Validation and measurement invariance of the Personal Financial Wellness Scale: Evidence from Chilean Young Adults by Gender, Ethnicity, and Employment Status

Validation and measurement invariance of the Personal Financial Wellness Scale: Evidence from Chilean Young Adults by Gender, Ethnicity, and Employment Status | 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 Validation and measurement invariance of the Personal Financial Wellness Scale: Evidence from Chilean Young Adults by Gender, Ethnicity, and Employment Status Luis Mario Castellanos-Alvarenga, José Andrés Sepúlveda-Maldonado, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8175823/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 Background Financial well-being is a key determinant of quality of life, since it reflects the individual's ability to meet current and ongoing financial obligations, feel secure about their financial future, and make choices that allow enjoyment of life. This is particularly important contexts with high inequality and uncertainty such as the case of Chile where many individuals rely on consumer credit from retail and banking institutions for monthly expenses. Therefore, having an instrument the reliably measures said variable across different sociodemographic groups, such as gender, ethnicity, and employment status is essential. Methods This study examined the psychometric properties of the Personal Financial Wellness Scale (PFWS) among Chilean emerging adults. An adapted version of the PFWS was administered to 624 university and technical students (64.1% women), aged 18 to 29 years (M = 20.44, SD = 3.35). Exploratory Structural Equation Modeling (ESEM) was used to test the factorial structure, and multigroup analyses evaluated measurement invariance across gender, ethnicity, and employment status. Results Results supported a two-factor model, financial distress and financial well-being, with satisfactory internal consistency and model fit. Partial measurement invariance was established across ethnicity and employment status, with changes in fit indices remaining within acceptable thresholds (ΔCFI ≤ .01, ΔRMSEA ≤ .015, ΔSRMR ≤ .030). For ethnicity, constraints on item 7 were released, and for employment status, intercepts of items 4 and 7 were relaxed. In contrast, full invariance was achieved across gender, with non-significant model comparisons (χ²(26) = 42.350, p = .023; χ²(52) = 61.946, p = .163) and stable fit indices across nested models (ΔCFI = 0.000, ΔRMSEA = 0.005, ΔSRMR = 0.013). Conclusions These findings confirm that the PFWS captures essential dimensions of financial well-being in a culturally sensitive way, supporting its use as an early detection tool for financial distress and for guiding targeted interventions in socioeconomically vulnerable young adults. Financial well-being Psychometrics Emerging adulthood Measurement invariance Introduction Financial well-being is a key determinant of quality of life (Mathew et al., 2022), as it reflects an individual's ability to meet current and ongoing financial obligations, feel secure about their financial future, and make choices that allow enjoyment of life (Tahir & Ahmed, 2021). Beyond economic sufficiency, it encompasses perceptions of control (Bialowolski, Weziak-Bialowolska, & McNeely, 2021), autonomy (Kumar et al., 2023), and mental health regarding financial matters (Ryu & Fan, 2023), influencing relational stability (Kelley et al., 2023), and overall life satisfaction (Foong et al., 2021). This multidimensional view of financial well-being becomes particularly relevant in contexts where high levels of debt and financial insecurity are prevalent (Simonse et al., 2024). In Chile, over 57% of households are in debt and many individuals rely on consumer credit from retail and banking institutions (Banco Central Chile, 2021). Moreover, 30.1% of young adults between 18 and 29 years report being heavily indebted, a figure that increases among women and younger adults (Instituto Nacional de la Juventud [INJUV], 2020). The growing acceptance of credit use and consumer debt, particularly among emerging adults, has generated increasing concern about their susceptibility to financial stress and psychological vulnerability (Castellanos-Alvarenga & Denegri-Coria, 2024). While financial stress has traditionally been associated with external conditions like income instability or inflation (Friedline et al., 2021), it also reflects subjective perceptions of financial inadequacy and insecurity (Kasal, 2023). The consequences of poor financial well-being include increased financial stress (Tran et al., 2025), reduced academic and job performance (Rosso et al., 2024), and lower subjective well-being (Rahman et al., 2021). Poor personal financial wellness has been consistently associated with adverse psychological outcomes, including higher levels of stress, anxiety, and depressive symptoms (Bialowolski et al., 2021; Guan et al., 2022). It may also impair sleep quality (Du et al., 2021), increases substance use (Nigatu et al., 2024), and negatively affects the quality of interpersonal relationships (Peetz et al., 2024). These associations are particularly relevant during emerging adulthood, a stage characterized by greater economic instability, increased vulnerability to mental health problems, and the pursuit of financial independence(Barrera-Herrera et al., 2019). This life period, situated between adolescence and full adulthood, involves significant developmental transitions that often intensify financial pressures and emotional demands (Felinto et al., 2020; Meier, 2020). Therefore, accurate assessment of personal financial wellness in young adults is not only important for financial behavior research, but also for identifying individuals at risk of psychological comorbidities and for informing early preventive strategies. To address this, several studies have highlighted the need for accurate and context-sensitive instruments to assess personal financial well-being in emerging adults (Castellanos-Alvarenga et al., 2022; She et al., 2023; Vosylis & Klimstra, 2020). The Personal Financial Wellness Scale (PFWS) is one of the most widely used instruments to measure financial well-being as a multidimensional construct, incorporating both objective and subjective aspects (Sabri & Aw, 2020). It has been validated in multiple countries and populations, but evidence on its psychometric properties in Latin American youth is scarce (Buabang et al., 2022). Moreover, this gap is particularly critical in Chile, where structural inequalities intersect with financial instability (Araujo, 2019), which is reflected in high levels of student debt (Sepúlveda-Maldonado et al., 2022), and precarious job conditions (Comisión para el Mercado Financiero [CMF], 2025; Marambio-Tapia, 2021). These socioeconomic dynamics underscore the need for a rigorous evaluation of the scale’s validity and measurement properties within the Chilean context. Beyond establishing internal consistency and factorial structure, it is essential to test the measurement invariance of the PFWS across key sociodemographic groups, particularly gender, ethnicity, and employment status. This issue is especially relevant in the Region of La Araucanía, where approximately 32.82% of the population self-identifies as Mapuche (Instituto Nacional de Estadística, 2018). The persistence of structural and cultural inequalities affecting Mapuche communities (Castillo et al., 2022; Delgado et al., 2025) raises the need to determine whether the PFWS functions equivalently across ethnic groups. Likewise, gender and employment status are key variables in the Chilean socio-economic landscape, as women and unemployed youth are disproportionately affected by financial vulnerability (Baquedano-Rodríguez et al., 2025; OECD, 2022) Without establishing invariance, group comparisons may yield misleading conclusions, undermining both the interpretability of research findings and the equity of interventions based on such data. Addressing this gap, the present study aims to examine the factorial validity of the Personal Financial Wellness Scale (PFWS) and to evaluate its measurement invariance across gender, ethnicity, and employment status in a sample of emerging adults from the Region of La Araucanía, Chile. Methodology Research Design The study utilized a cross-sectional design with a correlational and explanatory scope, applying a non-probabilistic purposive sampling strategy. Data were gathered through an online questionnaire administered to higher education students in the Araucanía Region, Chile, enabling the assessment of the factorial structure, internal consistency, and measurement invariance of the Personal Financial Wellness Scale across gender, ethnicity, and employment status at a single point in time. Procedure The research protocol was reviewed and approved by a university-based Research Ethics Committee in Chile, in accordance with the principles outlined in the Declaration of Helsinki. Participants were invited to complete the study instruments via an online survey platform. The estimated response time was approximately 15 minutes. All participants provided