
==== Front
Geriatrics (Basel)
Geriatrics (Basel)
geriatrics
Geriatrics
2308-3417
MDPI

10.3390/geriatrics9050111
geriatrics-09-00111
Article
New Psychometric Evidence of the Life Satisfaction Scale in Older Adults: An Exploratory Graph Analysis Approach
https://orcid.org/0000-0002-3671-3366
Dominguez-Vergara Julio Conceptualization Methodology Validation Formal analysis Investigation Resources Writing – original draft Writing – review & editing Visualization Project administration Funding acquisition 1*
Aguilar-Salcedo Brigitte Methodology Writing – original draft Visualization 2
https://orcid.org/0000-0002-7802-6230
Orihuela-Anaya Rita Writing – original draft Writing – review & editing Visualization 2
Villanueva-Alvarado José Validation Writing – original draft Writing – review & editing 3
Iwasa Hajime Academic Editor
Yoshida Yuko Academic Editor
1 Research Direction, Universidad Tecnológica del Perú, Lima 15046, Peru
2 Research, Science and Technology Unit (UICT), Faculty of Psychology, Universidad Peruana Cayetano Heredia, Lima 15074, Peru; brigitte.aguilar.s@upch.pe (B.A.-S.); rita.orihuela@upch.pe (R.O.-A.)
3 Academic Department of Psychology, Pontificia Universidad Católica del Perú (PUCP), Lima 15088, Peru; villanueva.alvarado@pucp.edu.pe
* Correspondence: c20928@utp.edu.pe
02 9 2024
10 2024
9 5 11120 6 2024
23 8 2024
28 8 2024
© 2024 by the authors.
2024
https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
The objective of the present study was to analyze the psychometric properties of a life satisfaction scale in older Peruvian adults using an exploratory graph analysis (EGA) approach. A total of 407 older adults aged between 60 and 95 years (M = 69.5; SD = 6.7) from three comprehensive elderly care centers (CIAMs) in Lima, Peru, were recruited. A non-probabilistic convenience sampling was used. The Satisfaction with Life Scale (SWLS) was analyzed using EGA with the Gaussian GLASSO model to assess its dimensionality and structural consistency. The relationship with other variables was analyzed using scales such as the GAD-7 and PHQ-9. The network structure of the SWLS indicates a single dimension. Additionally, network loadings (nodes) were examined, showing high values (>0.35) for most items except item 1, which had a moderate loading (>0.25). Structural reliability showed that a single dimension was identified 100% of the time. The post hoc CFA considering the unidimensional network structure obtained through EGA showed satisfactory fit (χ2/df = 3.48, CFI = 0.96, TLI = 0.92, SRMR = 0.02, RMSEA = 0.07 [90% CI 0.05, 0.08]). Finally, internal consistency reliability was acceptable (ω = 0.92). The SWLS measure is robust and consistent. These findings are a valuable reference for advancing research on aging in Peru, as they provide a practical, valid, and reliable measure.

life satisfaction
older adults
psychometrics
exploratory graph analysis
Peru
Universidad Peruana Cayetano HerediaUniversidad Tecnológica del PerúThis research received no external funding, and the APC was funded by Universidad Peruana Cayetano Heredia and Universidad Tecnológica del Perú.
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pmc1. Introduction

The increase in life expectancy among older adults (OAs) has driven the development of new social, economic, and health policies aimed at improving quality of life [1] and has also led to an increase in scientific research [2]. While demographic projections show a rise in the average life expectancy of older individuals, healthy life expectancy refers to the number of years of good health that an individual can expect to live at a given age [3]. In Latin America, the United Nations [4] reports that in 2022, the older adult population reached 88.6 million, representing 13.4% of the total population, with projections estimating an increase to 16.5% by 2030.

Peru also reflects this demographic trend; according to the National Institute of Statistics and Informatics [INEI] [5], people over 60 years old make up 13.6% of the country’s population, and it is estimated that by 2070, the proportion of older adults will reach 30.1%. Regarding social factors, 33.8% of working older adults are affiliated with a pension system, and 26.8% of Peruvian households have at least one older adult as the head of the household [5]. Therefore, the quality of life for older adults requires continued employment until very advanced ages, and the quality of support networks is also relevant as it can be a key factor in life satisfaction. In health, despite 92.1% of people over 60 years old having some form of health insurance, there is a decreasing trend in seeking medical care due to high demand and inefficient care processes [6]. These issues have led older adults to experience depression, anxiety, loneliness, and feelings of abandonment, significantly affecting their quality of life [7]. Among cultural factors, older adults value functional health through independence and the ability to perform daily activities; they also appreciate active participation in religious, leisure, and social integration activities [8].

These factors present challenges for social and health services in the pursuit of a satisfying life [9]. Therefore, improving the health and life satisfaction of older adults becomes crucial, as being content with one’s current life is considered an indicator of healthy aging [10]. Additionally, life satisfaction, being a subjective assessment, is susceptible to contextual changes and is influenced by the perceptions and interpretations of older adults, relating to their health and the social and economic conditions of their environment [11]. In this regard, Diener [12] emphasizes the importance of the global cognitive evaluation that older adults make of their own lives. Various studies indicate a positive relationship between life satisfaction and subjective health, as well as its impact on self-esteem, motivation, overall health, and better coping strategies [13,14]. In this context, life satisfaction becomes relevant because it helps to demystify the stigmas about older adults.

