Shall we add some meaning? Investigating useful single-item extensions to the short Warwick-Edinburgh Mental Well-Being Scale for national public health surveillance

Shall we add some meaning? 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Investigating useful single-item extensions to the short Warwick-Edinburgh Mental Well-Being Scale for national public health surveillance Caroline Cohrdes, Stephan Junker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6513849/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 Monitoring mental well-being at thenational level has become increasingly important and widespread. However, the type and scope of mental well-being measurement are highly diverse, to the disadvantage of comparability. By taking actual recommendations on how to measure well-being at the national level into account, the present study aimed to identify a multicomponent mental well-being measure suitable for regular nationwide surveillance and cross-national comparability. Methods In a sample of 2,614 participants who were equally balanced across age (18-29, 30-44, 45-59, 60-74 years), sex (male, female) and educational groups (low, moderate, high), we investigated the additional value of combining the seven-item Short Warwick-Edinburgh Well-being Scale (SWEMWBS) with the most frequently used single-item measures of mental well-being (i.e., happiness, satisfaction with life and meaning of life, which reflect the components of hedonia, eudaimonia and interpersonal well-being, respectively). Additionally, we investigated the factor structure of the ten-item composite measure. Results The results replicated the assumed three-factor structure of the SWEMWBS and suggested a fourth factor comprising the three single items as well as a higher-order general well-being factor. Overall, the results indicated the appropriateness of a composite score as a macroindicatorof mental well-being as well as a decomposed analysis of the hedonic and eudaimonic components or single-item measurements of mental well-being, which would enable more differentiated insights and maximal comparability. Meaning of life showed the highest added value, thus indicating its distinctiveness among the components of mental well-being. Conclusions The results show how a highly comparable comprehensive yet economical measurement of mental well-being is possible in large-scale assessments. The addition of three of the most popular single items - happiness, satisfaction with life and meaning of life - to the more rigorous measure of wellbeing via the WEMWBS not only ensures comparability but also allows for a more comprehensive understanding of wellbeing. Regular public surveillance of mental well-being indicators can provide insights regarding the application, differentiation, reliability and sensitivity to change of these measurements over time and help guide public health action measures. public health surveillance positive mental health well-being life satisfaction meaning of life happiness measurement indicator Figures Figure 1 Contributions to the Literature Well-being has been established as an important indicator of public health, but the measures used are diverse. Eudaimonic well-being has been neglected so far, though it is highly relevant for public health outcomes. Comparability and comprehensiveness of well-being measures are crucial for public well-being surveillance. The results enable comparable, comprehensive and economic well-being measurement on a national level. The three most commonly used items measure a distinct dimension of wellbeing and can be combined with a short scale to firm a macroindicator. Meaning of life seems particularly important, adding the highest value to the standard short scale. Background Sixteen years ago, Bhutan became the first country in the world to incorporate the goal of enhancing gross national happiness (GNH) into its national constitution ( 1 ). Subsequently, an increasing number of countries have acknowledged the need to consider and monitor their population’s subjective well-being instead of solely focusing on objective measures such as gross domestic product (GDP) as a measure of prosperity ( 2 , 3 ). The term subjective well-being describes self-reported evaluations of one’s own well-being in general or across various domains ( 4 ). Although objective and subjective indicators of subjective well-being are interrelated, they yield distinct information on the well-being of individuals living in a certain environment and society ( 5 ). Accordingly, providing equal consideration to measurements of social indicators, economic indices, and subjective well-being within a nation’s comprehensive surveillance system helps guide informed policy decisions and maximize societal growth ( 6 , 7 ). However, prior assumptions of a positive association between economic growth and subjective well-being have been called into question. Hence, there is a need for public health research on how to promote subjective well-being by other means, such as by studying the determinants and consequences of interindividual differences or changes in well-being and how policy-makers can modify these factors ( 6 , 8 ). Recent policy-directed initiatives targeting the enhancement of subjective well-being, such as those from the World Health Organization (WHO; ( 9 ), the Organization for Economic Co-operation and Development (OECD; ( 10 ), or the United Kingdom (UK Measuring National Well-Being Program; ( 11 ) have combined objective and subjective well-being indicators with determinants on multiple levels to promote the international exchange of best-practice examples and support measures (e.g., ( 12 , 13 ). Regularly measuring subjective well-being at the national level is a precondition for the development of support measures and has several potential benefits, such as detecting unmet needs and vulnerable groups, properly allocating resources, capturing functional limitations or disabilities associated with chronic or acute diseases, and evaluating intervention or prevention efforts ( 14 , 15 ). In short, the regular surveillance of subjective indicators can act as “an overall barometer of the nation’s well-being” ( 15 ), as it enables the early identification of population trends and changes in well-being, the detection of outcomes related to stressful political or natural events, and the investigation of potential influential factors as well as international or regional comparisons ( 2 ). These insights can be used for evidence-based policy making and for initiating public health actions with the aim of systematically enhancing well-being ( reference masked ; ( 3 , 16 ), provided that the indicators and measures are sensitive to changes in external factors ( 17 ). Based on the example of the COVID-19 pandemic as a global stressor, some studies have suggested that subjective well-being measures are indeed sensitive to external factors at the national level and change significantly over time (e.g., ( 18 , 19 ). However, despite the increasing number of nations providing data for at least some selected subjective well-being indicators, the operationalizations, measures, and regularity of measurements tend to differ between countries, thus making it difficult to compare and accurately infer true magnitudes of change ( 8 , 20 ). Conceptualizations of Mental Well-being Efforts to operationalize well-being have been rather diverse, and there is no gold standard method for measuring this concept ( 3 , 10 , 21 – 23 ). Taken together, the majority of subjective well-being research focuses on the mental domain, which is distinct from the physical and social domains; furthermore, most studies distinguish between two different theoretical frameworks and research perspectives, namely, hedonia and eudaimonia ( 24 – 26 ). While the hedonic perspective (also called “subjective well-being”) refers to the affective (e.g., intensity or frequency of experiences according to valence and arousal levels) and cognitive (e.g., life evaluation and satisfaction) aspects of mental well-being, the eudaimonic perspective (also called “psychological well-being”) refers to positive functioning consistent with one’s goals and values (e.g., self-acceptance, meaning of life; ( 27 ) to reach personal growth and full potential ( 24 – 26 ). Corresponding definitions of mental well-being and relevant measurement tools, such as the Flourishing Scale ( 28 ) or the Warwick-Edinburgh Mental Well-being Scale (WEMWBS; ( 29 ), represent approaches to bring together the two different components of mental well-being of hedonia and eudaimonia. The Current Status and Importance of Population-based Mental Well-being Surveillance Overall, accumulating evidence points towards the need to measure and monitor positive mental health more comprehensively and regularly in order to be more closely aligned with other national or international surveillance reporting systems ( 3 , 5 , 30 , 31 ). On the basis of the WHO’s original definition of well-being ( 32 ) and as part of the salutogenic health approach ( 33 ), public mental health self-images and definitions have evolved and increasingly considered aspects of mental health that go beyond the mere absence of mental disorders ( 23 , 34 ). Mental well-being is one central positive mental health indicator that is closely related to but also distinct from mental illness, with independent explanatory power ( 35 ). Mental well-being and illness can be operationalized in various gradations on two distinct continua, as described in the Two Continua Model ( 36 ) and as summarized in a scoping review of empirical evidence by van Agteren & Iasiello (2019). Using this operationalization and monitoring mental well-being in addition to psychopathology at the population level may facilitate the promotion of mental health and the prevention of mental disorders ( 37 , 38 ). Initiatives such as those from the UK Office for National Statistics (“Measuring National Well-being program”, ( 39 ), the OECD (e.g., “How’s life?”, ( 40 ) or the GNH Index in Buthan ( 1 ) are pioneering in terms of their focus on positive mental health, but they largely depend on what each country provides as a database. At present, there are a few well-established large-scale surveys, such as the EU Statistics on Income and Living Conditions (EU-SILC) or the Gallup World Poll, that regularly measure at least some aspects of mental well-being across different countries. While all 38 member countries of the OECD currently collect data on mental illness, such as prevalence estimates of mental health conditions or symptoms, data on positive aspects of mental health are more rare ( 3 , 23 ). Among OECD member states, the greatest advances in terms of measuring mental well-being have been related to the measurement of life satisfaction ( n = 35 countries), followed by measurements of affective well-being and/or happiness ( n = 27 countries) ( 23 ). National data on eudaimonic aspects of mental well-being have been underrepresented for a long time among OECD member states but have recently shown an increasing trend for assessing meaning of life, with one item used in n = 30 countries ( 23 ). To date, only a few countries ( n = 10) have implemented a comprehensive mental well-being measure covering the three aspects of mental well-being, i.e., life satisfaction and affect (i.e., hedonia) as well as eudaimonia. Overall, the inconsistency and cross-cultural comparability of measures is still a major issue ( 23 ), with a few promising examples such as the (S)WEMWBS ( 23 ). The Present Study In 2019, the institute masked initiated a national mental health surveillance (MHS) system with the aim of monitoring developments in the country masked population’s mental health based on consented key indicators (reference masked). In the initiation phase, 14 topics with high relevance for public mental health were identified, one of which represented positive mental health, including well-being (reference masked). When examining the best way to measure mental well-being in country masked , as well as in other national MHS system, some important aspects need to be considered: the measures need to (a) be heterogeneous enough to capture the various components of the construct, (b) have good psychometric properties, (c) be comparable with those of other countries, and (d) be sufficiently short and minimize redundancy. The Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) and its short form (SWEMWBS), which include 14 and 7 items, respectively, are designed to capture both hedonic and eudaimonic aspects of well-being ( 29 ). The psychometric properties of the WEMWBS have already been evaluated in several different samples and languages ( 41 , 42 ). Heterogeneity has been observed in terms of the optimal factor structure for the WEMWBS: in some studies, the single-factor solution showed a good fit to the data ( 42 ); however, other studies showed that a three-factor solution (i.e., hedonic, eudaimonic, interpersonal relationships) showed an even better fit ( 43 , 44 ). In 2017, Lang & Bachinger published a German translation of the (S)WEMWBS and conducted a validation study based on an Austrian sample ( 45 ). Their findings revealed a bifactor model with one general well-being factor and three grouping factors representing the assumed structure of positive affect (hedonic), functioning (eudaimonic) and interpersonal relationships. Additionally, considering the scale’s increasing use among OECD member countries ( n = 7) ( 23 ), the (S)WEMWBS already meets most of the aforementioned measurement requirements and has therefore been implemented in the country masked MHS system. The psychometric properties of the SWEMWBS in country masked were investigated in a validation study (reference masked) , and SWEMWBS population norm values were presented based on representative data for the adult population living in country masked (reference masked) . However, the SWEMWBS also has possible shortcomings regarding the comprehensiveness of the construct as well as international linking possibility and comparability since the most frequently assessed single-item indicators of mental well-being, namely, happiness, satisfaction with life, and meaning of life, are not assessed. Additionally, in view of the OECD recommendations on how to measure mental well-being ( 10 ), it is unclear whether these three items provide useful information when administering the SWEMWBS. Therefore, the aim of the present research was to examine whether supplementing the SWEMWBS with the three most commonly used single-item measures of mental well-being (i.e., happiness, satisfaction with life, and meaning of life) can add substantial value to the assessment of mental well-being, thus potentially yielding a useful extension for national MHS systems or other large-scale assessments at the population level. Methods Sample and Procedure The data for the present study were derived from a cross-sectional online survey with adults living in country masked . The survey was conducted from November to December 2022. The study was carried out on behalf of the institute masked by the market and opinion research institute respondi gmbh and included 2,614 respondents from their in-house access panel. The sample was balanced according to age group (18–29, 30–44, 45–59, 60–74), sex by birth (male, female) and educational degree (low, moderate, high). The respondents provided their informed consent and received an incentive after participation in the form of credit points, which could be collected or redeemed for money from the panel provider. There were no missing values due to a forced choice format. Measures In this survey, the country masked translation of the SWEMWBS ( 29 ), which includes 7 statements on how participants experienced their affect and thoughts during the last two weeks, was administered. The items were answered on a 5-point scale ranging from 1 (“none of the time”) to 5 (“all of the time”). Hereinafter, the SWEMWBS items are referred to with their label and position in the original 14-item long version of the WEMWBS (i.e., “wemwbs3” is used as an abbreviation for the third item from the original long version of the scale). Additionally, general personal agreement to the following three other commonly used single-item measures of mental well-being was assessed via a seven-point scale ranging from 1 = “strong disagreement” to 7 = “strong agreement”. Happiness (hap) was measured by the country