digital informed consent prior to beginning the questionnaire. Participation was entirely voluntary, and no financial or material incentives were offered. Anonymity and confidentiality were assured throughout the data collection process. Participants The study was conducted using a cross-sectional design with a correlational and explanatory approach, based on a non-probabilistic purposive sampling method (Creswell, 2014). The sample size was calculated a priori to ensure statistical power and reliable parameter estimates for structural equation modeling analyses, with Exploratory Structural Equation Modeling (ESEM) approach (Soper, 2024). ESEM is an advanced statistical technique that integrates features of exploratory factor analysis within a structural equation modeling framework, allowing for more flexible modeling of latent constructs by permitting cross-loadings (Morin, 2023). The final sample consisted of 624 higher education students, of whom 63.1% identified as female. The average age was 20.44 years (SD = 3.35). Inclusion criteria were: (1) being between 18 and 29 years old, (2) being enrolled in a technical or professional degree program between the first and fifth year, and (3) studying at an educational institution located in the Araucanía Region in southern Chile. Table 1 presents the sociodemographic characteristics of the sample. Table 1 Sociodemographic characteristics of the sample, separated by gender. Characteristic Male Female Total Group comparison n % n % n % Participants 224 35.9 400 64.1 624 100 Age ( M ; SD ) 20.81 (3.19) 20.23 (3.44) 20.44 (3.36) t (491.01) = -2.14, p = 0.033 Socioeconomic status Low 5 2.3 23 5.9 28 4.6 χ 2 (2) = 4.18, p = 0.124 Middle 190 87.6 326 83.6 516 85.0 High 22 10.1 41 10.5 63 10.4 Ethnicity Does not identify 169 75.4 307 76.8 476 76.3 χ 2 (1) = 0.14, p = 0.713 It identifies 55 24.6 93 23.3 148 23.7 Type of institution attended University 124 62.0 301 88.8 425 78.8 χ 2 (2) = 58.55, p < 0.001 Technical Center 50 25.0 17 5.0 67 12.4 Professional Institute 26 13.0 21 6.2 47 8.7 Employment situation It is not employed 143 63.8 277 69.3 420 67.3 χ 2 (1) = 1.91, p = 0.167 It is employed 81 36.2 123 30.8 204 32.7 Credit card It does not use 176 78.6 330 82.5 506 81.1 χ 2 (1) = 1.45, p = 0.229 It uses 48 21.4 70 17.5 118 18.9 Income ( M ; SD )* 1.34 (1.39) 1.43 (1.62) 1.40 (1.54) t (526) = 0.67, p = 0.504 Debt ( M ; SD )* 0.11 (0.50) 0.18 (1.17) 0.16 (0.98) t (622) = 0.91, p = 0.362 Note: n = absolute frequency; % = percentage; M = mean; SD = standard deviation; p = significance level. *Income and debt per each 100,000 Chilean pesos (CLP). Measuring Instruments To assess financial well-being, the Personal Financial Wellness Scale (Prawitz et al., 2006) was used. For this study, the adaptation began from the Spanish version employed by (Lobos et al., 2021) in a sample of Ecuadorian health workers. This version served as the basis for a cultural and linguistic adaptation to the Chilean context, which included modifications to vocabulary to ensure compatibility with Chilean Spanish and adjustments to reflect the local currency, as recommended in instrument adaptation guidelines (Cruchinho et al., 2024; Fenn et al., 2020). In addition, the original 10-point response format was shortened to a 6-point scale ranging from 1 (not at all healthy) to 6 (completely healthy), in order to assess perceived health level using more concise and manageable response options (Lee & Paek, 2014; Revilla et al., 2013). Content validity was established through the review of three academic experts in financial behavior and psychometrics, who independently evaluated the relevance, clarity, and cultural adequacy of the items. The expert judgments demonstrated high agreement. Following this, ten cognitive interviews were conducted with individuals from the target population, including five participants who self-identified as Mapuche and five as non-Mapuche, to assess comprehension and semantic clarity of the items. Based on their feedback, minor adjustments were made to improve wording. The final instrument retained the original eight items, which assess perceived financial control, security, and stability. The overall score was calculated by averaging the item responses. Previous research has consistently supported a unidimensional factor structure of the PFWS, with high internal consistency (α > .85) reported across studies (Mahdzan et al., 2019). Table 2 presents the means and standard deviations found in the current sample as well as the structure found in the present study. Statistical Analyses Data base was downloaded from the QuestionPro platform and processed using RStudio version 2023.12.0 + 369 (Posit team, 2023). Primary analyses were conducted with the esemComp package (Silvestrin & de Beer, 2022). Table 2 Personal Financial Wellness Scale Items M SD Financial Distress (ω = 0.759, IC 95% [0.725–0.793]) 1. What do you think is your current level of financial stress today? 3.679 1.555 4. How often do you worry about being able to cover normal monthly living expenses? 4.048 1.557 6. How often would you like to go out to eat, go to the movies, or do something else and you don’t because you can’t afford it? 3.726 1.623 7. How often do you manage on your own financially to get through the day? 3.196 1.581 8. How stressed do you feel about your personal finances in general? 3.752 1.677 Financial Well-Being (ω = 0.795, IC 95% [0.765–0.825]) 2. How satisfied are you with your current financial situation? 2.981 1.423 3. How do you feel about your current financial situation? 3.095 1.420 5. How confident are you that you could find the money to pay for a financial emergency costing around $ 800,000 CLP? 2.377 1.575 Note: All items use a five-point response scale; M = Mean; SD = Standard Deviation. Prior to analysis, multivariate outliers were excluded based on Mahalanobis distance, and descriptive analyses were performed to characterize the sample. To determine the factorial structure of the scale, a series of Exploratory Structural Equation Models (ESEM; (Morin, 2023) were estimated using the ESEM-within-CFA approach (Marsh et al., 2020) applying GeominQ rotation and the Weighted Least Squares Mean and Variance Adjusted Estimator (WLSMV), which is recommended for ordinal data (DiStefano & Morgan, 2014; Park, 2023). Model fit was evaluated using the following criteria: (a) Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI), with values ≥ .90 indicating acceptable fit and ≥ .95 indicating excellent fit; (b) Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR), with values ≤ 0.08 indicating acceptable fit and ≤ .06 indicating excellent fit (Marsh et al., 2005; Shi et al., 2019). The following models were compared to assess the factorial structure of the scale, and the best-fitting model was selected based on recommendations from the ESEM literature (Morin, 2023; Morin et al., 2016): (a) a unidimensional model to test for irrelevant multidimensionality; (b) the original two-factor model estimated using a confirmatory factor analysis (CFA) approach; and (c) a two-factor ESEM model to evaluate the potential presence of meaningful cross-loadings. Subsequently, a multigroup analysis was conducted based on the selected factorial solution to test for measurement invariance across gender, ethnicity, and employment situation. A sequence of increasingly restrictive models was estimated: (a) configural invariance, allowing item loadings to vary freely across groups but retaining the same factor structure; (b) metric invariance, constraining factor loadings to be equal across groups; (c) scalar invariance, constraining item intercepts; and (d) residual invariance, constraining residual variances (Widaman & Helm, 2023). Model fit at each level of invariance was compared to the configural model. Where full invariance was not achieved, equality constraints were relaxed based on the Wald test (Kline, 2023). Model comparisons were evaluated using changes in fit indices, with the following thresholds used to detect non-invariance (ΔCFI and ΔTLI < .010, ΔRMSEA y ΔSRMR < .015; (Chen, 2007; Morin et al., 2016). Finally, internal consistency reliability was assessed using McDonald’s Omega coefficient with a 95% confidence interval (Hayes & Coutts, 2020), computed via the semTools package (Jorgensen et al., 2022). Results Regarding the structure of the scale, the two-factor correlated model estimated using the ESEM approach (Model 1) showed excellent fit indices and well-defined factors based on their primary loadings (λFactor 1 = 0.464 to 0.823; λFactor 2 = 0.486 to 0.887), with weak cross-loadings (|λFactor 1| = 0.025 to 0.027; |λFactor 2| = 0.069 to 0.190) and low covariances between the factors (r = -0.239; p < 0.001). On the other hand, the one-factor model (Model 2) did not show acceptable fit, which supports the rejection of the unidimensionality of the construct. Additionally, although the two-factor model estimated using the CFA approach (Model 3) showed acceptable fit indices, its overall fit was substantially poorer compared to Model 1 (ΔCFI = -0.029; ΔTLI = -0.046; ΔRMSEA = + 0.038). Therefore, the ESEM solution appears to be optimal, as it