Various studies have demonstrated that physical activity, mood, and family support influence life satisfaction [15]. Additionally, other research has shown that factors such as living conditions [16], neighborhood environment [17], technology use [18,19,20], healthy eating [21,22], transportation [23], and social service [24] positively influence life satisfaction among older adults.

Therefore, there is a need to assess life satisfaction using instruments that ensure adequate validity and reliability. The Satisfaction with Life Scale (SWLS) was developed by Diener et al. [25] and is the most widely used measure in various studies. This measure can be applied to different age groups, has been translated into more than 30 languages, and demonstrates good psychometric evidence through reliability (internal consistency and test-retest) and validity evidence (content analysis, internal structure, and convergent validity) [26]. In Latin America, the SWLS has been validated in older adults in countries such as Mexico [27], Peru [28], Chile, and Ecuador [29], focusing on exploratory, confirmatory, and invariance methods. Given that the SWLS is widely used, new validations considering cultural variation are necessary, as differences and similarities in life satisfaction judgments emerge over time due to sociocultural influences [30].

In the last decade, network models have emerged as an alternative for exploring data structures; these models also complement existing latent variable techniques such as multidimensional scaling and exploratory factor analysis [31]. In contrast to latent variable models derived from classical test theory (CTT), the determination of the internal structure of latent factors can lead to a lack of consensus in the definition and interpretation of the obtained factors [32]. An innovative way to address the relationships between items is through network analysis [33,34]. One of the challenges is the ability to visualize relationships in a diagram composed of nodes (items) and edges (partial correlations); additionally, the thickness of the edges allows for the examination of the strength of the relationships [35]. Exploratory graph analysis (EGA) is combined with a set of weighted networks [36], enabling the examination of network loadings in the node diagram, structural consistency, and facet detection algorithms [34]. In this way, EGA allows for the immediate interpretation of elements belonging to each factor through the network graph using colors, and influential relationships between items and dimensions can be observed without the need to make decisions about the type of rotation to use for the factor structure [37]. Therefore, EGA is a useful tool for exploring the factor structure and item interactions of the SWLS.

The main objective of this study is to explore the factor structure of the SWLS in older Peruvian adults using the EGA methodology. Although previous studies have analyzed the factor structure of the SWLS, these have been conducted in university samples, considering the instrument’s “free domain” nature [38,39]. The SWLS has been used in more than 4000 studies to assess an individual‘s overall evaluation of their own life. This instrument is highly relevant and applicable, as the information provided through exploratory graph analysis (EGA) can have significant implications for clinical practice with older adults. Understanding the dimensionality of the SWLS ensures that the scores derived from a single measure are valid and useful for assessing life satisfaction. Therefore, the SWLS can be a valuable tool for professionals in psychology, psychiatry, and geriatrics, enabling specific screening in mental health settings.

2. Materials and Methods

2.1. Design

This study is instrumental in nature [40], as it examined the internal structure of the SWLS with the aim of evaluating its validity and reliability in older Peruvian adults.

2.2. Participants

A total of 407 older adults, aged between 60 and 95 years (M = 69.5; SD = 6.7), were recruited from three comprehensive senior centers (CIAMs) in Lima, Peru. The selection was carried out through non-probabilistic purposive sampling. Among the advantages of this type of sampling are the adaptability of the design when the sample is specific and difficult to reach, the estimations can be sufficiently accurate if applied correctly, and it is useful for obtaining preliminary data. However, the disadvantages of non-probabilistic sampling, particularly concerning representativeness and generalizability, must be noted [41]. The details of the sociodemographic data are presented in Table 1.

2.3. Instruments

Data Sheet

The data sheet includes questions about age, gender, educational level, living arrangement, employment status, and physical activity practice.

Diener’s Satisfaction with Life Scale (SWLS, Diener et al., 1985 [25])

This is a self-report scale composed of five items that assess general life satisfaction. For this study, the Spanish version by Atienza et al. [42] was used. Response alternatives are on a five-point Likert scale ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5), with higher scores indicating greater satisfaction. This scale was validated in the Peruvian context in a sample of older adults by Caycho-Rodríguez et al. [28]. Among its psychometric properties, the SWLS showed a unidimensional structure with acceptable fit indices (χ2 = 10.960, df = 5, p = 0.05, χ2/df = 2.192, GFI = 0.983, CFI = 0.994, NFI = 0.988; RMSEA = 0.071 [90% CI 0.000, 0.129] and SRMR = 0.013) and acceptable reliability (ω = 0.93).

Measures used for evidence are based on relationships with other variables.