masked translation of “Taking all things together, I feel happy”. This item was similar to the single item assessed in the European Social Survey (ESS) as well as in several countries outside Europe (e.g., USA, Japan; ( 23 ). Satisfaction with life (swl) was measured with the country masked translation of the one-item version of the Satisfaction With Life Scale (SWLS; ( 46 , 47 ). Meaning of life (mol) was measured by the country masked translation of the item with the highest factor loading from the Meaning in Life Questionnaire (MLQ; “I know my life’s meaning”, ( 48 ). The means and 95% confidence intervals of all items relevant to the present study in different sociodemographic groups (age, sex, and educational status) are shown in the supplementary file (S1). Statistical Analyses Data analyses were conducted with R statistics version 4.3.0 (2023-04-21 ucrt., “Already Tomorrow”; ( 49 ) and R Studio version 2023.06.2 + 561 (“Mountain Hydrangea”; ( 50 ). To achieve the aim of investigating the value of adding the three single items (hap, swl, and mol) to the SWEMWBS, the following three-step analysis plan was implemented: replication of the factor structure of the 7-item SWEMWBS, exploration of the factor structure of the extended 10-item mental well-being measure, and evaluation of the three single-item measures of mental well-being. Consistency and specificity were calculated to determine the additional value of the three single-item measures of mental well-being. For all the factor analyses, the wlsmv estimator for ordinal scales was applied via the R package lavaan ( 51 ), which uses diagonally weighted least squares to estimate model parameters and returns robust standard errors and robust goodness-of-fit indicators based on mean- and variance-adjusted test statistics ( 52 ). The R syntax for the current data analyses is provided in the supplementary file (S2). Statistical power. As the present study used data from a larger assessment protocol (e.g., also investigating coping strategies), the necessary sample size was calculated for group comparisons within a 4×2×3 design that required 100 participants per cell (2,400 participants). In the present study, a post hoc power analysis was conducted to analyse whether half of the final sample (2,614/2 = 1,307) was sufficient to apply a random split-half procedure and detect model misspecification in the most complex model with the R shiny app pwrSEM ( 53 ). We calculated power to detect model misspecification based on the root mean square error of approximation ( RMSEA ) by means of all three power estimates described by MacCallum et al. (1996) in the most complex model in our study. According to MacCallum (1996), close fit, not close fit, and exact fit refer to the power to detect a difference between RMSEA values of .05 and .08, .05 and .01, and zero and .05, respectively. Replication of the Factor Structure of the 7-item SWEMWBS. First, to replicate the factor structure of the SWEMWBS, three confirmatory factor analyses (CFAs) were conducted with one- and three-factor solutions, as well as a bifactor solution with three grouping factors, in accordance with previous evidence regarding the factor structure of the SWEMWBS (reference masked; ( 29 , 41 – 45 ). In the case of misfit, we have kept the option open for additional explorative factor analysis (EFA) and to search for a better modelling option. Following the recommendations of Schermelleh-Engel et al. (2003), the model fit was evaluated based on standardized root mean square residuals ( SRMR ), comparative fit indices ( CFI ), and RMSEA . For RMSEA , values ≤ .05 were interpreted as good, values between .05 and .08 were interpreted as acceptable, values between .08 and .10 were marginal, and values ≥ .10 were poor. SRMR values of ≤ .05 were interpreted as good, and values of \(\: .95 were interpreted as acceptable. Exploration of the Factor Structure of the Extended 10-item Mental Well-being Measure. In a second step, we conducted an EFA of the composite measure including the single-item measures of mental well-being (hap, swl, and mol) and the seven SWEMWBS items. A random split-half sample procedure was applied, such that one random half of the sample was used for the EFA with oblique (“geomin”) rotation and the other random half was used to conduct subsequent tests of model fit. Based on the results of the EFA and assumptions regarding the factor structure of the SWEMWBS from prior evidence, different possible modelling options were compared to identify a model that captures the relationships between the SWEMWBS items and the three single items as accurately as possible while not overfitting the model to the present data. The Kaiser‒Meyer‒Olkin criterion (KMO) was used to test whether the number of observations was sufficient after the split-half procedure was performed. Parallel analysis and Eigenvalues were used to deduce the number of factors. CFI , SRMR , and RMSEA were investigated based on the same criteria as described above. Additionally, internal consistencies of the resulting scales were indicated either by Cronbach’s alpha coefficient or by Spearman Brown’s correlation coefficient in the case of two-item scales, as suggested by Eisinga et al. (2013). Omega coefficients ( 57 ) were calculated to measure the reliability of the general factor and the whole measurement model. Reliability analyses (to obtain Cronbach’s alpha coefficients and Spearman Brown’s correlation coefficients), the KMO test, and parallel analysis were conducted using the psych package ( 58 ). Polychoric correlations were used, and omega coefficients were calculated directly from the estimated models. As the use of polychoric correlation has been criticized (i.e., “ordinal alpha”, ( 59 ), we also calculated Pearson correlation coefficients; when they are both reported, they are separated by a backslash (polychoric/Pearson). In the case of alpha, covariance was utilized to create standardized quasimetric scales. Evaluation of the Additional Value of the Three Single-item Measures of Mental Well-being. Third, to investigate the possible redundancy or additional value of the thee single-item measures of mental well-being (hap, swl, mol), factor loadings and two forms of variance decomposition were analysed. The factor loading of the three additional single-item measures of mental well-being onto a distinct (grouping) factor was considered to indicate that they represent another domain of mental well-being not captured by the SWEMWBS and thus that they add value. Furthermore, if the three single items showed higher factor loadings on the grouping factor of the bifactor model than on the general factor, then these items were considered to add value to the SWEMWBS, and furthermore, separate analyses were considered to be appropriate. These analyses were supplemented by investigations of the proportion of shared variance of the three single items explained by the general factor (omega general) and the grouping factor (omega total). While these techniques focus on the added value of the three items together, the variance decompositions (consistency and specificity) of the individual items were also analysed based on the best model resulting from the analyses in steps one and two, as described above. To gain a more detailed picture of the partial independence of the single items from the SWEMWBS, we investigated the proportions of variance that the general factor and group factors explained together (omega total), the proportion of variance explained only by the general factor (omega general) and the proportion of variance explained by the grouping factors (omega group). Additionally, three linear regressions were conducted with each of the three single items as criteria and the SWEMWBS total score as a predictor. The coefficients of determination ( \(\:{R}^{2}\) values), which, in this case, are the same as the squared correlations between the criterion and predictor, were calculated to determine the proportion of the variances in the single items could be explained by the SWEMWBS total score. To account for the categorical nature of the item scales while still obtaining R ² values, a second set of linear regressions was performed by regressing the SWEMWEBS total score on the dummy-coded single items. Results Statistical Power The three power estimates suggested by MacCallum et al. (1996) were close enough to one to be rounded to one given our sample size and our most complex model. Hence, the sample size of 1,307 participants could be considered sufficient for the purpose of our study. Replication of the 7-item SWEMWBS Factor Structure Overall, the one-factor SWEMWBS model showed an unacceptable fit to the data, χ 2 ( 14 ) = 813.038, p < .001; SRMR = .047, CFI = .929, TLI = .894, RMSEA = .143, 90% CI [.134,.152]. The three-factor structure reflecting the three well-being components of hedonia, eudaimonia and interpersonal relationships showed a better fit to the data, χ 2 ( 14 ) = 359.379, p < .001, SRMR = .028, CFI = .968, TLI = .938, RMSEA = .108, 90% CI [.098, .098], similar to the results from other country masked adult samples (references masked). To avoid anomalous results from the classical symmetric bifactor model, the final bifactor model was identified as a Bifactor-(S I – 1) Model and included one reference item loading only on the general factor ( 60 ). This model is a special case of the Bifactor S-1 Model with one domain (i.e., the items of one grouping factor) loading either on the general factor or the items measuring the construct in general ( 61 ). The fits of the three models using three different anchor items representing the hedonic factor (wemwbs6, wemwbs7, wemwbs11) were initially compared. The selection of one out of three reference items is based on the consideration of the highest factor loadings in the unidimensional model and ensuring at least two items per grouping factor. The bi-factor model using wemwbs6 (“I’ve been dealing with problems well”) as a reference item and correlated grouping factors showed the best fit to the present data, χ 2 ( 21 ) = 65.455, p < .001, SRMR = .011, CFI = .992; TLI = .968, RMSEA = .079, 90% CI [.064;.095]. Exploration of the Factor Structure of the Extended 10-item Mental Well-being Measure To investigate the factor structure of the combination of the seven SWEMWBS items with the three single items (SI; i.e., hap, swl, mol), an EFA was conducted with the first random half of the sample and subsequent confirmatory analyses with the second half. The Kaiser‒Meyer‒Olkin criterion (KMO) was .91/.91, indicating good factorizability of the data. The results from a parallel analysis as well as inspection of the scree plot suggested four factors, whereas the Kaiser–Guttman criterion suggested one factor. Thus, exploratory factor analyses were conducted for one to four factors. When the item wemwbs6, which was used as a reference item in the SWEMWBS, was removed, confirmatory factor analyses resulted in a solution that could yield both three- and four-factor models. Since previous evidence regarding the factor structure of the SWEMWBS without the additional single items suggested a three-factor solution, the four-factor solution was chosen for a combined analysis of the SWEMWBS and SI. In all solutions with n > 1 factors, the three single items, which were not part of the SWEMWBS, had very high factor loadings on one factor ( ≥ .55) and very low loadings on the other factors ( ≤ .15). Hence, in the following, a distinct factor was modelled for the SI. An examination of the factor loadings of the SWEMWBS items in this four-factor solution suggested modelling of a second-order factor containing items 1 and 3 (hedonic), a third factor containing items 2 and 9 (interpersonal relationships), and a fourth factor with items 6, 7, and 11 (eudaimonic), thus reflecting the suggested three-factor structure of the SWEMWBS ( 45 ). To further investigate the suggested factor structure resulting from EFA, three subsequent CFAs were performed with the second half of the random sample. Table 1 summarizes the results of a single-factor model (see Fig. 1 A), a model with four correlated factors (see Fig. 1 B), and a bifactor model including four correlated grouping factors uncorrelated with the general factor and using wemwbs6 as a reference item (see Fig. 1 C). Model C, with one general well-being factor and four grouping factors, yielded the best results and showed an excellent fit to the data (Table 1 , Fig. 1 C), and Model B, with four correlated factors, showed an acceptable fit to the present data (Table 1 , Fig. 1 B). The single-factor model did not show acceptable model fit indices. More detailed results of the CFA for both acceptable models (B, C) are presented in Table 2 . Table 1 Model fit indices resulting from three confirmatory factor analyses for the Short Warwick–Edinburgh Mental Well-Being Scale (SWEMWBS) and three single-item measures of mental well-being with a random split-half sample of N = 1,307 Fit indices A. One-factor model B. Four-factor model C. Bi-factor model χ 2 2261.10 537.77 112.37 df 35 29 20 p < .001 < .001 < .001 SRMR .108 .037 .015 CFI . 683 .961 .985 TLI .593 .939 .967 RMSEA [90% CI ] .260 [.250; .270.] .101 [.091; .111] .074 [.062; .086] Notes. Robust fit indices are reported for three competing models. A. One-factor model displayed in Fig. 2A, B. Model with four correlated factors displayed in Fig. 2B, C. Bifactor model with four grouping factors displayed in Fig. 2C.; χ 2 = chi-square values, SRMR = standardized root mean square residuals, CFI = comparative fit indices, TLI = Tucker‒Lewis indices, RMSEA [90% CI ] = Root mean square errors of approximation with corresponding 90% confidence intervals. Table 2 Results from confirmatory factor analysis of models B (four correlated factors) and C (four grouping factors) corresponding to Figs. 1 B and C with a random split-half sample of N = 1,307 Model B Model C Estimate SE p Estimate SE p Latent factors General (WB) ~ wemwbs1 0.70 0.021 < .001 wemwbs2 0.77 0.016 < .001 wemwbs3 0.75 0.014 < .001 wemwbs6 0.86 0.014 < .001 wemwbs7 0.81 0.016 < .001 wemwbs9 0.60 0.021 < .001 wemwbs11 0.79 0.016 < .001 hap 0.61 0.022 < .001 swl 0.60 0.022 < .001 mol 0.60 0.020 < .001 Eudaimonic ~ wemwbs6 0.90 0.011 < .001 -- -- -- wemwbs7 0.82 0.012 < .001 0.31 0.049 < .001 wemwbs11 0.81 0.012 < .001 0.30 0.043 < .001 Hedonic ~ wemwbs1 0.79 0.014 < .001 0.50 0.056 < .001 wemwbs3 0.77 0.013 < .001 0.19 0.028 < .001 Interpersonal ~ wemwbs2 0.85 0.013 < .001 0.34 0.036 < .001 wemwbs9 0.67 0.017 < .001 0.31 0.038 < .001 Single items (SI) ~ hap 0.97 0.001 < .001 0.77 0.018 < .001 swl 0.94 0.006 < .001 0.71 0.018 < .001 mol 0.79 0.0105 < .001 0.50 0.021 < .001 Covariances Eudaimonic ~ ~ Hedonic 0.86 0.015 < .001 -0.51 0.210 .016 Interpersonal 0.85 0.013 < .001 -0.28 0.196 .148 Single items (SI) 0.60 0.019 < .001 − .32 0.103 .002 Hedonic ~ ~ Interpersonal 0.92 0.017 < .001 0.48 0.088 < .001 Single items (SI) 0.79 0.016 < .001 0.53 0.056 < .001 Interpersonal ~ ~ Single items (SI) 0.75 0.018 < .001 0.46 0.055 < .001 Variances wemwbs1 0.37 0.26 wemwbs2 0.284 0.29 wemwbs3 0.40 0.41 wemwbs6 0.20 0.26 wemwbs7 0.33 0.25 wemwbs9 0.55 0.54 wemwbs11 0.35 0.28 hap 0.07 0.03 swl 0.12 0.14 mol 0.37 0.41 Note. The estimates represent standardized values. SI = single items; hap = happiness; swl = satisfaction with life; mol = meaning of life. The resulting scale reliabilities based on the random-half split sample from the best model C (i.e., the bifactor model) were acceptable, with ωh = .85 for the general well-being factor and ωt = .95 for the whole measurement model, including general and grouping factors. For the total sample, the reliabilities were α = .93/.91 for the general factor, α = .92/.89 for the three additional items, ρ = 77/.73 for the hedonic factor, ρ = .85/.79 for the eudaimonic factor and ρ = .73/.68 for the interpersonal relationships factor. Evaluation of the Additional Value of the Three Single-item Measures of Mental Well-being Next, shared variances were investigated to examine whether the three additional items (hap, swl, mol) add substantial information to the use of the SWEMWBS for measuring mental well-being, thus revealing whether these three single items are a useful extension in the context of the national MHS program. The first evidence indicating that SI items represent a valuable extension is related to the results from the EFA, which suggest that the three items could compose their own factor (see above). A second indicator is the omega general coefficient, which indicates the proportion of the common variance of the items identified by the grouping factors that was explained by the general factor, which was 0.84, 0.65 and 0.60 for the eudaimonic, hedonic and interpersonal factors, respectively, and only 0.43 for the single-item factor. The shared variance explained by the grouping factor indicated by the omega group ranged from 0.05 to 0.15 for the SWEMWBS factors and reached 0.50 for the single-item factor. This difference indicates that the single-item factor differs conceptually from the SWEMWBS factors. This difference is also reflected by relatively high loadings of the three items on the single-item factor compared with the relatively small loadings of the SWEMWBS items on their respective grouping factors (Table 3 ). Furthermore, the value of supplementing the SWEMWBS with the single items was determined based on the consistency and specificity coefficients for the single-item factor based on the bifactor model (Fig. 2C). In Bi-Factor S-1 models, the choice of which items are treated as reference items and which ones are explained by an additional grouping factor changes the meaning of the general factor ( 60 ). In this case, the general factor is given meaning by the item “I’ve been dealing with problems well” and represents common parts of this item’s variance with the other items’ variances that were not explained by the grouping factors (eudaimonic, hedonic, interpersonal relationships, single items). The single-item factor captured those proportions of the variances that were explained neither by the general factor nor by measurement error. The consistency coefficients of true (error-free) item scores are defined as the shared variances in the true scores that are explained by the general factor, whereas the specificity coefficients are defined as the shared variances in the true scores that are explained by a given grouping factor ( 60 ). As consistency and specificity coefficients were calculated for the items’ true score variances as opposed to the observed variables’ variances, consistency and specificity coefficients pertaining to the same item added up to one (aside from rounding errors) ( 60 ). Additionally, the reliability of the single-item factor was investigated, representing the shared variances of the observed items that were explained neither by the general mental well-being factor nor by the single-item factor. Specifically, 38.9% of the true score variance in the item hap was explained by the general mental well-being factor (consistency), and 61.1% was explained by the single-item factor (specificity). For the item mol, the consistency was 61.3%, and the specificity was 38.7%. For the item swl, the consistency was 41.2%, and the specificity was 58.8%. Table 3 Total, General and Group Omega for Comparing Explained Variances of the General and the Grouping Factors Well-being (general) Eudaimonic well-being Hedonic well-being Interpersonal well-being Single items Omega total 0.95 0.89 0.79 0.74 0.93 Omega general 0.84 0.84 0.65 0.60 0.43 Omega group 0.09 0.05 0.15 0.13 0.50 Note. These calculations are based upon a cluster solution (i.e., every item is assigned to one group); therefore, the omega general and omega groups do not add up to the omega total (Revelle, 2023). In the three linear regression analyses, the proportions of the observed variances of hap, swl, and mol that could be explained by the SWEMWBS total scores (adjusted R ²) were 40.3%, 36.9%, and 34.1%, respectively. The item scales were treated as quasimetric and standardized. To better account for the categorical nature of the items and to create an interpretable R ², we also regressed the SWEMWBS score on the dummy-coded items, yielding adjusted R ² values of 41.0%, 37.7% and 34.5% for hap, swl, and mol, respectively. Thus, mol was the item with the largest proportion of variance that was not explained by the latent mental well-being factor or by commonalities between the three single-item measures (40.5%). The relatively low specificity of mol indicated that the true score variance was not representative of the three additional items. Additionally, swl had a low proportion of variance that was not explained by the general mental well-being factor or by the grouping factor pertaining to the additional items (13.6%), followed by hap (3.4%). For swl, the specificity was 58.8%, and commonalities between the three additional items explained a higher proportion of the items’ true scores than mol (38.7%). Among the three single items, hap had the largest squared correlation with the SWEMWBS total score (41.0%, corresponding to a correlation of .640), and the specificity of hap was very similar to that of swl (61.1% vs. 58.8%). Overall, the hap item was most representative of both the general mental well-being factor and the grouping factor. Discussion Based on a country masked sample of 2,416 adults who were balanced according to age, sex and education, the present research investigated the value of supplementing the SWEMWBS with the three most frequently used single-item measures of mental well-being (happiness, satisfaction with life, meaning of life) as well as the factor structure of this composite measure in the context of public health surveillance or other large-scale population-based studies across various countries. The aim of this study was to derive empirical evidence and practical implications for the measurement and regular reporting of mental well-being at the national level as well as to enhance the ability to conduct international comparisons while considering the theoretical and conceptual presumptions and positive mental health and well-being measurement recommendations in OECD countries ( 23 ). The present results replicated the good-to-acceptable fit of the original SWEMWBS three-factor structure containing the components of mental well-being of hedonia, eudaimonia, and interpersonal relationships and were preferable to the single-factor solution (reference masked) ( 45 ). However, the consideration of one general well-being factor in a bi-factor model improved the fit even further, as observed in other countries ( 43 , 45 ). Moreover, research on the factor structure of the SWEMWBS combined with the three single-item measures of happiness, satisfaction with life, and meaning of life suggested that the three single items can be best represented by an additional factor. Thus, a substantial proportion of the variance in the participants’ answers was unique to those three additional items, suggesting the usefulness of assessing the three single items in addition to the SWEMWBS. When interpreting these findings, it is to be considered that the three single items refer to mental well-being in general, whereas the SWEMWBS items refer to mental well-being over the past two weeks. It is therefore reasonable to assume that SWEMWBS items measure rather situational mental well-being, and the single items measure dispositional mental well-being. As addressed by latent state-trait modelling ( 62 ), current mental well-being can be disaggregated into a general trait component (i.e., individual set-point) and a situation-specific component while taking the assumption into account that short-term fluctuations may coincide to some extent with long-term changes ( 63 ). There is evidence suggesting that satisfaction with life exhibits less sensitivity to change than affective states, which have been found to be more likely to exhibit intraindividual fluctuations over time ( 26 , 63 ). However, sensitivity to changes in various mental well-being indicators has yet to be fully elucidated at the population level; a longitudinal analysis (e.g., in a population subsample) is needed to examine this phenomenon. The within-person agreement between the three general mental well-being items and SWEMWBS items referring to an actual state needs to be further investigated and compared across diverse population groups under various life circumstances. The regular national surveillance of the different mental well-being domains can enable such analyses. Similar to other recent analyses, such as one from Finland ( 43 ), the application of a bifactor model in the present analyses outperformed the four-factor correlated model, thus indicating that the combined 10-item measure of mental well-being could be best described by adding one general factor to four grouping factors (i.e., eudaimonia, hedonia, interpersonal relationships, and the three single items). Therefore, the present findings extend current evidence and emphasize the need to recognize the multidimensionality and complexity of mental well-being ( 64 ). As discussed in previous research (masked reference) , even though the SWEMWBS already covers various components of mental well-being, it seems as if it cannot completely capture the construct of mental well-being. The results of this research suggest that a bundle of items cannot completely replace the commonly used single-item measures of the theoretically same mental well-being component, such as in the case of eudaimonia and meaning of life or hedonia and satisfaction with life. In contrast, the use of only single items would lead to a substantial loss of information, as noted by Ruggeri et al. (2020), within the context of a similar approach to measuring mental well-being. Considering the literature on sequencing effects of specific happiness or satisfaction questions asked before or after more general questions on the same topic (REF), we propose that the more general single-item measures analysed here provide access to the general or dispositional beliefs that people hold towards their lives. The more specific items of the SWEMWBS, on the other hand, require an active appraisal of the resondent’s life, which will then have an impact on their more general beliefs. It therefore makes sense to combine these single item measures - asked first - with more rigorous instruments to get a more comprehensive understanding of the individual's wellbeing. Compared with happiness and satisfaction with life, meaning of life had the lowest item reliability, the lowest degree of similarity to the other single items, and the smallest squared correlation with the SWEMWBS total scores. These findings suggest that meaning of life might be the most useful supplement to the SWEMWBS from a statistical perspective. This interpretation is supported by a phenomenologically driven perspective, as meaning of life, which captures existential elements of what it means to be human, has been defined as one of the most significant components of eudaimonia ( 65 ). Moreover, in light of evidence suggesting direct paths from meaninglessness to risky health behaviours ( 66 ), mortality ( 67 ) and suicide ( 68 ), the inclusion of meaning of life as a core indicator of eudaimonia in national mental health surveillance programs can be useful for developing preventive interventions. This is particularly relevant in view of the lack of eudaimonia assessments in international mental well-being surveillance systems ( 3 , 21 , 23 ). Both happiness and satisfaction with life showed higher reliability, lower consistency and lower specificity across all ten mental well-being items, thus indicating greater similarity to the SWEMWBS items. However, the results from manifest linear relations showed that considerable proportions of happiness (59.7%) and satisfaction with life (63.1%) could not be explained by the SWEMWBS total scores. From a theoretical perspective, positive affect and life satisfaction are interrelated but have the potential to provide distinct and complementary information when they are measured separately ( 69 ). Thus, it may also be advantageous to consider a differentiated analysis of the cognitive (i.e., satisfaction with life) and affective aspects of the hedonic component of mental well-being. The fact that the SWEMWBS comprises affect and life satisfaction within one hedonic component highlights the potential benefit of using additional single items to address more differentiated research questions. Regarding the criteria mentioned in the introduction (i.e., heterogeneity of construct, good psychometric properties, comparability, sufficient length without redundancy), the final bi-factor model can be considered appropriate, as it enables a 10-item composite measure as well as differentiated analyses of individuals components of mental well-being. However, this finding needs to be replicated by future studies, since country-specific bias, language-specific bias, or study-specific bias cannot be ruled out. In addition, the selection of reference items during bi-factor modelling was data driven and did not allow generalizability; thus, future representative studies are needed to obtain further evidence on the consistency and transferability of the results. National mental health surveillance programs will have to show if population-based levels are changing by examining external factors and by examining the composite and decomposed measures (i.e., factors and single items). These mental well-being data will have to be compared with objective well-being indicators. Despite ongoing progress and the additional insights provided by the current study, the factors relevant to and the composition of mental well-being may evolve in the face of societal changes or progressive methodology. For example, satisfaction with society has already been proposed to complement mental well-being at the macrolevel ( 5 ), and the identification of core indicators “that allow individuals, communities, and societies to flourish” ( 70 ) in the new century is still under development. Therefore, future studies should review societal dynamics as well as the risk and protective factors for mental well-being ( 69 ) to further evaluate the distinctiveness and relevancy of each component and item with respect to implementing public health strategies (e.g., targeted mental well-being promotion). Conclusions The three most commonly used single-item measures of mental well-being (i.e., happiness, satisfaction with life, meaning of life) can be used to supplement the SWEMWBS in a national MHS program. This finding is consistent with the recommendations of the OECD regarding how to measure positive mental health. The measurement of meaning of life was found to be particularly valuable, as it had the largest additional contribution to the assessment of mental well-being based on the present findings and due to the lack of eudaimonia assessments in large-scale population-based studies. Combining the 7-item SWEMWBS with these 3 single-item measures yields a composite score that can be used as a macroindicator of the latest (situational) and general (dispositional) tendencies as well as an indicator of the central components of mental well-being (i.e., hedonia and eudaimonia). Decomposing the macroindicator into its components or into single items enables more differentiated insights and comparisons with other data sources. Future research should determine the optimal measures of mental well-being in terms of construct validity, reliability, comprehensiveness, efficiency and feasibility. Furthermore, future studies should examine the sensitivity to change of various mental well-being assessments at the national level and with respect to international comparability. Abbreviations CFA – Confirmatory Factor Analysis CI – Confidence Interval EFA – Exploratory Factor Analysis GDP – Gross Domestic Product GNH – Gross National Happiness MHS – Mental Health Surveillance OECD - Organisation for Economic Co-operation and Development RMSEA – Root Mean Square Error of Approximation SI – single items SRMR – Standardized Root Mean Square Residual SWEMWBS – Short Warwick-Edinburgh Well-being Scale WHO – World health Organization Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of the country masked Psychological Society (approval number 2022-10-28VA). Consent for publication Not applicable. Availability of data and material As part of Coping study I, personal data was collected and processed that cannot be published as open data for data protection reasons. However, the data will be made available on request from the Epidemiological Data and Survey Centre of the Robert Koch Institute for non-commercial research. Requests should be directed to [email protected] . A detailed description of the data, the data scheme and sample data are published at the same time as the publication as open data and available at: Caroline Cohrdes (2024): Dealing with challenges in life - Coping study I. Berlin: Zenodo DOI: 10.5281/zenodo.13304391 Competing interests The authors have nothing to declare. Funding This research was funded by the Robert Koch Institute, Berlin, Germany (project number 911610). Author’s contributions CC developed the study conception and design and raised the funding. Data analyses were performed by SJ and CC. The first draft of the manuscript was written by CC. Both authors read, revised and approved the final manuscript. References Sithey G, Thow AM, Li M. Gross national happiness and health: Lessons from Bhutan. Bull World Health Organ. 