allows for the presence of cross-loadings (see Tables 3 and 4 ). This factorial structure showed adequate internal consistency indices for both factors (ωdistress = 0.759, CI 95% [0.725–0.793]; ωwell-being = 0.795, CI 95% [0.765–0.825]). Table 3 Goodness-of-fit indices for alternative models of the factorial structure. Model χ 2 ( df ) CFI TLI RMSEA (IC 90%) SRMR ΔCFI ΔTLI ΔRMSEA ΔSRMR M1 32.455 (13) 1.000 1.004 0.028 (0.016–0.041) 0.024 - - - - M2 343.558 (20) 0.732 0.625 0.180 (0.163–0.197) 0.149 -0.268 -0.379 + 0.152 + 0.125 M3 100.220 (19) 0.971 0.958 0.066 (0.054–0.079) 0.058 -0.029 -0.046 + 0.038 + 0.034 Note: M1 = Two-factor model with ESEM approach; M2 = One-factor model with CFA approach; M3 = Two-factor model with CFA approach; Δ = change in goodness-of-fit index. Table 4 Factor loadings for the estimated models. Items M1 M3 λ distress λ well−being δ λ distress λ well−being δ Item 1 0.653 -0.069 0.547 0.689 0.526 Item 2 -0.025 0.887 0.202 0.898 0.193 Item 3 -0.027 0.854 0.258 0.868 0.247 Item 4 0.621 0.190 0.635 0.498 0.752 Item 5 -0.027 0.486 0.756 0.486 0.764 Item 6 0.464 -0.130 0.739 0.527 0.723 Item 7 0.531 0.128 0.735 0.452 0.796 Item 8 0.823 -0.081 0.285 0.883 0.220 Note: λ = standardized factor loadings; δ = residual variances. Measurement invariance models by gender, ethnicity, and employment status were subsequently estimated, all based on the ESEM solution (see Table 5 ). Table 5 Goodness-of-fit indices for measurement invariance models by gender, ethnicity, and employment status . Model χ 2 ( df ) p CFI TLI RMSEA (90% IC) SRMR ΔCFI ΔTLI ΔRMSEA ΔSRMR Measurement invariance by gender Configural 42.350 (26) 0.023 1.000 1.019 0.026 (0.010–0.040) 0.024 - - - - Metric 46.237 (38) 0.169 1.000 1.015 0.021 (0.000–0.040) 0.032 0.000 -0.004 -0.005 + 0.008 Scalar 53.631 (44) 0.152 1.000 1.014 0.021 (0.000–0.039) 0.034 0.000 -0.005 0.005 + 0.010 Residual 61.946 (52) 0.163 1.000 1.013 0.021 (0.000–0.038) 0.037 0.000 -0.006 0.005 + 0.013 Measurement invariance by ethnic Configural 39.829 (26) 0.041 1.000 1.020 0.024 (0.005–0.038) 0.023 - - - - Metric 53.303 (38) 0.051 1.000 1.009 0.030 (0.000–0.047) 0.035 0.000 -0.011 + 0.006 + 0.012 Partial metric * 42.489 (37) 0.246 1.000 1.016 0.018 (0.000–0.038) 0.030 0.000 -0.004 -0.006 + 0.007 Measurement invariance by employment status Configural 37.369 (26) 0.069 1.000 1.020 0.023 (0.000–0.039) 0.023 - - - - Metric 36.242 (38) 0.551 1.000 1.020 0.000 (0.000–0.031) 0.027 0.000 0.000 -0.023 + 0.004 Scalar 75.623 (44) 0.002 0.999 0.999 0.041 (0.024–0.056) 0.040 -0.001 -0.021 + 0.018 + 0.017 Partial metric ** 51.744 (42) 0.144 1.000 1.012 0.023 (0.000–0.042) 0.033 0.000 -0.008 0.000 + 0.010 Note: * = Equality constraints on the factor loading of item 7 were released. ** = Equality constraints on the intercepts of items 4 and 7 were released The analysis began with the configural invariance model, used as the baseline for comparison. In this model, no equality constraints are imposed on the parameters, aside from maintaining the same measurement structure across groups. In the case of gender invariance, the metric, scalar, and residual models all demonstrated acceptable fit indices and did not show substantial deterioration in comparison to the configural model. These results indicate that the items hold the same meaning for men and women, the factor means can be validly compared across groups, and the indicators are measured with the same level of precision. For the ethnicity invariance analysis, although the metric invariance model exhibited excellent fit indices, its overall fit deteriorated substantially compared to the configural model. The Lagrange Multiplier test indicated that releasing item 7 would significantly improve model fit. Consequently, a partial metric invariance model was estimated by removing the equality constraint on item 7. This adjusted model showed a considerable improvement in fit indices, with no substantial differences from the configural model. These results suggest that, for the most part, the items have equivalent meaning for individuals who identify as belonging to an ethnic group and those who do not, except for item 7, whose relative contribution to the factor varies by ethnic background. Finally, regarding measurement invariance by employment status, the metric invariance model demonstrated excellent fit and did not differ substantially from the configural model, indicating that all items are interpreted similarly by individuals with and without paid employment. However, although the scalar invariance model also showed excellent fit indices, its overall fit deteriorated considerably relative to the configural model. Table 6 shows the factor loadings for the invariance models by gender, ethnicity, and employment status. Table 6 Factor loadings for the invariance models by gender, ethnicity, and employment status Item Gender Male Female λ distress λ well−being η δ λ distress λ well−being η δ 1 0.645 -0.069 2.318 0.562 0.657 -0.072 2.266 0.538 2 -0.026 0.865 2.161 0.243 -0.026 0.877 2.049 0.219 3 -0.023 0.864 2.259 0.245 -0.023 0.876 2.143 0.221 4 0.612 0.187 2.532 0.634 0.634 0.199 2.518 0.627 5 -0.021 0.473 1.528 0.772 -0.022 0.498 1.502 0.746 6 0.454 -0.128 2.260 0.756 0.466 -0.134 2.224 0.731 7 0.522 0.124 1.957 0.737 0.541 0.132 1.946 0.729 8 0.811 -0.083 2.179 0.311 0.816 -0.086 2.105 0.290 Item Ethnicity Does not identify It identifies λ distress λ well−being η δ λ distress λ well−being η δ 1 0.677 -0.056 2.304 0.520 0.599 -0.063 2.598 0.622 2 -0.025 0.866 2.132 0.239 -0.021 0.926 1.985 0.135 3 -0.027 0.834 2.198 0.292 -0.024 0.919 2.129 0.146 4 0.639 0.192 2.539 0.617 0.578 0.221 2.813 0.670 5 -0.025 0.487 1.509 0.756 -0.021 0.515 1.505 0.729 6 0.470 -0.127 2.247 0.733 0.405 -0.140 2.473 0.793 7 0.493 0.130 2.001 0.773 0.677 0.137 2.093 0.561 8 0.845 -0.081 2.190 0.245 0.717 -0.088 2.412 0.452 Item Employment situation Not employed Employed λ distress λ well−being η δ λ distress λ well−being η δ 1 0.671 -0.068 2.304 0.527 0.617 -0.061 2.350 0.592 2 -0.024 0.889 2.014 0.201 -0.025 0.895 2.298 0.184 3 -0.028 0.877 2.161 0.220 -0.026 0.800 2.232 0.346 4 0.592 0.187 2.426 0.659 0.596 0.185 2.995 0.681 5 -0.047 0.511 1.535 0.727 -0.041 0.435 1.483 0.797 6 0.491 -0.128 2.256 0.717 0.438 -0.111 2.233 0.764 7 0.528 0.126 1.929 0.732 0.463 0.108 2.224 0.806 8 0.830 -0.080 2.122 0.278 0.807 -0.076 2.288 0.304 The Lagrange Multiplier test suggested releasing the intercept constraints for items 4 and 7, and the model was subsequently re-estimated with these parameters freed. This partial scalar invariance model maintained acceptable fit indices without substantial differences from the configural model. These findings indicate that global mean comparisons across employment groups are not appropriate, due to intercept differences in items 4 and 7. Therefore, observed sum scores should not be used for group comparisons; instead, latent variable estimates are recommended to account for such measurement bias. Discussion This study provides empirical support for the psychometric validity of the Personal Financial Wellness Scale (PFWS) among Chilean emerging adults, in line with the growing recognition of financial well-being as a central component of quality of life (Mathew et al., 2022). The findings reinforce the notion that personal financial wellness is a multidimensional construct encompassing not only economic sufficiency but also individuals’ perceived ability to meet obligations, feel secure about the future, and make choices aligned with their life goals (Tahir & Ahmed, 2021). The scale demonstrated a two-factor structure (financial distress and financial well-being) that reflects the emotional and cognitive dimensions associated with managing personal finances. While the original purpose of authors such as Mathew et al. (2022) was not to address measurement models, their conceptualization supports the importance of assessing both negative and positive financial experiences. This structure is especially pertinent in the Chilean context, where more than half of households are in debt and financial insecurity is pervasive (Banco Central de Chile, 2021), particularly among young people (INJUV, 2020). The internal consistency indices and model fit support the PFWS as a reliable tool to capture these financial realities. The application of Exploratory Structural Equation Modeling (ESEM) offered a more flexible representation of the scale’s factorial structure, enabling modest cross-loadings that may reflect overlapping perceptions of control, sacrifice, and satisfaction, components that are central to subjective financial well-being (Bialowolski et al., 2021; Kumar et al., 2023). The two-dimensional solution supports the conceptual distinction between feeling overwhelmed by financial pressures and maintaining confidence in one’s financial management abilities. Consistent with findings from national reports and recent studies (Castellanos-Alvarenga & Denegri-Coria, 2024; Sepúlveda-Maldonado et al., 2022), descriptive analyses showed higher financial distress among participants, suggesting that debt and economic uncertainty are internalized as emotional discomfort, insecurity, and