; Nguyen et al. 2022; Rojas, 2018; San Mart\u0026iacute;n \u0026amp; Barra, 2013; Vera Noriega et al., 2016) as necessary conditions to achieve a fulfilling life.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, although the results showed that PSH has an effect on SWB throughout the adult life cycle, its significance was lower in young adulthood than in other age groups, which would reflect that the individual\u0026apos;s perception of the importance of physical health for his or her well-being changes over time, becoming more relevant the older the person is (Cuadra-Peralta et al., 2016). This could be explained taking into account that aging is a natural process that spans a lifetime and that, over the years, the body suffers a gradual wear and tear, causing diseases or limitations in organic functionality (De Juanas Oliva et al., 2013). That is why younger people, whose body is fully functional and does not yet show signs of deterioration, tend to have fewer concerns about their physical health (Griffa \u0026amp; Moreno, 2015); then, the PSH, despite denoting some incidence on SWB, would not prove to be such a predominant factor as it is in later stages. With the passage of time, individuals become aware of the diseases and physical limitations that occur with age, which would explain that the HSP acquires greater explanatory power on SWB over other conditions the older the age (Erikson, 1985; Griffa \u0026amp; Moreno, 2015; Guardini, 1997; Razo Gonz\u0026aacute;lez et al., 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn turn, we tested the postulate that, in explaining SWB in adulthood, the quality of ties is more important than the size of the support network (Ryan \u0026amp; Willits, 2007; Srivastava \u0026amp; Muhammad, 2021). This occurs at all stages, but is notable in late adulthood, where the weight of SSS on SWB is less significant than in the other age groups. This could be explained by two factors. First, Griffa and Moreno (2015) explain that at this stage the social support network is reduced due to a notable loss of significant ties; people, as they age, must face both the absence or death of family and friends, as well as the limitation in their own social capabilities, caused by the physical and mental deterioration characteristic of natural aging. Secondly, late adults tend to become more selective when establishing ties due to their life experience, which would generate a reduction in the size of their social support network (Hern\u0026aacute;ngomez et al., 2014). Thus, the relevance that people give to the number of relationships that make up their network is diminished, but the satisfaction of the affective, instrumental and informational functions continues to be as important as in previous stages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn turn, the results showed that objective economic-material factors have a greater incidence on SWB in middle adulthood than in the other periods. This could be related to the fact that this is the stage in which people assume a commitment to their life project, and take on family, professional and personal responsibilities (Griffa \u0026amp; Moreno, 2015). The main concern of middle adults is to be productive by engaging in activities that have to do, mainly, with professional and family development (Erikson, 1985). It makes sense, then, that MI acquires special relevance in the explanation of SWB in this period of life, since it presents a double function, turning out to be both an indicator of productivity and a means to obtain the necessary resources to carry out the chosen life project (Diener et al., 2018; Rojas, 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the same time, the results revealed that ESD is also important for SWB at this stage of life. Employment -understood as paid work- is a central pillar in middle adulthood, since it functions as a means both for obtaining the necessary income to carry out the life project, and for the development of an activity through which the individual feels useful to society (Erikson, 1985; Griffa \u0026amp; Moreno, 2015; Jebb et al., 2020). Calvo and Gonz\u0026aacute;lez (2013) argue that job quality is strongly related to life satisfaction and happiness, which would explain the explanatory power that this dimension acquires on SWB in this period of life.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHD and EAD appeared as explanatory factors of SWB in both young and middle adulthood, which would suggest that these dimensions take relevance in the evaluation that people make about their lives during the stages in which life projects are defined and carried out (Arnett, 2007a; Arnett, 2007b; Arslan \u0026amp; Zincirli, 2020; Burke, 2017; Erikson, 1985; Garc\u0026iacute;a- \u0026Aacute;lvarez et al., 2022; Griffa \u0026amp; Moreno, 2015). At these stages, then, access to decent housing and high education would acquire value in that they are considered necessary dimensions for personal development and the economic, social and cultural integration of individuals (Bonfiglio, 2023; Panico, 2019; Vinces Sandoval et al., 2022; Wang \u0026amp; Sohail, 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, it should be noted that EAP is the only economic-material condition that influences the level of SWB at all stages of adult life. This finding would be consistent with the literature that argues that how individuals value their income and how they spend their income, as well as personal expectations, have a greater weight on SWB than objective economic-material resources (Easterlin, 2015; Kahneman \u0026amp; Deaton, 2010; Kasser \u0026amp; Sheldon, 2009; Mogilner et al., 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, both the identification of the factors that influence SWB in each of the stages of adult life and the analysis of the interplay that occurs between them allow us to incorporate a more discerning view when carrying out interventions, public policies, programs, projects and social actions that improve the well-being of the population and, consequently, human development. Thus, it becomes possible to effectively direct efforts towards each of the population groups in order to achieve an improvement in society as a whole.