Patient Health Questionnaire (PHQ-9)

The PHQ-9 consists of nine items that reflect depressive symptoms, evaluated over the past two weeks with Likert-type options (0 = not at all, 1 = several days, 2 = more than half the days, 3 = nearly every day). The total score ranges from 0 to 27. Validated in the Peruvian population, the PHQ-9 shows a unidimensional model with good fit indicators (CFI = 0.99; TLI = 0.987; SRMR = 0.048; RMSEA = 0.071) and adequate reliability (ω = 0.861) [43]. For this study, reliability was assessed using the omega coefficient, yielding a value of 0.89.

Generalized Anxiety Disorder Scale (GAD-7)

This is a widely used screening measure to assess generalized anxiety. It contains seven items, and respondents are asked to rate the frequency of their anxiety symptoms over the past two weeks. The GAD-7 response options range from 0 (not at all) to 3 (nearly every day). The GAD-7 was validated by Franco-Jiménez and Nuñez-Magallanes [44], showing a unidimensional model with good indices (χ2 = 31.717, CFI = 0.995, TLI = 0.992, RMSEA = 0.056, SRMR = 0.026) and high reliability (ω = 0.92). For this research, reliability was calculated using the omega coefficient, achieving a value of 0.92.

2.4. Data Collection

Data collection was conducted between May and September 2023. To identify the participants, first, contact was made with four comprehensive senior centers (CIAMs) in the districts of Lima. Second, the center coordinators were contacted to inform them about this study and to schedule potential dates for data collection. Third, the researchers attended the CIAMs during the seniors’ activity sessions and consulted them in advance about their availability to participate in this study. Fourth, informed consent was provided to the participants, outlining this study’s objectives, confidentiality, anonymity, and voluntary participation. Fifth, once the older adults agreed to participate, they completed the questionnaires individually. For those who were illiterate, the statements and response options were read aloud to ensure accurate completion of the instruments, thereby avoiding biases related to reading and comprehension difficulties.

2.5. Data Analysis

A descriptive analysis of the SWLS was performed, obtaining measures of mean, standard deviation, skewness, and kurtosis. Additionally, response rates per item were calculated in percentages (%), considering the ordinal nature of the variable. The dimensionality of the SWLS was verified using the Gaussian GLASSO model through exploratory graph analysis (EGA) with the EGAnet library [45]. Network loadings were also calculated using the “net.loads” function, where small (0.15), moderate (0.25), and large (0.35) network values were considered [46]. Structural consistency was examined using the “bootEGA” function, which extracts dimensions derived from EGA through a Bootstrap simulator with 1000 replications. Item stability is explained by the number of times it replicates in the same dimension. A minimum threshold of 75% was used to assess the consistency and stability of the SWLS items [47].

Once the factorial structure was obtained through EGA, a post hoc confirmatory factor analysis (CFA) was performed. For the analysis of the factorial model, the weighted least squares mean and variance adjusted (WLSMV) estimation method was used, which is suitable for items with ordinal characteristics. Model fit indices were examined using the chi-square test (χ2), the root mean square error of approximation (RMSEA) with 90% confidence intervals, the standardized root mean square residual (SRMR), the comparative fit index (CFI), and the Tucker–Lewis index (TLI). For model fit evaluation, values less than 0.08 for RMSEA and SRMR indices and values greater than 0.90 for CFI and TLI were considered. Additionally, factor loadings were calculated, all of which were greater than 0.50 for each item.

The estimation of model reliability was obtained using McDonald’s Omega coefficient (ω) with 95% confidence intervals, considering acceptable values greater than 0.80.

Statistical analysis was performed using the RStudio environment [48].

3. Results

3.1. Preliminary Analysis of the SWLS

Descriptive measures were calculated, finding that item 3 (“I am satisfied with my life”) had the highest mean (M = 3.70), while item 2 (“The conditions of my life are excellent”) had the lowest mean value (M = 3.51). The standard deviation was higher for item 3 (SD = 1.27) and lower for item 2 (SD = 1.12). Skewness and kurtosis coefficients for all items showed values exceeding ± 1.5, suggesting that the data do not follow a normal distribution [49]. Item–test correlations exceeded the criterion of 0.20 for all five SWLS items [50]. Response percentages for the items demonstrated a high agreement tendency in option 4 (“agree”) (view Table 2).

3.2. Exploratory Graph Analysis

Figure 1 shows the network structure of the SWLS items using EGA, where a single dimension is evident. Additionally, network loadings (nodes) were examined, with high values (>0.35) observed in most items, except for item 1, which presented a moderate loading (>0.25). Regarding structural consistency, it was evidenced that 100% of the time, a single dimension was identified; this is verified through the stability graph, where the items were systematically identified in a single community.

3.3. Post Hoc Confirmatory Factor Analysis and Relationship with Other Variables

A post hoc confirmatory factor analysis (CFA) was conducted, considering the network structure obtained through EGA. The unidimensional structure demonstrated satisfactory fit (χ2/df = 3.48, CFI = 0.96, TLI = 0.92, SRMR = 0.02, RMSEA = 0.07 [90% CI 0.05, 0.08]). Factor loadings were acceptable (λ > 0.5), ensuring that the items adequately represented the construct (Figure 2). Additionally, negative correlations were found between life satisfaction scores and anxiety (r = −0.144) and depression (r = −0.129).