limited autonomy. These psychological consequences, including stress, anxiety, and depressive symptoms (Guan et al., 2022; Ryu & Fan, 2023), underscore the importance of financial wellness not only as an economic indicator but as a relevant factor in mental health promotion during emerging adulthood (Barrera-Herrera et al., 2019; Meier, 2020). Importantly, the study tested the measurement invariance of the PFWS across gender, ethnicity, and employment status. Although full scalar invariance was not achieved in all comparisons, the partial invariance observed still supports the scale’s utility for cross-group comparisons, with some limitations. In particular, the need to relax constraints on items related to daily management and sacrifice may reflect different lived financial experiences among subgroups, especially among women, unemployed youth, and Mapuche participants, who are disproportionately affected by systemic inequities (Delgado et al., 2025; Baquedano-Rodríguez et al., 2025; Castillo et al., 2022). The PFWS’s acceptable performance across these sociodemographic groups is especially relevant in La Araucanía, a region with a high proportion of Indigenous population and persistent structural and cultural inequalities. The ability to assess financial wellness fairly across such groups is essential to advancing equitable and context-sensitive interventions, as recommended by prior research (Araujo, 2019; OECD, 2022). Taken together, the findings suggest that the PFWS is a psychometrically sound instrument for evaluating personal financial wellness in emerging adults in Chile. Its use can contribute not only to understanding economic behavior but also to the early identification of psychological risk factors associated with financial distress. Given the increasing visibility of student debt, informal credit, and labor precariousness in Chile (Marambio-Tapia, 2021; CMF, 2025), systematic monitoring of financial wellness may inform targeted educational and policy strategies. Future research should include longitudinal designs to assess changes over time and examine how contextual factors such as inflation, educational costs, and employment instability influence financial wellness. Additionally, predictive analyses may help determine the role of financial well-being in academic performance, mental health outcomes, and interpersonal functioning, further validating the PFWS in diverse real-world contexts. Declarations Funding: Funding received by FONDECYT Project No. (ANONYMIZED) and by the National Doctoral Scholarship Program Grant No. (ANONYMIZED) of the National Agency for Research and Development of Chile (ANID). Author Contribution All authors have made substantial contributions to the work, whether through the conception or design of the study, the acquisition, analysis, or interpretation of data, or the development of new software used in the research. They have also participated in drafting the manuscript or revising it critically to ensure the inclusion of significant intellectual content. All authors have reviewed and approved the final version to be published, and they collectively accept responsibility for every aspect of the work, ensuring that any questions regarding the accuracy or integrity of any part are properly investigated and resolved. Data Availability The dataset will be made available upon request via email to the corresponding author. References Araujo, K. 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American Psychological Association. https://doi.org/10.1037/0000320-017 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. 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Beyond economic sufficiency, it encompasses perceptions of control (Bialowolski, Weziak-Bialowolska, \u0026amp; McNeely, 2021), autonomy (Kumar et al., 2023), and mental health regarding financial matters (Ryu \u0026amp; Fan, 2023), influencing relational stability (Kelley et al., 2023), and overall life satisfaction (Foong et al., 2021).\u003c/p\u003e\u003cp\u003eThis multidimensional view of financial well-being becomes particularly relevant in contexts where high levels of debt and financial insecurity are prevalent (Simonse et al., 2024).\u003c/p\u003e\u003cp\u003eIn Chile, over 57% of households are in debt and many individuals rely on consumer credit from retail and banking institutions (Banco Central Chile, 2021). Moreover, 30.1% of young adults between 18 and 29 years report being heavily indebted, a figure that increases among women and younger adults (Instituto Nacional de la Juventud [INJUV], 2020). The growing acceptance of credit use and consumer debt, particularly among emerging adults, has generated increasing concern about their susceptibility to financial stress and psychological vulnerability (Castellanos-Alvarenga \u0026amp; Denegri-Coria, 2024). While financial stress has traditionally been associated with external conditions like income instability or inflation (Friedline et al., 2021), it also reflects subjective perceptions of financial inadequacy and insecurity (Kasal, 2023).\u003c/p\u003e\u003cp\u003eThe consequences of poor financial well-being include increased financial stress (Tran et al., 2025), reduced academic and job performance (Rosso et al., 2024), and lower subjective well-being (Rahman et al., 2021). Poor personal financial wellness has been consistently associated with adverse psychological outcomes, including higher levels of stress, anxiety, and depressive symptoms (Bialowolski et al., 2021; Guan et al., 2022). It may also impair sleep quality (Du et al., 2021), increases substance use (Nigatu et al., 2024), and negatively affects the quality of interpersonal relationships (Peetz et al., 2024). These associations are particularly relevant during emerging adulthood, a stage characterized by greater economic instability, increased vulnerability to mental health problems, and the pursuit of financial independence(Barrera-Herrera et al., 2019). This life period, situated between adolescence and full adulthood, involves significant developmental transitions that often intensify financial pressures and emotional demands (Felinto et al., 2020; Meier, 2020).\u003c/p\u003e\u003cp\u003eTherefore, accurate assessment of personal financial wellness in young adults is not only important for financial behavior research, but also for identifying individuals at risk of psychological comorbidities and for informing early preventive strategies. To address this, several studies have highlighted the need for accurate and context-sensitive instruments to assess personal financial well-being in emerging adults (Castellanos-Alvarenga et al., 2022; She et al., 2023; Vosylis \u0026amp; Klimstra, 2020).\u003c/p\u003e\u003cp\u003eThe Personal Financial Wellness Scale (PFWS) is one of the most widely used instruments to measure financial well-being as a multidimensional construct, incorporating both objective and subjective aspects (Sabri \u0026amp; Aw, 2020). It has been validated in multiple countries and populations, but evidence on its psychometric properties in Latin American youth is scarce (Buabang et al., 2022). Moreover, this gap is particularly critical in Chile, where structural inequalities intersect with financial instability (Araujo, 2019), which is reflected in high levels of student debt (Sep\u0026uacute;lveda-Maldonado et al., 2022), and precarious job conditions (Comisi\u0026oacute;n para el Mercado Financiero [CMF], 2025; Marambio-Tapia, 2021). These socioeconomic dynamics underscore the need for a rigorous evaluation of the scale\u0026rsquo;s validity and measurement properties within the Chilean context.\u003c/p\u003e\u003cp\u003eBeyond establishing internal consistency and factorial structure, it is essential to test the measurement invariance of the PFWS across key sociodemographic groups, particularly gender, ethnicity, and employment status. This issue is especially relevant in the Region of La Araucan\u0026iacute;a, where approximately 32.82% of the population self-identifies as Mapuche (Instituto Nacional de Estad\u0026iacute;stica, 2018). The persistence of structural and cultural inequalities affecting Mapuche communities (Castillo et al., 2022; Delgado et al., 2025) raises the need to determine whether the PFWS functions equivalently across ethnic groups. Likewise, gender and employment status are key variables in the Chilean socio-economic landscape, as women and unemployed youth are disproportionately affected by financial vulnerability (Baquedano-Rodr\u0026iacute;guez et al., 2025; OECD, 2022)\u003c/p\u003e\u003cp\u003eWithout establishing invariance, group comparisons may yield misleading conclusions, undermining both the interpretability of research findings and the equity of interventions based on such data. Addressing this gap, the present study aims to examine the factorial validity of the Personal Financial Wellness Scale (PFWS) and to evaluate its measurement invariance across gender, ethnicity, and employment status in a sample of emerging adults from the Region of La Araucan\u0026iacute;a, Chile.