2015;93:514-. Helliwell JF, Layard R, Sachs JD, De Neve J-E, Aknin LB, Wang S. World Happiness Report 2022. New York: Sustainable Development Solutions Network; 2022. Vik MH, Carlquist E. Measuring subjective well-being for policy purposes: The example of well-being indicators in the WHO "Health 2020" framework. Scand J Public Health. 2018;46(2):279-86. Diener E, Suh E. Measuring quality of life: economis, social and subjective indicators. Social Indicators Research. 1997;40(1):189-216. Kroll C, Delhey J. A happy nation? Opportunities and challenges of using subjective indicators in policymaking. Social Indicators Research. 2013;114(1):13-28. Barry MM. Addressing the determinants of positive mental health: concepts, evidence and practice. International Journal of Mental Health Promotion. 2009;11(3):4-17. Diener E, Diener M, Diener C. Factors predicting the subjective well-being of nations. In: Diener E, editor. Culture and Well-Being: The Collected Works of Ed Diener. Dordrecht: Springer Netherlands; 2009. p. 43-70. Veenhoven R. Comparability of happiness across nations. Journal of Happiness Studies. 2009:211-34. World Health Organization. Targets and indicators for Health 2020: version 4. World Health Organization. Regional Office for Europe; 2018. OECD. OECD guidelines on measuring subjective well-being Paris: OECD Publishing; 2013. Jenkins M. Knowledge and practice mobilities in the process of policy-making: The case of UK national well-being statistics. Political Geography. 2017;56:24-33. OECD. How to Make Societies Thrive? Coordinating Approaches to Promote Well-being and Mental Health. Paris; 2023. European Commission. Synopsis report for the commission communication on a comprehensive approach to mental health 2023. Hennessy CH, Moriarty DG, Zack MM, Scherr PA, Brackbill R. Measuring health-related quality of life for public health surveillance. Public Health Rep. 1994;109(5):665-72. Hicks S, Tinkler L, Allin P. Measuring subjective well-being and its potential role in policy: Perspectives from the UK Office for National Statistics. Social Indicators Research. 2013;114(1):73-86. OECD. How's Life in Your Region? Measuring Regional and Local Well-being for Policy Making. Paris: OECD Publishing; 2014. Diener E. Guidelines for national indicators of subjective well-being and ill-being. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being. 2006;7(4):397-404. Office for National Statistics. Personal well-being in the UK: April 2020 to March 2021. Estimates of life satisfaction, feeling that the things done in life are worthwhile, happiness and anxiety at the UK, country, regional, county and local authority level. Newport, UK; 2021. Mauz E, Walther L, Junker S, Kersjes C, Damerow S, Eicher S, et al. Time trends in mental health indicators in Germany's adult population before and during the COVID-19 pandemic. Frontiers in public health. 2023;11(1065938). Tov W, Diener E. Culture and Subjective Well-Being. In: Diener E, editor. Culture and well-being: The collected works of Ed Diener. Dordrecht: Springer Netherlands; 2009. p. 9-41. Lindert J, Bain PA, Kubzansky LD, Stein C. Well-being measurement and the WHO health policy Health 2010: systematic review of measurement scales. European Journal of Public Health. 2015;25(4):731-40. Diener E, Wirtz D, Biswas-Diener R, Tov W, Kim-Prieto C, Choi D-w, et al. New measures of well-being. In: Diener E, editor. Assessing well-being. 39: Springer, Dordrecht; 2009. p. 247-66. OECD. Measuring population mental health: Tools and current country practice. Paris: OECD Publishing; 2023. Diener E, Ryan K. Subjective well-being: A general overview. South African Journal of Psychology. 2009;39(4):391-406. Keyes CLM, Shmotkin D, Ryff CD. Optimizing well-being: The empirical encounter of two traditions. Journal of Personality and Social Psychology. 2002;82(6):1007-22. Tov W. Well-being concepts and components. In: Diener E, Oishi S, Tay L, editors. Handbook of well-being. Salt Lake City, UT:: DEF Publishers; 2018. Ryff CD, Singer BH. Know thyself and become what you are: A eudaimonic approach to psychological well-being. J Happiness Stud. 2008;9(1):13-39. Diener E, Wirtz D, Tov W, Kim-Prieto C, Choi D-w, Oishi S, et al. New well-being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicators Research. 2010;97(2):143-56. Tennant R, Hiller L, Fishwick R, Platt S, Joseph S, Weich S, et al. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation. Health and Quality of Life Outcomes. 2007;5(1):63. Diener E, Oishi S, Lucas RE. National accounts of subjective well-being. American Psychologist. 2015;70(3):234-42. Forgeard MJ, Jayawickreme E, Kern ML, Seligman ME. Doing the right thing: Measuring wellbeing for public policy. International Journal of Wellbeing. 2011;1(1):79-106. World Health Organization. Official records of the World Health Organization no. 2. Summary Report on Proceedings, Minutes and Final Acts of the International Health Conference Held in New York From 19 June to 22 July 1946. . Geneva; 1948. Keyes CL. Mental health as a complete state: How the salutogenic perspective completes the picture. In: Bauer GF, Hämmig O, editors. Bridging occupational, organizational and public health. Heidelberg New York London Springer, Dordrecht; 2014. p. 179-92. Kobau R, Seligman ME, Peterson C, Diener E, Zack MM, Chapman D, et al. Mental health promotion in public health: perspectives and strategies from positive psychology. Am J Public Health. 2011;101(8):1-9. Keyes CLM. Mental Illness and/or mental health? Investigating axioms of the complete state model of health. Journal of Consulting and Clinical Psychology. 2005;73(3):539-48. Westerhof GJ, Keyes CL. Mental illness and mental health: The two continua model across the lifespan. Journal of Adult Development. 2010;17(2):110–9. Van Agteren J, Iasiello M. Advancing our understanding of mental wellbeing and mental health: The call to embrace complexity over simplification. Australian Psychologist. 2019;55(4):307-16. Keyes CLM, Dhingra SS, Simoes EJ. Change in level of positive mental health as a predictor of future risk of mental illness. American Journal of Public Health. 2010;100(12):2366-71. Jones R, Randall C. Measuring national well-being: Quality of life in the UK 2018. Newport, UK; 2018. OECD. How's Life? 2020: Measuring Well-being. Paris: OECD Publishing; 2020. Stewart-Brown S, Tennant A, Tennant R, Platt S, Parkinson J, Weich S. Internal construct validity of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS): a Rasch analysis using data from the Scottish Health Education Population Survey. Health Qual Life Outcomes. 2009;7(15). Koushede V, Lasgaard M, Hinrichsen C, Meilstrup C, Nielse nL, Rayce SB, et al. Measuring mental well-being in Denmark: Validation of the original and short version of the Warwick-Edinburgh mental well-being scale (WEMWBS and SWEMWBS) and cross-cultural comparison across four European settings. . Psychiatry Research. 2019;271:502-9. Sarasjärvi KK, Elovainio M, Appelqvist-Schmidlechner K, Solin P, Tamminen N, Therman S. Exploring the structure and psychometric properties of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in a representative adult population sample. . Psychiatry Research. 2023;328(115465.). Shannon S, Breslin G, Prentice G, Leavey G. Testing the factor structure of the Warwick-Edinburgh Mental Well-Being Scale in adolescents: A bi-factor modelling methodology. Psychiatry research. 2020;293(113393). Lang G, Bachinger A. Validation of the German Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in a community-based sample of adults in Austria: a bi-factor modelling approach. Journal of Public Health. 2017;25(2):135-46. Diener E, Emmons RA, Larsen RJ, Griffin S. The Satisfaction With Life Scale. J Pers Assess. 1985;49(1):71-5. Janke S, Glöckner-Rist A. Deutsche Version der Satisfaction with Life Scale (SWLS). Zusammenstellung sozialwissenschaftlicher Items und Skalen (ZIS). 2012. Steger MF, Frazier P, Oishi S, Kaler M. The meaning in life questionnaire: Assessing the presence of and search for meaning in life. Journal of Counseling Psychology. 2006;53(1):80-93. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. R Core Team. RStudio: Integrated development environment for R. Boston, MA: Posit Software, PBC; 2023. Rosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Soft. 2012;48(2):1-36. Savalei V. Improving Fit Indices in Structural Equation Modeling with Categorical Data. Multivariate Behavioral Research. 2020;56(3):390–407. Wang YA, Rhemtulla M. Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial. Adv Methods Pract Psychol Sci. 2021;4(1). MacCallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure modeling. Psychol Methods. 1996;1(2):130-49. Schermelleh-Engel K, Moosbrugger H, Müller H. Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Psychol Methods. 2003;8(2):23-74. Eisinga R, Te Grotenhuis M, Pelzer B. The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? International journal of public health. 2013;58(4):637-42. McDonald RP. Test theory: A unified treatment. . Mahwah, NJ: Lawrence Erlbaum; 2013. Revelle W. psych: Procedures for Psychological, Psychometric, and Personality Research. R package version 2.3.12 Evanston, Illinois: Northwestern University; 2023 [Available from: https://CRAN.R-project.org/package=psych. Chalmers RP. On misconceptions and the limited usefulness of ordinal alpha. Educational and Psychological Measurement. 2018;78(6):1056-71. Eid M, Geiser C, Koch T, Heene M. Anomalous results in G-factor models: Explanations and alternatives. Psychol Methods. 2017;22(3):541-62. Eid M. Multi-faceted constructs in abnormal psychology: Implications of the Bifactor S - 1 Model for individual clinical assessment. J Abnorm Child Psychol. 2020;48:895–900. Steyer R, Schmitt M, Eid M. Latent state–trait theory and research in personality and individual differences. European Journal of Personality. 1999;13(5):389-408. Eid M, Diener E. Global judgments of subjective well-being: Situational variability and long-term stability. Social Indicators Research. 2004;65(3):245-77. Ruggeri K, Garcia-Garzon E, Maguire Á, Matz S, Huppert FA. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries Health Qual Life Outcomes. 2020;18(192). Ryff CD. Psychological well-being revisited: advances in the science and practice of eudaimonia. Psychotherapy and Psychosomatics. 2014;83(1):10–28. Steger MF, Fitch-Martin AR, Donnelly J, Rickard KM. Meaning in life and health: Proactive health orientation links meaning in life to health variables among american undergraduates. Journal of Happiness Studies. 2015;16(3):583-97. Krause N. Meaning in Life and Mortality. The Journals of Gerontology: Series B. 2009;64B(4):517-27. Kleiman EM, Beaver JK. A meaningful life is worth living: Meaning in life as a suicide resiliency factor. Psychiatry Research. 2013;210(3):934-9. Pavot W, Diener E. The Satisfaction With Life Scale and the emerging construct of life satisfaction. The Journal of Positive Psychology. 2008;3(2):137-52. Seligman M, Csikszentmihalyi M. Positive psychology. An introduction. American Psychologist. 2000;55(1):5-14. Additional Declarations No competing interests reported. Supplementary Files 250114SUPWBCohrdesJunker.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6513849","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453953395,"identity":"0d33e5a0-7a09-4fb5-92b2-044cd446501a","order_by":0,"name":"Caroline Cohrdes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBAC/gYeBgYgkudnYGM4AMSEgcQBiBbDmQ3EajFwgGhJ2HAApJwoLey9xz68qahLML59LPEAQ5kNEVp4ziXPnHPmcILZubQDBxjOpRGhRSLHmJm37UCC2Rn2hgOMbYeJ1fIP6LAesJb/RGiJAGlpYE7YwMN2AKjlAGEtEmfOGDPOOXbYcMYZtoQDCeeSCWvhb+8xZnhTUyfP38Nm/OFDmR1hLagggVQNo2AUjIJRMAqwAwBOIznH3bksYgAAAABJRU5ErkJggg==","orcid":"","institution":"Robert Koch Institute","correspondingAuthor":true,"prefix":"","firstName":"Caroline","middleName":"","lastName":"Cohrdes","suffix":""},{"id":453953396,"identity":"8d7cc1d0-2515-4378-b544-536343456d6f","order_by":1,"name":"Stephan Junker","email":"","orcid":"","institution":"Robert Koch Institute","correspondingAuthor":false,"prefix":"","firstName":"Stephan","middleName":"","lastName":"Junker","suffix":""}],"badges":[],"createdAt":"2025-04-23 15:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6513849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6513849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82582955,"identity":"8fcab793-e348-42a3-aa71-77086b13e19d","added_by":"auto","created_at":"2025-05-13 06:47:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110292,"visible":true,"origin":"","legend":"\u003cp\u003eThree competing factor models for the Short Warwick–EdinburghMental Well-Being Scale supplemented with the three frequently used single-item measures of happiness (hap), satisfaction with life (swl) and meaning of life (mol). \u003cstrong\u003eA\u003c/strong\u003e is a simple one-factor model, \u003cstrong\u003eB\u003c/strong\u003e is a model with four correlated factors, \u003cstrong\u003eD\u003c/strong\u003e is a bifactorwith four grouping factors and wemwbs6 as the reference item.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6513849/v1/c41f230e74e0144d913e81c2.jpg"},{"id":93317512,"identity":"d6c22ea2-911b-448d-81bd-329fd69030cd","added_by":"auto","created_at":"2025-10-12 02:46:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1350797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6513849/v1/4dd0f79f-a85b-4f98-b797-0bea03703c26.pdf"},{"id":82582956,"identity":"4d24b19e-a270-4a28-a70e-5719ce7c1ff0","added_by":"auto","created_at":"2025-05-13 06:47:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27526,"visible":true,"origin":"","legend":"","description":"","filename":"250114SUPWBCohrdesJunker.docx","url":"https://assets-eu.researchsquare.com/files/rs-6513849/v1/3e297060e01bfe17730447a1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Shall we add some meaning? Investigating useful single-item extensions to the short Warwick-Edinburgh Mental Well-Being Scale for national public health surveillance","fulltext":[{"header":"Contributions to the Literature","content":"\u003cul\u003e\n \u003cli\u003eWell-being has been established as an important indicator of public health, but the measures used are diverse.\u003c/li\u003e\n \u003cli\u003eEudaimonic well-being has been neglected so far, though it is highly relevant for public health outcomes.\u003c/li\u003e\n \u003cli\u003eComparability and comprehensiveness of well-being measures are crucial for public well-being surveillance.\u003c/li\u003e\n \u003cli\u003eThe results enable comparable, comprehensive and economic well-being measurement on a national level.\u003c/li\u003e\n \u003cli\u003eThe three most commonly used items measure a distinct dimension of wellbeing and can be combined with a short scale to firm a macroindicator.\u003c/li\u003e\n \u003cli\u003eMeaning of life seems particularly important, adding the highest value to the standard short scale.