Finally, reliability through internal consistency using McDonald’s omega coefficient was acceptable (ω = 0.92, 95% CI [0.91–0.94]).

4. Discussion

The present study aimed to validate the SWLS using a network model in older Peruvian adults. Among the findings, it was evidenced that the SWLS presents adequate evidence concerning its reliability, internal structure, and relationship with variables (anxiety and depression). Thus, the results support the unidimensional structure through EGA with precision and stability.

According to various psychometric studies, the SWLS has been validated using different factor and invariance methods in different samples, including adolescents [51], university students [38,52], clinical samples [53], and community samples [54]. However, the SWLS has not been analyzed in older adults through a psychometric network approach. Therefore, network models have proven to be superior to traditional factor analysis techniques [55,56], especially highlighting the absence of factor rotation and their intuitive nature.

EGA was used to examine the internal structure to evaluate the dimensionality and determine the number of factors present [47,57]. Thus, the unidimensional structure of the SWLS is consistent with the original model and supported by various studies with older adults [27,29,58,59,60]. Regarding connections within the network, item 3 (SWLS3: “I am satisfied with my life”) showed the strongest node loading in relation to the direct measure of life satisfaction. The SWLS items presented adequate loadings, indicating that they are directly linked to life satisfaction and suggesting a reciprocal cause–effect relationship between the network attributes [61]. Following the exploratory graph analysis, a post hoc confirmatory factor analysis of the SWLS was conducted, obtaining acceptable indices, thus validating a unidimensional structure that functions in older adults.

Reliability was determined through structural consistency, verifying that all data were systematically organized into a single dimension from replications. These results demonstrate the stability of the reciprocal variance between items. However, no previous study has analyzed the SWLS in older adults from a network analysis approach, but only under the framework of classical test theory. Adequate internal consistency values have been found through the estimation of Cronbach’s alpha coefficients [27,29] and Omega [28]. Despite this, the use of the network model is preferred due to its incompatibility with calculation, as common covariances are eliminated, valuing item correlations; moreover, internal consistency measures do not adequately report whether items remain through a unidimensional factor in multidimensional models [34]. In addition, this study also thoroughly analyzed internal consistency reliability, obtaining acceptable measures (ω = 0.92) for the SWLS.

The behavior of the SWLS with other measures found negative correlations with anxiety and depression. These results are consistent with previous studies that similarly report this association between the aforementioned variables [62,63]. This explains that the coexistence of anxiety symptoms can cause cognitive maladaptation, feelings of loneliness [64], and a decrease in quality of life [65], significantly affecting life satisfaction in older adults [66].

Among the practical implications, the use of the EGA methodology that supports the unidimensional determination of the SWLS stands out. In this way, this procedure contributes to the breadth of knowledge, as the EGA managed to identify a single dimension impartially and in congruence with what was analyzed in the literature. These findings offer a clear scope of the relationships of the items with the SWLS dimension, as well as how they position themselves within a single community. On the other hand, the internal structure of the SWLS conceptually contributes to the study of life satisfaction in samples of older adults in a Peruvian context. The use of the instrument will be useful in specific areas that support the accuracy of their evaluations, such as psychology, geriatrics, and medicine. The exploration of a single dimension is structurally in line with what is reported through various psychometric evaluation studies, with the SWLS functioning in various sociocultural contexts. Therefore, having a practical, brief, simple, and self-report measure will allow incorporating personal data on life satisfaction.

Despite the important results, this study has some limitations. First, although the sample size was moderate, it could not be subdivided to perform both validation procedures (EGA and CFA), so a post hoc CFA was conducted. For future research, it is suggested to adjust the unidimensional model of the SWLS using CFA with a similar sample. Second, the older adults recruited in comprehensive senior centers were selected through convenience sampling; therefore, they do not necessarily constitute an adequate representation of older adults in Peru. Third, the cross-sectional study does not allow for the estimation of temporal or directional relationships, limiting conclusions about causal relationships between the SWLS, depression, and anxiety. Fourth, due to non-probabilistic sampling, the sample was predominantly composed of women. Finally, self-report measures can introduce social desirability bias, as participants might present a positive image of their lives.

5. Conclusions

In conclusion, the SWLS shows satisfactory results in terms of validity and reliability, making it a suitable instrument for use and consolidation in future psychometric studies as a tool for measuring life satisfaction in older adults.

Acknowledgments

The authors express their gratitude to the comprehensive senior centers for their selfless support in the development of this study.

Author Contributions

Conceptualization, J.D.-V.; methodology, J.D.-V. and B.A.-S.; validation, J.D.-V. and J.V.-A.; formal analysis J.D.-V.; investigation, J.D.-V.; resources, J.D.-V.; writing—original draft preparation, J.D.-V., B.A.-S., R.O.-A. and J.V.-A.; writing—review and editing, J.D.-V., R.O.-A. and J.V.-A.; visualization, J.D.-V., B.A.-S. and R.O.-A.; project administration, J.D.-V.; funding acquisition, J.D.-V. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Universidad Tecnológica del Perú (protocol code 124-2023-CEI-UTP, 1 August 2023) for studies involving humans.