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch Design\u003c/h2\u003e\u003cp\u003eThe study utilized a cross-sectional design with a correlational and explanatory scope, applying a non-probabilistic purposive sampling strategy. Data were gathered through an online questionnaire administered to higher education students in the Araucan\u0026iacute;a Region, Chile, enabling the assessment of the factorial structure, internal consistency, and measurement invariance of the Personal Financial Wellness Scale across gender, ethnicity, and employment status at a single point in time.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e The research protocol was reviewed and approved by a university-based Research Ethics Committee in Chile, in accordance with the principles outlined in the Declaration of Helsinki. Participants were invited to complete the study instruments via an online survey platform. The estimated response time was approximately 15 minutes. All participants provided digital informed consent prior to beginning the questionnaire. Participation was entirely voluntary, and no financial or material incentives were offered. Anonymity and confidentiality were assured throughout the data collection process.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe study was conducted using a cross-sectional design with a correlational and explanatory approach, based on a non-probabilistic purposive sampling method (Creswell, 2014). The sample size was calculated a priori to ensure statistical power and reliable parameter estimates for structural equation modeling analyses, with Exploratory Structural Equation Modeling (ESEM) approach (Soper, 2024). ESEM is an advanced statistical technique that integrates features of exploratory factor analysis within a structural equation modeling framework, allowing for more flexible modeling of latent constructs by permitting cross-loadings (Morin, 2023).\u003c/p\u003e\u003cp\u003eThe final sample consisted of 624 higher education students, of whom 63.1% identified as female. The average age was 20.44 years (SD\u0026thinsp;=\u0026thinsp;3.35). Inclusion criteria were: (1) being between 18 and 29 years old, (2) being enrolled in a technical or professional degree program between the first and fifth year, and (3) studying at an educational institution located in the Araucan\u0026iacute;a Region in southern Chile. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the sociodemographic characteristics of the sample.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic characteristics of the sample, separated by gender.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGroup comparison\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (\u003cem\u003eM\u003c/em\u003e; \u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e20.81 (3.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e20.23 (3.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e20.44 (3.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e(491.01) = -2.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eSocioeconomic status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(2)\u0026thinsp;=\u0026thinsp;4.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e83.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e85.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoes not identify\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e76.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.713\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt identifies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eType of institution attended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e78.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(2)\u0026thinsp;=\u0026thinsp;58.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical Center\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfessional Institute\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eEmployment situation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt is not employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;1.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt is employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eCredit card\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt does not use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e81.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;1.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt uses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIncome (\u003cem\u003eM\u003c/em\u003e; \u003cem\u003eSD\u003c/em\u003e)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.34 (1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1.43 (1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1.40 (1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e(526)\u0026thinsp;=\u0026thinsp;0.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDebt (\u003cem\u003eM\u003c/em\u003e; \u003cem\u003eSD\u003c/em\u003e)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.11 (0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.18 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e0.16 (0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e(622)\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.362\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: n\u0026thinsp;=\u0026thinsp;absolute frequency; % = percentage; M\u0026thinsp;=\u0026thinsp;mean; SD\u0026thinsp;=\u0026thinsp;standard deviation; p\u0026thinsp;=\u0026thinsp;significance level. *Income and debt per each 100,000 Chilean pesos (CLP).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMeasuring Instruments\u003c/h3\u003e\n\u003cp\u003eTo assess financial well-being, the Personal Financial Wellness Scale (Prawitz et al., 2006) was used. For this study, the adaptation began from the Spanish version employed by (Lobos et al., 2021) in a sample of Ecuadorian health workers. This version served as the basis for a cultural and linguistic adaptation to the Chilean context, which included modifications to vocabulary to ensure compatibility with Chilean Spanish and adjustments to reflect the local currency, as recommended in instrument adaptation guidelines (Cruchinho et al., 2024; Fenn et al., 2020).\u003c/p\u003e\u003cp\u003eIn addition, the original 10-point response format was shortened to a 6-point scale ranging from 1 (not at all healthy) to 6 (completely healthy), in order to assess perceived health level using more concise and manageable response options (Lee \u0026amp; Paek, 2014; Revilla et al., 2013). Content validity was established through the review of three academic experts in financial behavior and psychometrics, who independently evaluated the relevance, clarity, and cultural adequacy of the items. The expert judgments demonstrated high agreement. Following this, ten cognitive interviews were conducted with individuals from the target population, including five participants who self-identified as Mapuche and five as non-Mapuche, to assess comprehension and semantic clarity of the items. Based on their feedback, minor adjustments were made to improve wording. The final instrument retained the original eight items, which assess perceived financial control, security, and stability. The overall score was calculated by averaging the item responses.\u003c/p\u003e\u003cp\u003ePrevious research has consistently supported a unidimensional factor structure of the PFWS, with high internal consistency (α\u0026thinsp;\u0026gt;\u0026thinsp;.85) reported across studies (Mahdzan et al., 2019). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the means and standard deviations found in the current sample as well as the structure found in the present study.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eData base was downloaded from the QuestionPro platform and processed using RStudio version 2023.12.0\u0026thinsp;+\u0026thinsp;369 (Posit team, 2023). Primary analyses were conducted with the esemComp package (Silvestrin \u0026amp; de Beer, 2022).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePersonal Financial Wellness Scale\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinancial Distress\u003c/b\u003e (ω\u0026thinsp;=\u0026thinsp;0.759, IC 95% [0.725\u0026ndash;0.793])\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. What do you think is your current level of financial stress today?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.555\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. How often do you worry about being able to cover normal monthly living expenses?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. How often would you like to go out to eat, go to the movies, or do something else and you don\u0026rsquo;t because you can\u0026rsquo;t afford it?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7. How often do you manage on your own financially to get through the day?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.581\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8. How stressed do you feel about your personal finances in general?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFinancial Well-Being\u003c/b\u003e (ω\u0026thinsp;=\u0026thinsp;0.795, IC 95% [0.765\u0026ndash;0.825])\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. How satisfied are you with your current financial situation?