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eSixteen years ago, Bhutan became the first country in the world to incorporate the goal of enhancing gross national happiness (GNH) into its national constitution (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Subsequently, an increasing number of countries have acknowledged the need to consider and monitor their population\u0026rsquo;s \u003cem\u003esubjective well-being\u003c/em\u003e instead of solely focusing on objective measures such as gross domestic product (GDP) as a measure of prosperity (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The term subjective well-being describes self-reported evaluations of one\u0026rsquo;s own well-being in general or across various domains (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Although objective and subjective indicators of subjective well-being are interrelated, they yield distinct information on the well-being of individuals living in a certain environment and society (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Accordingly, providing equal consideration to measurements of social indicators, economic indices, and subjective well-being within a nation\u0026rsquo;s comprehensive surveillance system helps guide informed policy decisions and maximize societal growth (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, prior assumptions of a positive association between economic growth and subjective well-being have been called into question. Hence, there is a need for public health research on how to promote subjective well-being by other means, such as by studying the determinants and consequences of interindividual differences or changes in well-being and how policy-makers can modify these factors (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Recent policy-directed initiatives targeting the enhancement of subjective well-being, such as those from the World Health Organization (WHO; (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), the Organization for Economic Co-operation and Development (OECD; (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), or the United Kingdom (UK Measuring National Well-Being Program; (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) have combined objective and subjective well-being indicators with determinants on multiple levels to promote the international exchange of best-practice examples and support measures (e.g., (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegularly measuring subjective well-being at the national level is a precondition for the development of support measures and has several potential benefits, such as detecting unmet needs and vulnerable groups, properly allocating resources, capturing functional limitations or disabilities associated with chronic or acute diseases, and evaluating intervention or prevention efforts (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In short, the regular surveillance of subjective indicators can act as \u0026ldquo;an overall barometer of the nation\u0026rsquo;s well-being\u0026rdquo; (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), as it enables the early identification of population trends and changes in well-being, the detection of outcomes related to stressful political or natural events, and the investigation of potential influential factors as well as international or regional comparisons (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These insights can be used for evidence-based policy making and for initiating public health actions with the aim of systematically enhancing well-being (\u003cem\u003ereference masked\u003c/em\u003e; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), provided that the indicators and measures are sensitive to changes in external factors (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Based on the example of the COVID-19 pandemic as a global stressor, some studies have suggested that subjective well-being measures are indeed sensitive to external factors at the national level and change significantly over time (e.g., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, despite the increasing number of nations providing data for at least some selected subjective well-being indicators, the operationalizations, measures, and regularity of measurements tend to differ between countries, thus making it difficult to compare and accurately infer true magnitudes of change (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eConceptualizations of Mental Well-being\u003c/h3\u003e\n\u003cp\u003eEfforts to operationalize well-being have been rather diverse, and there is no gold standard method for measuring this concept (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Taken together, the majority of subjective well-being research focuses on the \u003cem\u003emental\u003c/em\u003e domain, which is distinct from the physical and social domains; furthermore, most studies distinguish between two different theoretical frameworks and research perspectives, namely, \u003cem\u003ehedonia\u003c/em\u003e and \u003cem\u003eeudaimonia\u003c/em\u003e (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). While the hedonic perspective (also called \u0026ldquo;subjective well-being\u0026rdquo;) refers to the affective (e.g., intensity or frequency of experiences according to valence and arousal levels) and cognitive (e.g., life evaluation and satisfaction) aspects of mental well-being, the eudaimonic perspective (also called \u0026ldquo;psychological well-being\u0026rdquo;) refers to positive functioning consistent with one\u0026rsquo;s goals and values (e.g., self-acceptance, meaning of life; (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) to reach personal growth and full potential (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Corresponding definitions of mental well-being and relevant measurement tools, such as the Flourishing Scale (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) or the Warwick-Edinburgh Mental Well-being Scale (WEMWBS; (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), represent approaches to bring together the two different components of mental well-being of hedonia and eudaimonia.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe Current Status and Importance of Population-based Mental Well-being Surveillance\u003c/h2\u003e \u003cp\u003eOverall, accumulating evidence points towards the need to measure and monitor \u003cem\u003epositive mental health\u003c/em\u003e more comprehensively and regularly in order to be more closely aligned with other national or international surveillance reporting systems (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). On the basis of the WHO\u0026rsquo;s original definition of well-being (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and as part of the salutogenic health approach (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), public mental health self-images and definitions have evolved and increasingly considered aspects of mental health that go beyond the mere absence of mental disorders (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Mental well-being is one central positive mental health indicator that is closely related to but also distinct from mental illness, with independent explanatory power (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Mental well-being and illness can be operationalized in various gradations on two distinct continua, as described in the \u003cem\u003eTwo Continua Model\u003c/em\u003e (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and as summarized in a scoping review of empirical evidence by van Agteren \u0026amp; Iasiello (2019). Using this operationalization and monitoring mental well-being in addition to psychopathology at the population level may facilitate the promotion of mental health and the prevention of mental disorders (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInitiatives such as those from the UK Office for National Statistics (\u0026ldquo;Measuring National Well-being program\u0026rdquo;, (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), the OECD (e.g., \u0026ldquo;How\u0026rsquo;s life?\u0026rdquo;, (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) or the GNH Index in Buthan (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) are pioneering in terms of their focus on positive mental health, but they largely depend on what each country provides as a database. At present, there are a few well-established large-scale surveys, such as the EU Statistics on Income and Living Conditions (EU-SILC) or the Gallup World Poll, that regularly measure at least some aspects of mental well-being across different countries. While all 38 member countries of the OECD currently collect data on mental illness, such as prevalence estimates of mental health conditions or symptoms, data on positive aspects of mental health are more rare (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Among OECD member states, the greatest advances in terms of measuring mental well-being have been related to the measurement of life satisfaction (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35 countries), followed by measurements of affective well-being and/or happiness (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27 countries) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). National data on eudaimonic aspects of mental well-being have been underrepresented for a long time among OECD member states but have recently shown an increasing trend for assessing meaning of life, with one item used in \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30 countries (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). To date, only a few countries (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10) have implemented a comprehensive mental well-being measure covering the three aspects of mental well-being, i.e., life satisfaction and affect (i.e., hedonia) as well as eudaimonia. Overall, the inconsistency and cross-cultural comparability of measures is still a major issue (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), with a few promising examples such as the (S)WEMWBS (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe Present Study\u003c/h3\u003e\n\u003cp\u003eIn 2019, the \u003cem\u003einstitute masked\u003c/em\u003e initiated a national mental health surveillance (MHS) system with the aim of monitoring developments in the \u003cem\u003ecountry masked\u003c/em\u003e population\u0026rsquo;s mental health based on consented key indicators \u003cem\u003e(reference masked).\u003c/em\u003e In the initiation phase, 14 topics with high relevance for public mental health were identified, one of which represented positive mental health, including well-being \u003cem\u003e(reference masked).\u003c/em\u003e When examining the best way to measure mental well-being in \u003cem\u003ecountry masked\u003c/em\u003e, as well as in other national MHS system, some important aspects need to be considered: the measures need to (a) be heterogeneous enough to capture the various components of the construct, (b) have good psychometric properties, (c) be comparable with those of other countries, and (d) be sufficiently short and minimize redundancy.\u003c/p\u003e \u003cp\u003eThe Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) and its short form (SWEMWBS), which include 14 and 7 items, respectively, are designed to capture both hedonic and eudaimonic aspects of well-being (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The psychometric properties of the WEMWBS have already been evaluated in several different samples and languages (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Heterogeneity has been observed in terms of the optimal factor structure for the WEMWBS: in some studies, the single-factor solution showed a good fit to the data (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e); however, other studies showed that a three-factor solution (i.e., hedonic, eudaimonic, interpersonal relationships) showed an even better fit (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In 2017, Lang \u0026amp; Bachinger published a German translation of the (S)WEMWBS and conducted a validation study based on an Austrian sample (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Their findings revealed a bifactor model with one general well-being factor and three grouping factors representing the assumed structure of positive affect (hedonic), functioning (eudaimonic) and interpersonal relationships. Additionally, considering the scale\u0026rsquo;s increasing use among OECD member countries (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), the (S)WEMWBS already meets most of the aforementioned measurement requirements and has therefore been implemented in the \u003cem\u003ecountry masked\u003c/em\u003e MHS system. The psychometric properties of the SWEMWBS in \u003cem\u003ecountry masked\u003c/em\u003e were investigated in a validation study \u003cem\u003e(reference masked)\u003c/em\u003e, and SWEMWBS population norm values were presented based on representative data for the adult population living in \u003cem\u003ecountry masked (reference masked)\u003c/em\u003e. However, the SWEMWBS also has possible shortcomings regarding the comprehensiveness of the construct as well as international linking possibility and comparability since the most frequently assessed single-item indicators of mental well-being, namely, happiness, satisfaction with life, and meaning of life, are not assessed. Additionally, in view of the OECD recommendations on how to measure mental well-being (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), it is unclear whether these three items provide useful information when administering the SWEMWBS.\u003c/p\u003e \u003cp\u003eTherefore, the aim of the present research was to examine whether supplementing the SWEMWBS with the three most commonly used single-item measures of mental well-being (i.e., happiness, satisfaction with life, and meaning of life) can add substantial value to the assessment of mental well-being, thus potentially yielding a useful extension for national MHS systems or other large-scale assessments at the population level.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample and Procedure\u003c/h2\u003e \u003cp\u003eThe data for the present study were derived from a cross-sectional online survey with adults living in \u003cem\u003ecountry masked\u003c/em\u003e. The survey was conducted from November to December 2022. The study was carried out on behalf of the \u003cem\u003einstitute masked\u003c/em\u003e by the market and opinion research institute respondi gmbh and included 2,614 respondents from their in-house access panel. The sample was balanced according to age group (18\u0026ndash;29, 30\u0026ndash;44, 45\u0026ndash;59, 60\u0026ndash;74), sex by birth (male, female) and educational degree (low, moderate, high). The respondents provided their informed consent and received an incentive after participation in the form of credit points, which could be collected or redeemed for money from the panel provider. There were no missing values due to a forced choice format.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eIn this survey, the \u003cem\u003ecountry masked\u003c/em\u003e translation of the SWEMWBS (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), which includes 7 statements on how participants experienced their affect and thoughts during the last two weeks, was administered. The items were answered on a 5-point scale ranging from 1 (\u0026ldquo;none of the time\u0026rdquo;) to 5 (\u0026ldquo;all of the time\u0026rdquo;). Hereinafter, the SWEMWBS items are referred to with their label and position in the original 14-item long version of the WEMWBS (i.e., \u0026ldquo;wemwbs3\u0026rdquo; is used as an abbreviation for the third item from the original long version of the scale). Additionally, general personal agreement to the following three other commonly used single-item measures of mental well-being was assessed via a seven-point scale ranging from 1 = \u0026ldquo;strong disagreement\u0026rdquo; to 7 = \u0026ldquo;strong agreement\u0026rdquo;. Happiness (hap) was measured by the \u003cem\u003ecountry masked\u003c/em\u003e translation of \u0026ldquo;Taking all things together, I feel happy\u0026rdquo;. This item was similar to the single item assessed in the European Social Survey (ESS) as well as in several countries outside Europe (e.g., USA, Japan; (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Satisfaction with life (swl) was measured with the \u003cem\u003ecountry masked\u003c/em\u003e translation of the one-item version of the Satisfaction With Life Scale (SWLS; (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Meaning of life (mol) was measured by the \u003cem\u003ecountry masked\u003c/em\u003e translation of the item with the highest factor loading from the Meaning in Life Questionnaire (MLQ; \u0026ldquo;I know my life\u0026rsquo;s meaning\u0026rdquo;, (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The means and 95% confidence intervals of all items relevant to the present study in different sociodemographic groups (age, sex, and educational status) are shown in the supplementary file (S1).