Informed Consent Statement

Informed consent was received from all participants in this study.

Data Availability Statement

The data shown can be accessed by contacting the corresponding author and are available for academic and research purposes.

Conflicts of Interest

The authors declare no conflicts of interest.

Figure 1 Dimensionality and structural stability of items.

Figure 2 Factor structure of the SWLS.

geriatrics-09-00111-t001_Table 1 Table 1 Characteristics of older adults (n = 407).

Variable	Category	Frequency	%	
Age (M ± SD)		69.5 ± 6.7		
Sex	Male	160	39.3	
Female	247	60.7	
Living Arrangement	Lives alone	35	8.6	
Lives with spouse	106	26	
Lives with children	120	29.5	
Lives with spouse and children	112	27.5	
Marital Status	Single	40	9.8	
Married	227	55.8	
Cohabiting	26	6.4	
Divorced/separated	35	8.6	
Widowed	79	19.4	
Educational Level	No education	8	2	
With basic education	181	44.4	
Higher education	122	30	
Employment	Yes	103	25.3	
No	304	74.7	
Physical Activity	Never	16	3.9	
Rarely	78	19.2	
Sometimes	177	43.5	
Frequently	108	26.5	
Very frequently	28	6.9	
Note: M = mean; SD = standard deviation.

geriatrics-09-00111-t002_Table 2 Table 2 Descriptive statistics, item–test correlation, and response percentages (n = 407).

SWLS Items	M	SD	g1	g2	Item–Test Correlation	Responses (%)	
1	2	3	4	5	
SWLS—1	3.47	1.06	−0.71	−0.01	0.74	7	10	25	45	13	
SWLS—2	3.51	1.12	−0.81	−0.07	0.84	9	10	19	47	15	
SWLS—3	3.70	1.27	−0.85	−0.27	0.85	11	6	19	33	32	
SWLS—4	3.69	1.17	−0.76	−0.25	0.83	7	11	17	38	27	
SWLS—5	3.54	1.25	−0.63	−0.56	0.78	10	10	21	33	25	
Note: M = mean; SD = standard deviation; g1 = skewness; g2 = kurtosis; 1 = strongly disagree; 2 = disagree; 3 = neither agree nor disagree; 4 = agree; 5 = strongly agree; SWLS—1 = in most respects, my life is as I want it to be; SWLS—2 = the conditions of my life are good; SWLS—3 = I am satisfied with my life; SWLS—4 = so far I have gotten the important things I want in life; SWLS—5 = if I could live my life over, I would repeat it just the same way it has been.