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.423\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. How do you feel about your current financial situation?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. How confident are you that you could find the money to pay for a financial emergency costing around \u003cspan\u003e$\u003c/span\u003e800,000 CLP?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.575\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: All items use a five-point response scale; \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Mean; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standard Deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePrior to analysis, multivariate outliers were excluded based on Mahalanobis distance, and descriptive analyses were performed to characterize the sample. To determine the factorial structure of the scale, a series of Exploratory Structural Equation Models (ESEM; (Morin, 2023) were estimated using the \u003cem\u003eESEM-within-CFA\u003c/em\u003e approach (Marsh et al., 2020) applying \u003cem\u003eGeominQ\u003c/em\u003e rotation and the Weighted Least Squares Mean and Variance Adjusted Estimator (WLSMV), which is recommended for ordinal data (DiStefano \u0026amp; Morgan, 2014; Park, 2023). Model fit was evaluated using the following criteria: (a) Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI), with values\u0026thinsp;\u0026ge;\u0026thinsp;.90 indicating acceptable fit and \u0026ge;\u0026thinsp;.95 indicating excellent fit; (b) Root Mean Square Error of Approximation (RMSEA) and Standardized Root Mean Square Residual (SRMR), with values\u0026thinsp;\u0026le;\u0026thinsp;0.08 indicating acceptable fit and \u0026le;\u0026thinsp;.06 indicating excellent fit (Marsh et al., 2005; Shi et al., 2019).\u003c/p\u003e\u003cp\u003eThe following models were compared to assess the factorial structure of the scale, and the best-fitting model was selected based on recommendations from the ESEM literature (Morin, 2023; Morin et al., 2016): (a) a unidimensional model to test for irrelevant multidimensionality; (b) the original two-factor model estimated using a confirmatory factor analysis (CFA) approach; and (c) a two-factor ESEM model to evaluate the potential presence of meaningful cross-loadings.\u003c/p\u003e\u003cp\u003eSubsequently, a multigroup analysis was conducted based on the selected factorial solution to test for measurement invariance across gender, ethnicity, and employment situation. A sequence of increasingly restrictive models was estimated: (a) configural invariance, allowing item loadings to vary freely across groups but retaining the same factor structure; (b) metric invariance, constraining factor loadings to be equal across groups; (c) scalar invariance, constraining item intercepts; and (d) residual invariance, constraining residual variances (Widaman \u0026amp; Helm, 2023). Model fit at each level of invariance was compared to the configural model.\u003c/p\u003e\u003cp\u003eWhere full invariance was not achieved, equality constraints were relaxed based on the Wald test (Kline, 2023). Model comparisons were evaluated using changes in fit indices, with the following thresholds used to detect non-invariance (ΔCFI and ΔTLI\u0026thinsp;\u0026lt;\u0026thinsp;.010, ΔRMSEA y ΔSRMR\u0026thinsp;\u0026lt;\u0026thinsp;.015; (Chen, 2007; Morin et al., 2016). Finally, internal consistency reliability was assessed using McDonald\u0026rsquo;s Omega coefficient with a 95% confidence interval (Hayes \u0026amp; Coutts, 2020), computed via the \u003cem\u003esemTools\u003c/em\u003e package (Jorgensen et al., 2022).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eRegarding the structure of the scale, the two-factor correlated model estimated using the ESEM approach (Model 1) showed excellent fit indices and well-defined factors based on their primary loadings (λFactor 1\u0026thinsp;=\u0026thinsp;0.464 to 0.823; λFactor 2\u0026thinsp;=\u0026thinsp;0.486 to 0.887), with weak cross-loadings (|λFactor 1| = 0.025 to 0.027; |λFactor 2| = 0.069 to 0.190) and low covariances between the factors (r = -0.239; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). On the other hand, the one-factor model (Model 2) did not show acceptable fit, which supports the rejection of the unidimensionality of the construct. Additionally, although the two-factor model estimated using the CFA approach (Model 3) showed acceptable fit indices, its overall fit was substantially poorer compared to Model 1 (ΔCFI = -0.029; ΔTLI = -0.046; ΔRMSEA\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.038). Therefore, the ESEM solution appears to be optimal, as it allows for the presence of cross-loadings (see Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This factorial structure showed adequate internal consistency indices for both factors (ωdistress\u0026thinsp;=\u0026thinsp;0.759, CI 95% [0.725\u0026ndash;0.793]; ωwell-being =\u0026thinsp;0.795, CI 95% [0.765\u0026ndash;0.825]).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eGoodness-of-fit indices for alternative models of the factorial structure.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e(\u003cem\u003edf\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSEA (IC 90%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eΔCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eΔTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eΔRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eΔSRMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.455 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.028 (0.016\u0026ndash;0.041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e343.558 (20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.180 (0.163\u0026ndash;0.197)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u0026thinsp;0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u0026thinsp;0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.220 (19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.066 (0.054\u0026ndash;0.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u0026thinsp;0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u0026thinsp;0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: \u003cem\u003eM1\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Two-factor model with ESEM approach; \u003cem\u003eM2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;One-factor model with CFA approach; \u003cem\u003eM3\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Two-factor model with CFA approach; \u003cem\u003eΔ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;change in goodness-of-fit index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eFactor loadings for the estimated models.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eM3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eλ\u003csub\u003edistress\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eλ\u003csub\u003ewell\u0026minus;being\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eδ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eλ\u003csub\u003edistress\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eλ\u003csub\u003ewell\u0026minus;being\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eδ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.653\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.526\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.887\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.854\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.621\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.486\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.464\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.531\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.823\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: λ\u0026thinsp;=\u0026thinsp;standardized factor loadings; δ\u0026thinsp;=\u0026thinsp;residual variances.