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eData analyses were conducted with R statistics version 4.3.0 (2023-04-21 ucrt., \u0026ldquo;Already Tomorrow\u0026rdquo;; (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) and R Studio version 2023.06.2\u0026thinsp;+\u0026thinsp;561 (\u0026ldquo;Mountain Hydrangea\u0026rdquo;; (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). To achieve the aim of investigating the value of adding the three single items (hap, swl, and mol) to the SWEMWBS, the following three-step analysis plan was implemented: replication of the factor structure of the 7-item SWEMWBS, exploration of the factor structure of the extended 10-item mental well-being measure, and evaluation of the three single-item measures of mental well-being. Consistency and specificity were calculated to determine the additional value of the three single-item measures of mental well-being. For all the factor analyses, the wlsmv estimator for ordinal scales was applied via the R package \u003cem\u003elavaan\u003c/em\u003e (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), which uses diagonally weighted least squares to estimate model parameters and returns robust standard errors and robust goodness-of-fit indicators based on mean- and variance-adjusted test statistics (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). The R syntax for the current data analyses is provided in the supplementary file (S2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical power.\u003c/b\u003e As the present study used data from a larger assessment protocol (e.g., also investigating coping strategies), the necessary sample size was calculated for group comparisons within a 4\u0026times;2\u0026times;3 design that required 100 participants per cell (2,400 participants). In the present study, a post hoc power analysis was conducted to analyse whether half of the final sample (2,614/2\u0026thinsp;=\u0026thinsp;1,307) was sufficient to apply a random split-half procedure and detect model misspecification in the most complex model with the R shiny app \u003cem\u003epwrSEM\u003c/em\u003e (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). We calculated power to detect model misspecification based on the root mean square error of approximation (\u003cem\u003eRMSEA\u003c/em\u003e) by means of all three power estimates described by MacCallum et al. (1996) in the most complex model in our study. According to MacCallum (1996), close fit, not close fit, and exact fit refer to the power to detect a difference between \u003cem\u003eRMSEA\u003c/em\u003e values of .05 and .08, .05 and .01, and zero and .05, respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReplication of the Factor Structure of the 7-item SWEMWBS.\u003c/b\u003e First, to replicate the factor structure of the SWEMWBS, three confirmatory factor analyses (CFAs) were conducted with one- and three-factor solutions, as well as a bifactor solution with three grouping factors, in accordance with previous evidence regarding the factor structure of the SWEMWBS \u003cem\u003e(reference masked;\u003c/em\u003e (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). In the case of misfit, we have kept the option open for additional explorative factor analysis (EFA) and to search for a better modelling option. Following the recommendations of Schermelleh-Engel et al. (2003), the model fit was evaluated based on standardized root mean square residuals (\u003cem\u003eSRMR\u003c/em\u003e), comparative fit indices (\u003cem\u003eCFI\u003c/em\u003e), and \u003cem\u003eRMSEA\u003c/em\u003e. For \u003cem\u003eRMSEA\u003c/em\u003e, values\u0026thinsp;\u0026le;\u0026thinsp;.05 were interpreted as good, values between .05 and .08 were interpreted as acceptable, values between .08 and .10 were marginal, and values\u0026thinsp;\u0026ge;\u0026thinsp;.10 were poor. \u003cem\u003eSRMR\u003c/em\u003e values of \u0026le;\u0026thinsp;.05 were interpreted as good, and values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e .10 were interpreted as acceptable. For the CFI, values \u0026ge; .97 were interpreted as good, and values \u0026gt; .95 were interpreted as acceptable.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExploration of the Factor Structure of the Extended 10-item Mental Well-being Measure.\u003c/b\u003e In a second step, we conducted an EFA of the composite measure including the single-item measures of mental well-being (hap, swl, and mol) and the seven SWEMWBS items. A random split-half sample procedure was applied, such that one random half of the sample was used for the EFA with oblique (\u0026ldquo;geomin\u0026rdquo;) rotation and the other random half was used to conduct subsequent tests of model fit. Based on the results of the EFA and assumptions regarding the factor structure of the SWEMWBS from prior evidence, different possible modelling options were compared to identify a model that captures the relationships between the SWEMWBS items and the three single items as accurately as possible while not overfitting the model to the present data. The Kaiser‒Meyer‒Olkin criterion (KMO) was used to test whether the number of observations was sufficient after the split-half procedure was performed. Parallel analysis and Eigenvalues were used to deduce the number of factors. \u003cem\u003eCFI\u003c/em\u003e, \u003cem\u003eSRMR\u003c/em\u003e, and \u003cem\u003eRMSEA\u003c/em\u003e were investigated based on the same criteria as described above. Additionally, internal consistencies of the resulting scales were indicated either by Cronbach\u0026rsquo;s alpha coefficient or by Spearman Brown\u0026rsquo;s correlation coefficient in the case of two-item scales, as suggested by Eisinga et al. (2013). Omega coefficients (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) were calculated to measure the reliability of the general factor and the whole measurement model. Reliability analyses (to obtain Cronbach\u0026rsquo;s alpha coefficients and Spearman Brown\u0026rsquo;s correlation coefficients), the KMO test, and parallel analysis were conducted using the \u003cem\u003epsych\u003c/em\u003e package (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Polychoric correlations were used, and omega coefficients were calculated directly from the estimated models. As the use of polychoric correlation has been criticized (i.e., \u0026ldquo;ordinal alpha\u0026rdquo;, (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), we also calculated Pearson correlation coefficients; when they are both reported, they are separated by a backslash (polychoric/Pearson). In the case of alpha, covariance was utilized to create standardized quasimetric scales.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEvaluation of the Additional Value of the Three Single-item Measures of Mental Well-being.\u003c/b\u003e Third, to investigate the possible redundancy or additional value of the thee single-item measures of mental well-being (hap, swl, mol), factor loadings and two forms of variance decomposition were analysed. The factor loading of the three additional single-item measures of mental well-being onto a distinct (grouping) factor was considered to indicate that they represent another domain of mental well-being not captured by the SWEMWBS and thus that they add value. Furthermore, if the three single items showed higher factor loadings on the grouping factor of the bifactor model than on the general factor, then these items were considered to add value to the SWEMWBS, and furthermore, separate analyses were considered to be appropriate. These analyses were supplemented by investigations of the proportion of shared variance of the three single items explained by the general factor (omega general) and the grouping factor (omega total). While these techniques focus on the added value of the three items together, the variance decompositions (consistency and specificity) of the individual items were also analysed based on the best model resulting from the analyses in steps one and two, as described above. To gain a more detailed picture of the partial independence of the single items from the SWEMWBS, we investigated the proportions of variance that the general factor and group factors explained together (omega total), the proportion of variance explained only by the general factor (omega general) and the proportion of variance explained by the grouping factors (omega group). Additionally, three linear regressions were conducted with each of the three single items as criteria and the SWEMWBS total score as a predictor. The coefficients of determination (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e values), which, in this case, are the same as the squared correlations between the criterion and predictor, were calculated to determine the proportion of the variances in the single items could be explained by the SWEMWBS total score. To account for the categorical nature of the item scales while still obtaining \u003cem\u003eR\u003c/em\u003e\u0026sup2; values, a second set of linear regressions was performed by regressing the SWEMWEBS total score on the dummy-coded single items.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Power\u003c/h2\u003e \u003cp\u003eThe three power estimates suggested by MacCallum et al. (1996) were close enough to one to be rounded to one given our sample size and our most complex model. Hence, the sample size of 1,307 participants could be considered sufficient for the purpose of our study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReplication of the 7-item SWEMWBS Factor Structure\u003c/h2\u003e \u003cp\u003eOverall, the one-factor SWEMWBS model showed an unacceptable fit to the data, χ\u003csup\u003e2\u003c/sup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;813.038, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; \u003cem\u003eSRMR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.047, \u003cem\u003eCFI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.929, \u003cem\u003eTLI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.894, \u003cem\u003eRMSEA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.143, 90% \u003cem\u003eCI\u003c/em\u003e [.134,.152]. The three-factor structure reflecting the three well-being components of hedonia, eudaimonia and interpersonal relationships showed a better fit to the data, χ\u003csup\u003e2\u003c/sup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;359.379, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eSRMR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.028, \u003cem\u003eCFI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.968, \u003cem\u003eTLI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.938, \u003cem\u003eRMSEA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.108, 90% \u003cem\u003eCI\u003c/em\u003e [.098, .098], similar to the results from other \u003cem\u003ecountry masked\u003c/em\u003e adult samples \u003cem\u003e(references masked).\u003c/em\u003e To avoid anomalous results from the classical symmetric bifactor model, the final bifactor model was identified as a Bifactor-(S I \u0026ndash; 1) Model and included one reference item loading only on the general factor (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). This model is a special case of the Bifactor S-1 Model with one domain (i.e., the items of one grouping factor) loading either on the general factor or the items measuring the construct in general (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). The fits of the three models using three different anchor items representing the hedonic factor (wemwbs6, wemwbs7, wemwbs11) were initially compared. The selection of one out of three reference items is based on the consideration of the highest factor loadings in the unidimensional model and ensuring at least two items per grouping factor. The bi-factor model using wemwbs6 (\u0026ldquo;I\u0026rsquo;ve been dealing with problems well\u0026rdquo;) as a reference item and correlated grouping factors showed the best fit to the present data, χ\u003csup\u003e2\u003c/sup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;65.455, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eSRMR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.011, \u003cem\u003eCFI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.992; \u003cem\u003eTLI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.968, \u003cem\u003eRMSEA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.079, 90% \u003cem\u003eCI\u003c/em\u003e [.064;.095].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExploration of the Factor Structure of the Extended 10-item Mental Well-being Measure\u003c/h2\u003e \u003cp\u003eTo investigate the factor structure of the combination of the seven SWEMWBS items with the three single items (SI; i.e., hap, swl, mol), an EFA was conducted with the first random half of the sample and subsequent confirmatory analyses with the second half. The Kaiser‒Meyer‒Olkin criterion (KMO) was .91/.91, indicating good factorizability of the data. The results from a parallel analysis as well as inspection of the scree plot suggested four factors, whereas the Kaiser\u0026ndash;Guttman criterion suggested one factor. Thus, exploratory factor analyses were conducted for one to four factors. When the item wemwbs6, which was used as a reference item in the SWEMWBS, was removed, confirmatory factor analyses resulted in a solution that could yield both three- and four-factor models. Since previous evidence regarding the factor structure of the SWEMWBS without the additional single items suggested a three-factor solution, the four-factor solution was chosen for a combined analysis of the SWEMWBS and SI. In all solutions with n\u0026thinsp;\u0026gt;\u0026thinsp;1 factors, the three single items, which were not part of the SWEMWBS, had very high factor loadings on one factor (\u003cb\u003e\u0026ge;\u003c/b\u003e\u0026thinsp;.55) and very low loadings on the other factors (\u003cb\u003e\u0026le;\u003c/b\u003e\u0026thinsp;.15). Hence, in the following, a distinct factor was modelled for the SI. An examination of the factor loadings of the SWEMWBS items in this four-factor solution suggested modelling of a second-order factor containing items 1 and 3 (hedonic), a third factor containing items 2 and 9 (interpersonal relationships), and a fourth factor with items 6, 7, and 11 (eudaimonic), thus reflecting the suggested three-factor structure of the SWEMWBS (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo further investigate the suggested factor structure resulting from EFA, three subsequent CFAs were performed with the second half of the random sample. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the results of a single-factor model (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), a model with four correlated factors (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), and a bifactor model including four correlated grouping factors uncorrelated with the general factor and using wemwbs6 as a reference item (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Model C, with one general well-being factor and four grouping factors, yielded the best results and showed an excellent fit to the data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), and Model B, with four correlated factors, showed an acceptable fit to the present data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The single-factor model did not show acceptable model fit indices. More detailed results of the CFA for both acceptable models (B, C) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eModel fit indices resulting from three confirmatory factor analyses for the Short Warwick\u0026ndash;Edinburgh Mental Well-Being Scale (SWEMWBS) and three single-item measures of mental well-being with a random split-half sample of \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,307\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA. One-factor model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB. Four-factor model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC. Bi-factor model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2261.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e537.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSRMR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCFI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e. 683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTLI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRMSEA \u003c/em\u003e\u003c/p\u003e \u003cp\u003e[90% \u003cem\u003eCI\u003c/em\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.260\u003c/p\u003e \u003cp\u003e[.250; .270.]