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References

1. World Health Organization By 2024, The 65-and-over Age Group Will Outnumber the Youth Group: New WHO Report on Healthy Ageing: WHO World Health Organization Geneva, Switzerland 2024 Available online: https://www.who.int/azerbaijan/news/item/11-10-2023-by-2024--the-65-and-over-age-group-will-outnumber-the-youth-group--new-who-report-on-healthy-ageing (accessed on 10 August 2024)
2. Kutubaeva R.Z. Analysis of life satisfaction of the elderly population on the example of Sweden, Austria and Germany Popul. Econ. 2019 3 102 116 10.3897/popecon.3.e47192
3. Stiefel M.C. Perla R.J. Zell B.L. A Healthy Bottom Line: Healthy Life Expectancy as an Outcome Measure for Health Improvement Efforts Milbank Q. 2010 88 30 53 10.1111/j.1468-0009.2010.00588.x 20377757
4. United Nations World Population Ageing 2022 UN New York, NY, USA 2022
5. Instituto Nacional de Estadística e Informática (INEI) Informe Técnico: Situación de la Población Adulta Mayor INEI Lima, Peru 2024 Available online: https://www.gob.pe/institucion/inei/colecciones/6117-poblacion-adulta-mayor (accessed on 6 June 2024)
6. Gutiérrez C. Romaní F.R. Wong P. Sara J.D.C. Brecha entre cobertura poblacional y prestacional en salud: Un reto para la reforma de salud en el Perú An. Fac. Med. 2018 79 65 70 10.15381/anales.v79i1.14595
7. Tenorio-Mucha J. Romero-Albino Z. Roncal-Vidal V. Cuba-Fuentes M.S. Calidad de vida de adultos mayores de la Seguridad Social peruana durante la pandemia por COVID-19 Rev. Del Cuerpo Médico Hosp. Nac. Almanzor Aguinaga Asenjo 2021 14 41 48 10.35434/rcmhnaaa.2021.14Sup1.1165
8. Domínguez-Vergara J. Santa-Cruz-Espinoza H. Torres-Villanueva G.N. Zelada E.F.C. Dominios operativos del envejecimiento saludable: Una descripción cualitativa en personas adultas mayores de Perú Rev. Esp. Geriatr. Gerontol. 2024 59 101485 10.1016/j.regg.2024.101485 38518548
9. Jauhiainen J.S. Will the retiring baby boomers return to rural periphery? J. Rural. Stud. 2008 25 25 34 10.1016/j.jrurstud.2008.05.001
10. Mekonnen H.S. Lindgren H. Geda B. Azale T. Erlandsson K. Satisfaction with life and associated factors among elderly people living in two cities in northwest Ethiopia: A community-based cross-sectional study BMJ Open 2022 12 e061931 10.1136/bmjopen-2022-061931
11. Bramhankar M. Kundu S. Pandey M. Mishra N.L. Adarsh A. An assessment of self-rated life satisfaction and its correlates with physical, mental and social health status among older adults in India Sci. Rep. 2023 13 9117 10.1038/s41598-023-36041-3 37277415
12. Diener E. The Remarkable Changes in the Science of Subjective Well-Being Perspect. Psychol. Sci. 2013 8 663 666 10.1177/1745691613507583 26173230
13. Inga J. Vara A. Factores Asociados a la Satisfacción de la Vida de Adultos Mayores de 60 años en Lima-Perú Univ. Psychol. 2006 5 475 485
14. Cancino-Ulloa A. Contreras-Saavedra C. Descripción del índice Satisfacción con la Vida (LSI-A) de personas mayores participantes en Programa del Adulto Mayo en una Universidad Chilena Rev. Estud. Exp. Educ. 2023 22 121 140 10.21703/rexe.v22i50.2065
15. Hong R.Q. Abdul Rahman H. Ali M. Hoon C.Y. Slesman L. Tengah A. Yusof-Kozlowski Y.M. Abdul-Mumin K.H. Health Determinants of Life Satisfaction Among Older Adults in Brunei: A Multivariate Analysis Ageing Int. 2023 49 64 77 10.1007/s12126-023-09528-7
16. Lee M.A. Ryu H. Kim G. Is living alone beneficial to older adults during the COVID-19 pandemic? Examining associations between living arrangements and life satisfaction by gender in Korea Aging Ment Health 2023 28 121 129 10.1080/13607863.2023.2253182 37697800
17. Pan Z. Liu Y. Liu Y. Huo Z. Han W. Age-friendly neighbourhood environment, functional abilities and life satisfaction: A longitudinal analysis of older adults in urban China Soc. Sci. Med. 2024 340 116403 10.1016/j.socscimed.2023.116403 37989046
18. Na Jeong H. Chang S.J. Kim S. Associations with smartphone usage and life satisfaction among older adults: Mediating roles of depressive symptoms and cognitive function Geriatr. Nurs. 2024 55 168 175 10.1016/j.gerinurse.2023.11.013 38006722
19. Cheng X. Ge T. Cosco T.D. Internet use and life satisfaction among Chinese older adults: The mediating effects of social interaction Curr. Psychol. 2023 43 717 724 10.1007/s12144-023-04303-y
20. Ma J. Zhang S. Yang H. Tang X. Internet use and Chinese migrant older adults’ life satisfaction: A panel data study Int. J. Soc. Welf. 2023 33 381 392 10.1111/ijsw.12609
21. Borisenkov M.F. Dorogina O.I. Popov S.V. Smirnov V.V. Pecherkina A.A. Symaniuk E.E. The Positive Association between Melatonin-Containing Food Consumption and Older Adult Life Satisfaction, Psychoemotional State, and Cognitive Function Nutrients 2024 16 1064 10.3390/nu16071064 38613097
22. Wang R.S. Huang Y.-N. Wahlqvist M.L. Wan T.T.H. Tung T.-H. Wang B.-L. The combination of physical activity with fruit and vegetable intake associated with life satisfaction among middle-aged and older adults: A 16-year population-based cohort study BMC Geriatr. 2024 24 41 10.1186/s12877-023-04563-0 38195433