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMeasurement invariance models by gender, ethnicity, and employment status were subsequently estimated, all based on the ESEM solution (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eGoodness-of-fit indices for measurement invariance models by gender, ethnicity, and employment status\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e(\u003cem\u003edf\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRMSEA (90% IC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eΔCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eΔTLI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eΔRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eΔSRMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eMeasurement invariance by gender\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfigural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.350 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.026 (0.010\u0026ndash;0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.237 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021 (0.000\u0026ndash;0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u0026thinsp;0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScalar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.631 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021 (0.000\u0026ndash;0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u0026thinsp;0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.946 (52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021 (0.000\u0026ndash;0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u0026thinsp;0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eMeasurement invariance by ethnic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfigural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.829 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.024 (0.005\u0026ndash;0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.303 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.030 (0.000\u0026ndash;0.047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u0026thinsp;0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u0026thinsp;0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartial metric *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.489 (37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.018 (0.000\u0026ndash;0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u0026thinsp;0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eMeasurement invariance by employment status\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfigural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.369 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023 (0.000\u0026ndash;0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.242 (38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000 (0.000\u0026ndash;0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u0026thinsp;0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScalar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.623 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.041 (0.024\u0026ndash;0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u0026thinsp;0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u0026thinsp;0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartial metric **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.744 (42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023 (0.000\u0026ndash;0.042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u0026thinsp;0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: * = Equality constraints on the factor loading of item 7 were released. ** = Equality constraints on the intercepts of items 4 and 7 were released\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe analysis began with the configural invariance model, used as the baseline for comparison. In this model, no equality constraints are imposed on the parameters, aside from maintaining the same measurement structure across groups. In the case of gender invariance, the metric, scalar, and residual models all demonstrated acceptable fit indices and did not show substantial deterioration in comparison to the configural model. These results indicate that the items hold the same meaning for men and women, the factor means can be validly compared across groups, and the indicators are measured with the same level of precision.\u003c/p\u003e\u003cp\u003eFor the ethnicity invariance analysis, although the metric invariance model exhibited excellent fit indices, its overall fit deteriorated substantially compared to the configural model. The Lagrange Multiplier test indicated that releasing item 7 would significantly improve model fit. Consequently, a partial metric invariance model was estimated by removing the equality constraint on item 7. This adjusted model showed a considerable improvement in fit indices, with no substantial differences from the configural model. These results suggest that, for the most part, the items have equivalent meaning for individuals who identify as belonging to an ethnic group and those who do not, except for item 7, whose relative contribution to the factor varies by ethnic background.\u003c/p\u003e\u003cp\u003eFinally, regarding measurement invariance by employment status, the metric invariance model demonstrated excellent fit and did not differ substantially from the configural model, indicating that all items are interpreted similarly by individuals with and without paid employment. However, although the scalar invariance model also showed excellent fit indices, its overall fit deteriorated considerably relative to the configural model. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the factor loadings for the invariance models by gender, ethnicity, and employment status.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor loadings for the invariance models by gender, ethnicity, and employment status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eλ\u003csub\u003edistress\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eλ\u003csub\u003ewell\u0026minus;being\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eη\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eδ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eλ\u003csub\u003edistress\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eλ\u003csub\u003ewell\u0026minus;being\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eη\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eδ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.538\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eDoes not identify\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eIt identifies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eλ\u003csub\u003edistress\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eλ\u003csub\u003ewell\u0026minus;being\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eη\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eδ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eλ\u003csub\u003edistress\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eλ\u003csub\u003ewell\u0026minus;being\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eη\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eδ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003eEmployment situation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eNot employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eλ\u003csub\u003edistress\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eλ\u003csub\u003ewell\u0026minus;being\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eη\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eδ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eλ\u003csub\u003edistress\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eλ\u003csub\u003ewell\u0026minus;being\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eη\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eδ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.806\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Lagrange Multiplier test suggested releasing the intercept constraints for items 4 and 7, and the model was subsequently re-estimated with these parameters freed. This partial scalar invariance model maintained acceptable fit indices without substantial differences from the configural model. These findings indicate that global mean comparisons across employment groups are not appropriate, due to intercept differences in items 4 and 7. Therefore, observed sum scores should not be used for group comparisons; instead, latent variable estimates are recommended to account for such measurement bias.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides empirical support for the psychometric validity of the Personal Financial Wellness Scale (PFWS) among Chilean emerging adults, in line with the growing recognition of financial well-being as a central component of quality of life (Mathew et al., 2022). The findings reinforce the notion that personal financial wellness is a multidimensional construct encompassing not only economic sufficiency but also individuals\u0026rsquo; perceived ability to meet obligations, feel secure about the future, and make choices aligned with their life goals (Tahir \u0026amp; Ahmed, 2021).\u003c/p\u003e\u003cp\u003eThe scale demonstrated a two-factor structure (financial distress and financial well-being) that reflects the emotional and cognitive dimensions associated with managing personal finances. While the original purpose of authors such as Mathew et al. (2022) was not to address measurement models, their conceptualization supports the importance of assessing both negative and positive financial experiences. This structure is especially pertinent in the Chilean context, where more than half of households are in debt and financial insecurity is pervasive (Banco Central de Chile, 2021), particularly among young people (INJUV, 2020). The internal consistency indices and model fit support the PFWS as a reliable tool to capture these financial realities.