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe data for Argentina was collected by the Argentine Social Debt Observatory-UCA (Pontifical Catholic University of Argentina). All procedures followed the ethical guidelines recommended for research involving humans by the Declaration of Helsinki and National Law 25326 on the Protection of Personal Data in accordance with Argentine legislation. For more information, visit \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.uca.edu.ar/index.php/site/index/es/uca/observatorio-de-la-deuda-social-argentina/encuesta-de-la-deuda-social/anexo-metodologico-encuesta-deuda-social/\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by a doctoral fellowship from the National Scientific and Technical Research Council (CONICET), Argentina.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eP.F. wrote the manuscript, S.S.R.E. reviewed it. Both authors analyzed data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlvarado, H., \u0026amp; Batanero, C. (2006). El significado del teorema central del límite: Evolución histórica a partir de sus campos de problemas. In Á. Contreras de la Fuente, L. Ordónez \u0026amp; C. Batanero (Eds.), \u003cem\u003eCongreso Internacional sobre Aplicaciones y Desarrollos de la Teoría de las Funciones Semióticas \u003c/em\u003e(pp. 257-277). Universidad de Jaén.\u003c/li\u003e\n\u003cli\u003eArnett, J. J. (2007a). Emerging Adulthood: What is it and what is it good for? \u003cem\u003eChild Development Perspectives, 1\u003c/em\u003e(2), 68-73. https://doi.org/10.1111/j.1750- 8606.2007.00016.x \u003c/li\u003e\n\u003cli\u003eArnett, J. J. (2007b). Socialization in emerging adulthood: From the family to the wider world, from socialization to self-socialization In J. Grusec \u0026amp; P. Hastings (Eds.), \u003cem\u003eHandbook of socialization \u003c/em\u003e(pp. 208-231). Guilford. \u003c/li\u003e\n\u003cli\u003eArslan, Ş. N., \u0026amp; Zincirli, M. (2020). As A Reflection of Positive Psychology on Education; Subjective Well-Being. \u003cem\u003eJass Studies-The Journal of Academic Social Science Studies, 13\u003c/em\u003e(82), 131-145. https://doi.org/10.29228/JASSS.48025 \u003c/li\u003e\n\u003cli\u003eBauer, J. M., Levin, V., Munoz Boudet, A. M., Nie, P., \u0026amp; Sousa-Poza, A. (2016). Subjective Well-being Across the Lifespan in Europe and Central Asia. \u003cem\u003eJournal of Population Ageing, 10\u003c/em\u003e(2), 125-158\u003cem\u003e. \u003c/em\u003ehttps://doi.org/10.1007/s12062-016-9148-0 \u003c/li\u003e\n\u003cli\u003eBeck, A. T., Steer, R. A., \u0026amp; Brown, G. K. (2006). BDI-II. \u003cem\u003eInventario de Depresión de Beck\u003c/em\u003e. Paidós.\u003c/li\u003e\n\u003cli\u003eBlanchflower, D. G., \u0026amp; Oswald, A. J. (2019). Do Humans Suffer a Psychological Low in Midlife? Two Approaches (With and Without Controls) in Seven Data Sets. In M. 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WHO. https://apps.who.int/gb/bd/PDF/bd48/basic-documents-48th-edition- sp.pdf?ua=1#page=7 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"subjective well-being, health conditions, economic-material factors, adulthood, age groups, inequality, Argentina","lastPublishedDoi":"10.21203/rs.3.rs-7892672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7892672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study explores the influence of health and economic-material conditions on subjective well-being (SWB) in different age groups in adulthood. Using a multidimensional perspective, SWB is conceptualized as a construct that combines cognitive and affective components reflecting individuals' evaluations of their lives. Using data collected from a representative sample, the research examines how different life circumstances, such as physical health, access to resources, and socioeconomic conditions, affect perceptions of SWB.\u003c/p\u003e\u003cp\u003eThe results reveal significant differences in SWB between age groups, highlighting the fundamental role of health and economic-material factors in shaping the well-being of individuals. Younger adults have higher levels of SWB, while middle and older adults show notable declines. The study underlines the need for policies aimed at reducing health and economic inequalities, taking into account the specific challenges faced by different age groups. These results contribute to a deeper understanding of the determinants of SWB at different stages of life and offer insights for developing interventions aimed at improving quality of life.\u003c/p\u003e","manuscriptTitle":"Influence of Health and Economic-Material Conditions on Subjective Well-Being throughout the Adult Life Cycle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 13:01:38","doi":"10.21203/rs.3.rs-7892672/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ae46d59-0d1d-4a50-9da6-05efcbc3168e","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T13:11:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 13:01:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7892672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7892672","identity":"rs-7892672","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}