23. Sun H. Jing P. Liu Y. Wang D. Wang B. Xu M. The causal effect of private transport on life satisfaction among older adults and the mediation effect of social participation in China Cities 2024 148 104865 10.1016/j.cities.2024.104865
24. Wang H. Liu H. Wu B. Hai L. The Association between Trajectories of Perceived Unmet Needs for Home and Community-Based Services and Life Satisfaction Among Chinese Older Adults: The Moderating Effect of Psychological Resilience Res. Aging 2023 46 139 152 10.1177/01640275231203608 37768843
25. Diener E. Subjective well-being Psychol. Bull. 1984 95 542 575 10.1037/0033-2909.95.3.542 6399758
26. Townshend K. Satisfaction with Life Scale (SWLS) Handbook of Assessment in Mindfulness Research Medvedev O.N. Krägeloh C.U. Siegert R.J. Singh N.N. Springer Cham, Switzerland 2023 83-1 10.1007/978-3-030-77644-2_83-1
27. López-Ortega M. Torres-Castro S. Rosas-Carrasco O. Psychometric properties of the Satisfaction with Life Scale (SWLS): Secondary analysis of the Mexican Health and Aging Study Health Qual. Life Outcomes 2016 14 170 10.1186/s12955-016-0573-9 27938407
28. Caycho-Rodríguez T. Ventura-León J. Cadena C.H.G. Barboza-Palomino M. Gallegos W.L.A. Dominguez-Vergara J. Azabache-Alvarado K. Cabrera-Orosco I. Pinho A.S. Psychometric Evidence of the Diener’s Satisfaction with Life Scale in Peruvian Elderly Rev. Cienc. Salud 2018 16 488 506 10.12804/revistas.urosario.edu.co/revsalud/a.7267
29. Schnettler B. Miranda-Zapata E. Lobos G. Lapo M.d.C. Adasme-Berríos C. Hueche C. Measurement invariance in the Satisfaction with Life Scale in Chilean and Ecuadorian older adults Pers. Individ. Differ. 2017 110 96 101 10.1016/j.paid.2017.01.036
30. Pavot W. Diener E. The Satisfaction with Life Scale and the emerging construct of life satisfaction J. Posit. Psychol. 2008 3 137 152 10.1080/17439760701756946
31. Borsboom D. Deserno M.K. Rhemtulla M. Epskamp S. Fried E.I. McNally R.J. Robinaugh D.J. Perugini M. Dalege J. Costantini G. Network analysis of multivariate data in psychological science Nat. Rev. Methods Prim. 2021 1 58 10.1038/s43586-021-00055-w
32. Borsboom D. Mellenbergh G.J. van Heerden J. The theoretical status of latent variables Psychol. Rev. 2003 110 203 219 10.1037/0033-295X.110.2.203 12747522
33. Manson J.H. Kruger D.J. Network analysis of psychometric life history indicators Evol. Hum. Behav. 2022 43 197 211 10.1016/j.evolhumbehav.2022.01.004
34. Christensen A.P. Golino H. Silvia P.J. A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires Eur. J. Pers. 2020 34 1095 1108 10.1002/per.2265
35. Epskamp S. Fried E.I. A tutorial on regularized partial correlation networks Psychol. Methods 2018 23 617 634 10.1037/met0000167 29595293
36. Pons P. Latapy M. Computing communities in large networks using random walks Comput. Inf. Sci. 2005 3733 284 293 10.1007/11569596_31
37. Golino H. Shi D. Christensen A.P. Garrido L.E. Nieto M.D. Sadana R. Thiyagarajan J.A. Martínez-Molina A. Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial Psychol. Methods 2020 25 292 320 10.1037/met0000255 32191105
38. Lingán-Huamán S.K. Ventura-León J. Sakihara C.T. Callo C.C. The satisfaction with life scale and the well-being index WHO-5 in young Peruvians: A network analysis Int. J. Adolesc. Youth 2024 29 2331586 10.1080/02673843.2024.2331586
39. Diaz-Milanes D. Salado V. Santín Vilariño C. Andrés-Villas M. Pérez-Moreno P.J. A network analysis study on the structure and gender invariance of the Satisfaction with Life Scale among Spanish university students Healthcare 2024 12 237 10.3390/healthcare12020237 38255125
40. Montero I. León G. Sistema de Clasificación del Método en los Informes de Investigación Int. J. Clin. Health Psychol. 2005 5 115 127
41. Cornesse C. Blom A.G. Dutwin D. Krosnick J.A. De Leeuw E.D. Legleye S. Pasek J. Pennay D. Phillips B. Sakshaug J.W. A review of conceptual approaches and empirical evidence on probability and nonprobability sample survey research J. Surv. Stat. Methodol. 2020 8 4 36 10.1093/jssam/smz041
42. Atienza F.L. Pons D. Balaguer I. García-Merita M. Propiedades Psicométricas de la Escala de Satisfacción con la Vida en Adolescentes Psicothema 2000 12 314 319 Available online: https://reunido.uniovi.es/index.php/PST/article/view/7597 (accessed on 10 July 2024)
43. Cjuno J. Julca-Guerrero F. Oruro-Zuloaga Y. Cruz-Mendoza F. Auccatoma-Quispe A. Gómez-Hurtado H. Peralta-Alvarez F. Bazo-Alvarez J.C. Adaptación cultural al Quechua y análisis psicométrico del Patient Health Questionnaire (PHQ-9) en población peruana Rev. Peru. Med. Exp. Salud Publica 2023 40 267 277 10.17843/rpmesp.2023.403.12571 37991030
44. Franco-Jimenez R.A. Nuñez-Magallanes A. Propiedades psicométricas del GAD-7, GAD-2 y GAD-Mini en universitarios peruanos Propos. Represent. 2022 10 10.20511/pyr2022.v10n1.1437