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.101\u003c/p\u003e \u003cp\u003e[.091; .111]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.074\u003c/p\u003e \u003cp\u003e[.062; .086]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes.\u003c/em\u003e Robust fit indices are reported for three competing models. A. One-factor model displayed in Fig.\u0026nbsp;2A, B. Model with four correlated factors displayed in Fig.\u0026nbsp;2B, C. Bifactor model with four grouping factors displayed in Fig.\u0026nbsp;2C.; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;chi-square values, \u003cem\u003eSRMR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standardized root mean square residuals, \u003cem\u003eCFI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;comparative fit indices, \u003cem\u003eTLI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Tucker‒Lewis indices, \u003cem\u003eRMSEA\u003c/em\u003e [90% \u003cem\u003eCI\u003c/em\u003e]\u0026thinsp;=\u0026thinsp;Root mean square errors of approximation with corresponding 90% confidence intervals.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \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\u003eResults from confirmatory factor analysis of models B (four correlated factors) and C (four grouping factors) corresponding to Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and C with a random split-half sample of \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,307\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatent factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral (WB) ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eswl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEudaimonic ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHedonic ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpersonal ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle items (SI) ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eswl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariances\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEudaimonic ~ ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHedonic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpersonal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle items (SI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHedonic ~ ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpersonal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle items (SI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpersonal ~ ~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle items (SI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVariances\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewemwbs11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eswl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote.\u003c/em\u003e The estimates represent standardized values. SI\u0026thinsp;=\u0026thinsp;single items; hap\u0026thinsp;=\u0026thinsp;happiness; swl\u0026thinsp;=\u0026thinsp;satisfaction with life; mol\u0026thinsp;=\u0026thinsp;meaning of life.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe resulting scale reliabilities based on the random-half split sample from the best model C (i.e., the bifactor model) were acceptable, with ωh\u0026thinsp;=\u0026thinsp;.85 for the general well-being factor and ωt\u0026thinsp;=\u0026thinsp;.95 for the whole measurement model, including general and grouping factors. For the total sample, the reliabilities were α\u0026thinsp;=\u0026thinsp;.93/.91 for the general factor, α\u0026thinsp;=\u0026thinsp;.92/.89 for the three additional items, ρ\u0026thinsp;=\u0026thinsp;77/.73 for the hedonic factor, ρ\u0026thinsp;=\u0026thinsp;.85/.79 for the eudaimonic factor and ρ\u0026thinsp;=\u0026thinsp;.73/.68 for the interpersonal relationships factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the Additional Value of the Three Single-item Measures of Mental Well-being\u003c/h2\u003e \u003cp\u003eNext, shared variances were investigated to examine whether the three additional items (hap, swl, mol) add substantial information to the use of the SWEMWBS for measuring mental well-being, thus revealing whether these three single items are a useful extension in the context of the national MHS program. The first evidence indicating that SI items represent a valuable extension is related to the results from the EFA, which suggest that the three items could compose their own factor (see above). A second indicator is the omega general coefficient, which indicates the proportion of the common variance of the items identified by the grouping factors that was explained by the general factor, which was 0.84, 0.65 and 0.60 for the eudaimonic, hedonic and interpersonal factors, respectively, and only 0.43 for the single-item factor. The shared variance explained by the grouping factor indicated by the omega group ranged from 0.05 to 0.15 for the SWEMWBS factors and reached 0.50 for the single-item factor. This difference indicates that the single-item factor differs conceptually from the SWEMWBS factors. This difference is also reflected by relatively high loadings of the three items on the single-item factor compared with the relatively small loadings of the SWEMWBS items on their respective grouping factors (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, the value of supplementing the SWEMWBS with the single items was determined based on the consistency and specificity coefficients for the single-item factor based on the bifactor model (Fig.\u0026nbsp;2C). In Bi-Factor S-1 models, the choice of which items are treated as reference items and which ones are explained by an additional grouping factor changes the meaning of the general factor (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). In this case, the general factor is given meaning by the item \u0026ldquo;I\u0026rsquo;ve been dealing with problems well\u0026rdquo; and represents common parts of this item\u0026rsquo;s variance with the other items\u0026rsquo; variances that were not explained by the grouping factors (eudaimonic, hedonic, interpersonal relationships, single items). The single-item factor captured those proportions of the variances that were explained neither by the general factor nor by measurement error. The \u003cem\u003econsistency\u003c/em\u003e coefficients of true (error-free) item scores are defined as the shared variances in the true scores that are explained by the general factor, whereas the \u003cem\u003especificity\u003c/em\u003e coefficients are defined as the shared variances in the true scores that are explained by a given grouping factor (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). As consistency and specificity coefficients were calculated for the items\u0026rsquo; true score variances as opposed to the observed variables\u0026rsquo; variances, consistency and specificity coefficients pertaining to the same item added up to one (aside from rounding errors) (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Additionally, the reliability of the single-item factor was investigated, representing the shared variances of the observed items that were explained neither by the general mental well-being factor nor by the single-item factor. Specifically, 38.9% of the true score variance in the item hap was explained by the general mental well-being factor (consistency), and 61.1% was explained by the single-item factor (specificity). For the item mol, the consistency was 61.3%, and the specificity was 38.7%. For the item swl, the consistency was 41.2%, and the specificity was 58.8%.\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\u003eTotal, General and Group Omega for Comparing Explained Variances of the General and the Grouping Factors\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWell-being\u003c/p\u003e \u003cp\u003e(general)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEudaimonic well-being\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHedonic well-being\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterpersonal\u003c/p\u003e \u003cp\u003ewell-being\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSingle items\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmega total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmega general\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmega group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e These calculations are based upon a cluster solution (i.e., every item is assigned to one group); therefore, the omega general and omega groups do not add up to the omega total (Revelle, 2023).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the three linear regression analyses, the proportions of the observed variances of hap, swl, and mol that could be explained by the SWEMWBS total scores (adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2;) were 40.3%, 36.9%, and 34.1%, respectively. The item scales were treated as quasimetric and standardized. To better account for the categorical nature of the items and to create an interpretable \u003cem\u003eR\u003c/em\u003e\u0026sup2;, we also regressed the SWEMWBS score on the dummy-coded items, yielding adjusted \u003cem\u003eR\u003c/em\u003e\u0026sup2; values of 41.0%, 37.7% and 34.5% for hap, swl, and mol, respectively.\u003c/p\u003e \u003cp\u003eThus, mol was the item with the largest proportion of variance that was not explained by the latent mental well-being factor or by commonalities between the three single-item measures (40.5%). The relatively low specificity of mol indicated that the true score variance was not representative of the three additional items. Additionally, swl had a low proportion of variance that was not explained by the general mental well-being factor or by the grouping factor pertaining to the additional items (13.6%), followed by hap (3.4%). For swl, the specificity was 58.8%, and commonalities between the three additional items explained a higher proportion of the items\u0026rsquo; true scores than mol (38.7%). Among the three single items, hap had the largest squared correlation with the SWEMWBS total score (41.0%, corresponding to a correlation of .640), and the specificity of hap was very similar to that of swl (61.1% vs. 58.8%). Overall, the hap item was most representative of both the general mental well-being factor and the grouping factor.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on a \u003cem\u003ecountry masked\u003c/em\u003e sample of 2,416 adults who were balanced according to age, sex and education, the present research investigated the value of supplementing the SWEMWBS with the three most frequently used single-item measures of mental well-being (happiness, satisfaction with life, meaning of life) as well as the factor structure of this composite measure in the context of public health surveillance or other large-scale population-based studies across various countries. The aim of this study was to derive empirical evidence and practical implications for the measurement and regular reporting of mental well-being at the national level as well as to enhance the ability to conduct international comparisons while considering the theoretical and conceptual presumptions and positive mental health and well-being measurement recommendations in OECD countries (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present results replicated the good-to-acceptable fit of the original SWEMWBS three-factor structure containing the components of mental well-being of hedonia, eudaimonia, and interpersonal relationships and were preferable to the single-factor solution \u003cem\u003e(reference masked)\u003c/em\u003e (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). However, the consideration of one general well-being factor in a bi-factor model improved the fit even further, as observed in other countries (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, research on the factor structure of the SWEMWBS combined with the three single-item measures of happiness, satisfaction with life, and meaning of life suggested that the three single items can be best represented by an additional factor. Thus, a substantial proportion of the variance in the participants\u0026rsquo; answers was unique to those three additional items, suggesting the usefulness of assessing the three single items in addition to the SWEMWBS.\u003c/p\u003e \u003cp\u003eWhen interpreting these findings, it is to be considered that the three single items refer to mental well-being in general, whereas the SWEMWBS items refer to mental well-being over the past two weeks. It is therefore reasonable to assume that SWEMWBS items measure rather situational mental well-being, and the single items measure dispositional mental well-being. As addressed by latent state-trait modelling (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), current mental well-being can be disaggregated into a general trait component (i.e., individual set-point) and a situation-specific component while taking the assumption into account that short-term fluctuations may coincide to some extent with long-term changes (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). There is evidence suggesting that satisfaction with life exhibits less sensitivity to change than affective states, which have been found to be more likely to exhibit intraindividual fluctuations over time (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). However, sensitivity to changes in various mental well-being indicators has yet to be fully elucidated at the population level; a longitudinal analysis (e.g., in a population subsample) is needed to examine this phenomenon. The within-person agreement between the three general mental well-being items and SWEMWBS items referring to an actual state needs to be further investigated and compared across diverse population groups under various life circumstances. The regular national surveillance of the different mental well-being domains can enable such analyses.\u003c/p\u003e \u003cp\u003eSimilar to other recent analyses, such as one from Finland (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), the application of a bifactor model in the present analyses outperformed the four-factor correlated model, thus indicating that the combined 10-item measure of mental well-being could be best described by adding one general factor to four grouping factors (i.e., eudaimonia, hedonia, interpersonal relationships, and the three single items). Therefore, the present findings extend current evidence and emphasize the need to recognize the multidimensionality and complexity of mental well-being (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). As discussed in previous research \u003cem\u003e(masked reference)\u003c/em\u003e, even though the SWEMWBS already covers various components of mental well-being, it seems as if it cannot completely capture the construct of mental well-being. The results of this research suggest that a bundle of items cannot completely replace the commonly used single-item measures of the theoretically same mental well-being component, such as in the case of eudaimonia and meaning of life or hedonia and satisfaction with life. In contrast, the use of only single items would lead to a substantial loss of information, as noted by Ruggeri et al. (2020), within the context of a similar approach to measuring mental well-being. Considering the literature on sequencing effects of specific happiness or satisfaction questions asked before or after more general questions on the same topic (REF), we propose that the more general single-item measures analysed here provide access to the general or dispositional beliefs that people hold towards their lives. The more specific items of the SWEMWBS, on the other hand, require an active appraisal of the resondent\u0026rsquo;s life, which will then have an impact on their more general beliefs. It therefore makes sense to combine these single item measures - asked first - with more rigorous instruments to get a more comprehensive understanding of the individual's wellbeing.