\u003c/p\u003e\u003cp\u003eThe application of Exploratory Structural Equation Modeling (ESEM) offered a more flexible representation of the scale\u0026rsquo;s factorial structure, enabling modest cross-loadings that may reflect overlapping perceptions of control, sacrifice, and satisfaction, components that are central to subjective financial well-being (Bialowolski et al., 2021; Kumar et al., 2023). The two-dimensional solution supports the conceptual distinction between feeling overwhelmed by financial pressures and maintaining confidence in one\u0026rsquo;s financial management abilities.\u003c/p\u003e\u003cp\u003eConsistent with findings from national reports and recent studies (Castellanos-Alvarenga \u0026amp; Denegri-Coria, 2024; Sep\u0026uacute;lveda-Maldonado et al., 2022), descriptive analyses showed higher financial distress among participants, suggesting that debt and economic uncertainty are internalized as emotional discomfort, insecurity, and limited autonomy. These psychological consequences, including stress, anxiety, and depressive symptoms (Guan et al., 2022; Ryu \u0026amp; Fan, 2023), underscore the importance of financial wellness not only as an economic indicator but as a relevant factor in mental health promotion during emerging adulthood (Barrera-Herrera et al., 2019; Meier, 2020).\u003c/p\u003e\u003cp\u003eImportantly, the study tested the measurement invariance of the PFWS across gender, ethnicity, and employment status. Although full scalar invariance was not achieved in all comparisons, the partial invariance observed still supports the scale\u0026rsquo;s utility for cross-group comparisons, with some limitations. In particular, the need to relax constraints on items related to daily management and sacrifice may reflect different lived financial experiences among subgroups, especially among women, unemployed youth, and Mapuche participants, who are disproportionately affected by systemic inequities (Delgado et al., 2025; Baquedano-Rodr\u0026iacute;guez et al., 2025; Castillo et al., 2022).\u003c/p\u003e\u003cp\u003eThe PFWS\u0026rsquo;s acceptable performance across these sociodemographic groups is especially relevant in La Araucan\u0026iacute;a, a region with a high proportion of Indigenous population and persistent structural and cultural inequalities. The ability to assess financial wellness fairly across such groups is essential to advancing equitable and context-sensitive interventions, as recommended by prior research (Araujo, 2019; OECD, 2022).\u003c/p\u003e\u003cp\u003eTaken together, the findings suggest that the PFWS is a psychometrically sound instrument for evaluating personal financial wellness in emerging adults in Chile. Its use can contribute not only to understanding economic behavior but also to the early identification of psychological risk factors associated with financial distress. Given the increasing visibility of student debt, informal credit, and labor precariousness in Chile (Marambio-Tapia, 2021; CMF, 2025), systematic monitoring of financial wellness may inform targeted educational and policy strategies.\u003c/p\u003e\u003cp\u003eFuture research should include longitudinal designs to assess changes over time and examine how contextual factors such as inflation, educational costs, and employment instability influence financial wellness. Additionally, predictive analyses may help determine the role of financial well-being in academic performance, mental health outcomes, and interpersonal functioning, further validating the PFWS in diverse real-world contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eFunding received by FONDECYT Project No. (ANONYMIZED) and by the National Doctoral Scholarship Program Grant No. (ANONYMIZED) of the National Agency for Research and Development of Chile (ANID).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors have made substantial contributions to the work, whether through the conception or design of the study, the acquisition, analysis, or interpretation of data, or the development of new software used in the research. They have also participated in drafting the manuscript or revising it critically to ensure the inclusion of significant intellectual content. All authors have reviewed and approved the final version to be published, and they collectively accept responsibility for every aspect of the work, ensuring that any questions regarding the accuracy or integrity of any part are properly investigated and resolved.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset will be made available upon request via email to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAraujo, K. (2019). Hilos tensados para leer el octubre chileno. In \u003cem\u003ePaper Knowledge. Toward a Media History of Documents\u003c/em\u003e. Colecci\u0026oacute;n IDEA. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bit.ly/3q52UXs\u003c/span\u003e\u003cspan address=\"https://bit.ly/3q52UXs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eBanco Central Chile. 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Short-Term Longitudinal Associations Between Perceived Features of Emerging Adulthood, Financial Behaviors, and Financial Well-Being. \u003cem\u003eEmerging Adulthood\u003c/em\u003e, 1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2167696820908970\u003c/span\u003e\u003cspan address=\"10.1177/2167696820908970\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWidaman, K. F., \u0026amp; Helm, J. L. (2023). Exploratory factor analysis and confirmatory factor analysis. In H. Cooper, M. N. Coutanche, L. M. McMullen, A. T. Panter, D. Rindskopf, \u0026amp; K. J. Sher (Eds.), \u003cem\u003eAPA handbook of research methods in psychology: Data analysis and research publication\u003c/em\u003e (2nd ed., pp. 379\u0026ndash;410). American Psychological Association. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0000320-017\u003c/span\u003e\u003cspan address=\"10.1037/0000320-017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Financial well-being, Psychometrics, Emerging adulthood, Measurement invariance","lastPublishedDoi":"10.21203/rs.3.rs-8175823/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8175823/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFinancial well-being is a key determinant of quality of life, since it reflects the individual's ability to meet current and ongoing financial obligations, feel secure about their financial future, and make choices that allow enjoyment of life. This is particularly important contexts with high inequality and uncertainty such as the case of Chile where many individuals rely on consumer credit from retail and banking institutions for monthly expenses. Therefore, having an instrument the reliably measures said variable across different sociodemographic groups, such as gender, ethnicity, and employment status is essential.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study examined the psychometric properties of the Personal Financial Wellness Scale (PFWS) among Chilean emerging adults. An adapted version of the PFWS was administered to 624 university and technical students (64.1% women), aged 18 to 29 years (M\u0026thinsp;=\u0026thinsp;20.44, SD\u0026thinsp;=\u0026thinsp;3.35). Exploratory Structural Equation Modeling (ESEM) was used to test the factorial structure, and multigroup analyses evaluated measurement invariance across gender, ethnicity, and employment status.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eResults supported a two-factor model, financial distress and financial well-being, with satisfactory internal consistency and model fit. Partial measurement invariance was established across ethnicity and employment status, with changes in fit indices remaining within acceptable thresholds (ΔCFI\u0026thinsp;\u0026le;\u0026thinsp;.01, ΔRMSEA\u0026thinsp;\u0026le;\u0026thinsp;.015, ΔSRMR\u0026thinsp;\u0026le;\u0026thinsp;.030). For ethnicity, constraints on item 7 were released, and for employment status, intercepts of items 4 and 7 were relaxed. In contrast, full invariance was achieved across gender, with non-significant model comparisons (χ\u0026sup2;(26)\u0026thinsp;=\u0026thinsp;42.350, p\u0026thinsp;=\u0026thinsp;.023; χ\u0026sup2;(52)\u0026thinsp;=\u0026thinsp;61.946, p\u0026thinsp;=\u0026thinsp;.163) and stable fit indices across nested models (ΔCFI\u0026thinsp;=\u0026thinsp;0.000, ΔRMSEA\u0026thinsp;=\u0026thinsp;0.005, ΔSRMR\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings confirm that the PFWS captures essential dimensions of financial well-being in a culturally sensitive way, supporting its use as an early detection tool for financial distress and for guiding targeted interventions in socioeconomically vulnerable young adults.\u003c/p\u003e","manuscriptTitle":"Validation and measurement invariance of the Personal Financial Wellness Scale: Evidence from Chilean Young Adults by Gender, Ethnicity, and Employment Status","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 03:23:44","doi":"10.21203/rs.3.rs-8175823/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":"301f5dee-6ac8-4771-9c67-c719abf1f88e","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T10:34:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 03:23:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8175823","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8175823","identity":"rs-8175823","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}