45. Golino H.F. Christensen A.P. EGAnet: Exploratory Graph Analysis: A Framework for Estimating the Number of Dimensions in Multivariate Data Using Network Psychometrics [Internet] 2020 Available online: https://cran.r-project.org/web/packages/EGAnet/index.html (accessed on 11 July 2024)
46. Christensen A.P. Golino H. On the equivalency of factor and network loadings Behav. Res. Methods 2021 53 1563 1580 10.3758/s13428-020-01500-6 33409985
47. Golino H. Moulder R. Shi D. Christensen A.P. Garrido L.E. Nieto M.D. Boker S.M. Entropy fit indices: New fit measures for assessing the structure and dimensionality of multiple latent variables Multivar. Behav. Res. 2021 56 874 902 10.1080/00273171.2020.1779642 32634057
48. R Studio Team RStudio: Integrated Development Environment for R. RStudio, Inc R Studio Team: 2018 Available online: https://www.rstudio.com (accessed on 15 July 2024)
49. Finney S.J. DiStefano C. Non-normal and categorical data in structural equation modeling Structural Equation Modeling: A Second Course Hancock G.R. Mueller R.O. Information Age Publishing, Inc. Charlotte, NC, USA 2006 Volume 10 269 314
50. Kline T.J.B. Psychological Testing: A Practical Approach to Design and Evaluation SAGE Publications Thousand Oaks, CA, USA 2005
51. Areepattamannil S. Bano S. Psychometric properties of the Satisfaction with Life Scale (SWLS) among middle adolescents in a collectivist cultural setting Psychol. Stud. 2020 65 497 503 10.1007/s12646-020-00578-4
52. Anthimou A. Koutsogiorgi C. Michaelides M.P. Psychometric properties of the Satisfaction with Life Scale in a Cypriot student sample Psychol. J. Hell. Psychol. Soc. 2021 26 273 282 10.12681/psy_hps.29152
53. Cerezo M.V. Soria-Reyes L.M. Alarcon R. Blanca M.J. The Satisfaction with Life Scale in breast cancer patients: Psychometric properties Int. J. Clin. Health Psychol. 2022 22 100274 10.1016/j.ijchp.2021.100274 34703465
54. Arrindell W.A. Checa I. Espejo B. Chen I.H. Carrozzino D. Vu-Bich P. Dambach H. Vagos P. Measurement invariance and construct validity of the Satisfaction with Life Scale (SWLS) in community volunteers in Vietnam Int. J. Environ. Res. Public Health 2022 19 3460 10.3390/ijerph19063460 35329151
55. Jiménez M. Abad F.J. Garcia-Garzon E. Golino H. Christensen A.P. Garrido L.E. Dimensionality assessment in bifactor structures with multiple general factors: A network psychometrics approach Psychol. Methods 2023 10.1037/met0000590 37410419
56. Cosemans T. Rosseel Y. Gelper S. Exploratory Graph Analysis for Factor Retention: Simulation Results for Continuous and Binary Data Educ. Psychol. Meas. 2022 82 880 910 10.1177/00131644211059089 35989724
57. Epskamp S. Waldorp L.J. Mõttus R. Borsboom D. The Gaussian graphical model in cross-sectional and time-series data Multivar. Behav. Res. 2018 53 453 480 10.1080/00273171.2018.1454823
58. Lee K. Validation of the satisfaction with life scale for Korean older adults using item response theory J. Health Psychol. 2024 13591053241233461 10.1177/13591053241233461 38384145
59. Durak M. Senol-Durak E. Gencoz T. Psychometric Properties of the Satisfaction with Life Scale among Turkish University Students, Correctional Officers, and Elderly Adults Soc. Indic. Res. 2010 99 413 429 10.1007/s11205-010-9589-4
60. von Humboldt S. Leal I. A Health-Related Satisfaction with Life Scale Measure for Use with Cross-National Older Adults: A Validation Study Rev. Eur. Stud. 2017 9 21 10.5539/res.v9n3p21
61. Borsboom D. A network theory of mental disorders World Psychiatry 2017 16 5 13 10.1002/wps.20375 28127906
62. Kekäläinen T. Koivunen K. Pynnönen K. Portegijs E. Rantanen T. Cohort Differences in Depressive Symptoms and Life Satisfaction in 75- and 80-Year-Olds: A Comparison of Two Cohorts 28 Years Apart J. Aging Health 2023 36 3 13 10.1177/08982643231164739 36947727
63. Głowacka M. Przybyła N. Humańska M. Kornatowski M. Depression and anxiety as predictors of performance status and life satisfaction in older adult neurological patients: A cross-sectional cohort study Front. Psychiatry 2024 15 1412747 10.3389/fpsyt.2024.1412747 38832331
64. Curran E. Rosato M. Ferry F. Leavey G. Prevalence and factors associated with anxiety and depression in older adults: Gender differences in psychosocial indicators J. Affect. Disord. 2020 267 114 122 10.1016/j.jad.2020.02.018 32063562
65. Ribeiro O. Teixeira L. Araújo L. Rodríguez-Blázquez C. Calderón-Larrañaga A. Forjaz M.J. Ribeiro O. Teixeira L. Araújo L. Rodríguez-Blázquez C. Anxiety, Depression and Quality of Life in Older Adults: Trajectories of Influence across Age Int. J. Environ. Res. Public Health 2020 17 9039 10.3390/ijerph17239039 33291547
66. Saldivia S. Aslan J. Cova F. Bustos C. Inostroza C. Castillo-Carreño A. Life satisfaction, positive affect, depression and anxiety symptoms, and their relationship with sociodemographic, psychosocial, and clinical variables in a general elderly population sample from Chile Front. Psychiatry 2023 14 1203590 10.3389/fpsyt.2023.1203590 37441146