\u003c/p\u003e \u003cp\u003eCompared with happiness and satisfaction with life, meaning of life had the lowest item reliability, the lowest degree of similarity to the other single items, and the smallest squared correlation with the SWEMWBS total scores. These findings suggest that meaning of life might be the most useful supplement to the SWEMWBS from a statistical perspective. This interpretation is supported by a phenomenologically driven perspective, as meaning of life, which captures existential elements of what it means to be human, has been defined as one of the most significant components of eudaimonia (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Moreover, in light of evidence suggesting direct paths from meaninglessness to risky health behaviours (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), mortality (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) and suicide (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), the inclusion of meaning of life as a core indicator of eudaimonia in national mental health surveillance programs can be useful for developing preventive interventions. This is particularly relevant in view of the lack of eudaimonia assessments in international mental well-being surveillance systems (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth happiness and satisfaction with life showed higher reliability, lower consistency and lower specificity across all ten mental well-being items, thus indicating greater similarity to the SWEMWBS items. However, the results from manifest linear relations showed that considerable proportions of happiness (59.7%) and satisfaction with life (63.1%) could not be explained by the SWEMWBS total scores. From a theoretical perspective, positive affect and life satisfaction are interrelated but have the potential to provide distinct and complementary information when they are measured separately (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Thus, it may also be advantageous to consider a differentiated analysis of the cognitive (i.e., satisfaction with life) and affective aspects of the hedonic component of mental well-being. The fact that the SWEMWBS comprises affect and life satisfaction within one hedonic component highlights the potential benefit of using additional single items to address more differentiated research questions.\u003c/p\u003e \u003cp\u003eRegarding the criteria mentioned in the introduction (i.e., heterogeneity of construct, good psychometric properties, comparability, sufficient length without redundancy), the final bi-factor model can be considered appropriate, as it enables a 10-item composite measure as well as differentiated analyses of individuals components of mental well-being. However, this finding needs to be replicated by future studies, since country-specific bias, language-specific bias, or study-specific bias cannot be ruled out. In addition, the selection of reference items during bi-factor modelling was data driven and did not allow generalizability; thus, future representative studies are needed to obtain further evidence on the consistency and transferability of the results. National mental health surveillance programs will have to show if population-based levels are changing by examining external factors and by examining the composite and decomposed measures (i.e., factors and single items). These mental well-being data will have to be compared with objective well-being indicators. Despite ongoing progress and the additional insights provided by the current study, the factors relevant to and the composition of mental well-being may evolve in the face of societal changes or progressive methodology. For example, satisfaction with society has already been proposed to complement mental well-being at the macrolevel (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and the identification of core indicators \u0026ldquo;that allow individuals, communities, and societies to flourish\u0026rdquo; (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e) in the new century is still under development. Therefore, future studies should review societal dynamics as well as the risk and protective factors for mental well-being (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) to further evaluate the distinctiveness and relevancy of each component and item with respect to implementing public health strategies (e.g., targeted mental well-being promotion).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe three most commonly used single-item measures of mental well-being (i.e., happiness, satisfaction with life, meaning of life) can be used to supplement the SWEMWBS in a national MHS program. This finding is consistent with the recommendations of the OECD regarding how to measure positive mental health. The measurement of meaning of life was found to be particularly valuable, as it had the largest additional contribution to the assessment of mental well-being based on the present findings and due to the lack of eudaimonia assessments in large-scale population-based studies. Combining the 7-item SWEMWBS with these 3 single-item measures yields a composite score that can be used as a macroindicator of the latest (situational) and general (dispositional) tendencies as well as an indicator of the central components of mental well-being (i.e., hedonia and eudaimonia). Decomposing the macroindicator into its components or into single items enables more differentiated insights and comparisons with other data sources. Future research should determine the optimal measures of mental well-being in terms of construct validity, reliability, comprehensiveness, efficiency and feasibility. Furthermore, future studies should examine the sensitivity to change of various mental well-being assessments at the national level and with respect to international comparability.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCFA \u0026ndash; Confirmatory Factor Analysis\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence Interval\u003c/p\u003e\n\u003cp\u003eEFA \u0026ndash; Exploratory Factor Analysis\u003c/p\u003e\n\u003cp\u003eGDP \u0026ndash; \u0026nbsp;Gross Domestic Product\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGNH \u0026ndash; \u0026nbsp;Gross National Happiness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMHS \u0026ndash; Mental Health Surveillance\u003c/p\u003e\n\u003cp\u003eOECD - Organisation for Economic Co-operation and Development\u003c/p\u003e\n\u003cp\u003eRMSEA \u0026ndash; Root Mean Square Error of Approximation\u003c/p\u003e\n\u003cp\u003eSI \u0026ndash; single items\u003c/p\u003e\n\u003cp\u003eSRMR \u0026ndash; Standardized Root Mean Square Residual\u003c/p\u003e\n\u003cp\u003eSWEMWBS \u0026ndash; Short Warwick-Edinburgh Well-being Scale\u003c/p\u003e\n\u003cp\u003eWHO \u0026ndash; World health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of the \u003cem\u003ecountry masked\u003c/em\u003e Psychological Society (approval number 2022-10-28VA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs part of Coping study I, personal data was collected and processed that cannot be published as open data for data protection reasons. However, the data will be made available on request from the Epidemiological Data and Survey Centre of the Robert Koch Institute for non-commercial research. Requests should be directed to [email protected]. A detailed description of the data, the data scheme and sample data are published at the same time as the publication as open data and available at: Caroline Cohrdes (2024): Dealing with challenges in life - Coping study I. Berlin: Zenodo DOI: 10.5281/zenodo.13304391\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Robert Koch Institute, Berlin, Germany (project number 911610).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCC developed the study conception and design and raised the funding. Data analyses were performed by SJ and CC. The first draft of the manuscript was written by CC. Both authors read, revised and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSithey G, Thow AM, Li M. Gross national happiness and health: Lessons from Bhutan. Bull World Health Organ. 2015;93:514-.\u003c/li\u003e\n\u003cli\u003eHelliwell JF, Layard R, Sachs JD, De Neve J-E, Aknin LB, Wang S. World Happiness Report 2022. New York: Sustainable Development Solutions Network; 2022.\u003c/li\u003e\n\u003cli\u003eVik MH, Carlquist E. Measuring subjective well-being for policy purposes: The example of well-being indicators in the WHO \u0026quot;Health 2020\u0026quot; framework. Scand J Public Health. 2018;46(2):279-86.\u003c/li\u003e\n\u003cli\u003eDiener E, Suh E. Measuring quality of life: economis, social and subjective indicators. Social Indicators Research. 1997;40(1):189-216.\u003c/li\u003e\n\u003cli\u003eKroll C, Delhey J. A happy nation? Opportunities and challenges of using subjective indicators in policymaking. Social Indicators Research. 2013;114(1):13-28.\u003c/li\u003e\n\u003cli\u003eBarry MM. Addressing the determinants of positive mental health: concepts, evidence and practice. International Journal of Mental Health Promotion. 2009;11(3):4-17.\u003c/li\u003e\n\u003cli\u003eDiener E, Diener M, Diener C. Factors predicting the subjective well-being of nations. In: Diener E, editor. Culture and Well-Being: The Collected Works of Ed Diener. Dordrecht: Springer Netherlands; 2009. p. 43-70.\u003c/li\u003e\n\u003cli\u003eVeenhoven R. Comparability of happiness across nations. Journal of Happiness Studies. 2009:211-34.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Targets and indicators for Health 2020: version 4. World Health Organization. Regional Office for Europe; 2018.\u003c/li\u003e\n\u003cli\u003eOECD. OECD guidelines on measuring subjective well-being Paris: OECD Publishing; 2013.\u003c/li\u003e\n\u003cli\u003eJenkins M. Knowledge and practice mobilities in the process of policy-making: The case of UK national well-being statistics. Political Geography. 2017;56:24-33.\u003c/li\u003e\n\u003cli\u003eOECD. How to Make Societies Thrive? Coordinating Approaches to Promote Well-being and Mental Health. Paris; 2023.\u003c/li\u003e\n\u003cli\u003eEuropean Commission. Synopsis report for the commission communication on a comprehensive approach to mental health 2023.\u003c/li\u003e\n\u003cli\u003eHennessy CH, Moriarty DG, Zack MM, Scherr PA, Brackbill R. Measuring health-related quality of life for public health surveillance. Public Health Rep. 1994;109(5):665-72.\u003c/li\u003e\n\u003cli\u003eHicks S, Tinkler L, Allin P. Measuring subjective well-being and its potential role in policy: Perspectives from the UK Office for National Statistics. Social Indicators Research. 2013;114(1):73-86.\u003c/li\u003e\n\u003cli\u003eOECD. How\u0026apos;s Life in Your Region? Measuring Regional and Local Well-being for Policy Making. Paris: OECD Publishing; 2014.\u003c/li\u003e\n\u003cli\u003eDiener E. Guidelines for national indicators of subjective well-being and ill-being. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being. 2006;7(4):397-404.\u003c/li\u003e\n\u003cli\u003eOffice for National Statistics. Personal well-being in the UK: April 2020 to March 2021. Estimates of life satisfaction, feeling that the things done in life are worthwhile, happiness and anxiety at the UK, country, regional, county and local authority level. Newport, UK; 2021.\u003c/li\u003e\n\u003cli\u003eMauz E, Walther L, Junker S, Kersjes C, Damerow S, Eicher S, et al. Time trends in mental health indicators in Germany\u0026apos;s adult population before and during the COVID-19 pandemic. Frontiers in public health. 2023;11(1065938).\u003c/li\u003e\n\u003cli\u003eTov W, Diener E. Culture and Subjective Well-Being. In: Diener E, editor. Culture and well-being: The collected works of Ed Diener. Dordrecht: Springer Netherlands; 2009. p. 9-41.\u003c/li\u003e\n\u003cli\u003eLindert J, Bain PA, Kubzansky LD, Stein C. Well-being measurement and the WHO health policy Health 2010: systematic review of measurement scales. European Journal of Public Health. 2015;25(4):731-40.\u003c/li\u003e\n\u003cli\u003eDiener E, Wirtz D, Biswas-Diener R, Tov W, Kim-Prieto C, Choi D-w, et al. New measures of well-being. In: Diener E, editor. Assessing well-being. 39: Springer, Dordrecht; 2009. p. 247-66.\u003c/li\u003e\n\u003cli\u003eOECD. Measuring population mental health: Tools and current country practice. Paris: OECD Publishing; 2023.\u003c/li\u003e\n\u003cli\u003eDiener E, Ryan K. Subjective well-being: A general overview. South African Journal of Psychology. 2009;39(4):391-406.\u003c/li\u003e\n\u003cli\u003eKeyes CLM, Shmotkin D, Ryff CD. Optimizing well-being: The empirical encounter of two traditions. Journal of Personality and Social Psychology. 2002;82(6):1007-22.\u003c/li\u003e\n\u003cli\u003eTov W. Well-being concepts and components. In: Diener E, Oishi S, Tay L, editors. Handbook of well-being. Salt Lake City, UT:: DEF Publishers; 2018.\u003c/li\u003e\n\u003cli\u003eRyff CD, Singer BH. Know thyself and become what you are: A eudaimonic approach to psychological well-being. J Happiness Stud. 2008;9(1):13-39.\u003c/li\u003e\n\u003cli\u003eDiener E, Wirtz D, Tov W, Kim-Prieto C, Choi D-w, Oishi S, et al. New well-being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicators Research. 2010;97(2):143-56.\u003c/li\u003e\n\u003cli\u003eTennant R, Hiller L, Fishwick R, Platt S, Joseph S, Weich S, et al. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation. Health and Quality of Life Outcomes. 2007;5(1):63.\u003c/li\u003e\n\u003cli\u003eDiener E, Oishi S, Lucas RE. National accounts of subjective well-being. American Psychologist. 2015;70(3):234-42.\u003c/li\u003e\n\u003cli\u003eForgeard MJ, Jayawickreme E, Kern ML, Seligman ME. Doing the right thing: Measuring wellbeing for public policy. International Journal of Wellbeing. 2011;1(1):79-106.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Official records of the World Health Organization no. 2. Summary Report on Proceedings, Minutes and Final Acts of the International Health Conference Held in New York From 19 June to 22 July 1946. . Geneva; 1948.\u003c/li\u003e\n\u003cli\u003eKeyes CL. Mental health as a complete state: How the salutogenic perspective completes the picture. In: Bauer GF, H\u0026auml;mmig O, editors. Bridging occupational, organizational and public health. Heidelberg New York London Springer, Dordrecht; 2014. p. 179-92.\u003c/li\u003e\n\u003cli\u003eKobau R, Seligman ME, Peterson C, Diener E, Zack MM, Chapman D, et al. Mental health promotion in public health: perspectives and strategies from positive psychology. Am J Public Health. 2011;101(8):1-9.\u003c/li\u003e\n\u003cli\u003eKeyes CLM. Mental Illness and/or mental health? Investigating axioms of the complete state model of health. Journal of Consulting and Clinical Psychology. 2005;73(3):539-48.\u003c/li\u003e\n\u003cli\u003eWesterhof GJ, Keyes CL. Mental illness and mental health: The two continua model across the lifespan. Journal of Adult Development. 2010;17(2):110\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eVan Agteren J, Iasiello M. Advancing our understanding of mental wellbeing and mental health: The call to embrace complexity over simplification. Australian Psychologist. 2019;55(4):307-16.\u003c/li\u003e\n\u003cli\u003eKeyes CLM, Dhingra SS, Simoes EJ. Change in level of positive mental health as a predictor of future risk of mental illness. American Journal of Public Health. 2010;100(12):2366-71.\u003c/li\u003e\n\u003cli\u003eJones R, Randall C. Measuring national well-being: Quality of life in the UK 2018. Newport, UK; 2018.\u003c/li\u003e\n\u003cli\u003eOECD. How\u0026apos;s Life? 2020: Measuring Well-being. Paris: OECD Publishing; 2020.\u003c/li\u003e\n\u003cli\u003eStewart-Brown S, Tennant A, Tennant R, Platt S, Parkinson J, Weich S. Internal construct validity of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS): a Rasch analysis using data from the Scottish Health Education Population Survey. Health Qual Life Outcomes. 2009;7(15).\u003c/li\u003e\n\u003cli\u003eKoushede V, Lasgaard M, Hinrichsen C, Meilstrup C, Nielse nL, Rayce SB, et al. Measuring mental well-being in Denmark: Validation of the original and short version of the Warwick-Edinburgh mental well-being scale (WEMWBS and SWEMWBS) and cross-cultural comparison across four European settings. . Psychiatry Research. 2019;271:502-9.\u003c/li\u003e\n\u003cli\u003eSarasj\u0026auml;rvi KK, Elovainio M, Appelqvist-Schmidlechner K, Solin P, Tamminen N, Therman S. Exploring the structure and psychometric properties of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in a representative adult population sample. . Psychiatry Research. 2023;328(115465.).\u003c/li\u003e\n\u003cli\u003eShannon S, Breslin G, Prentice G, Leavey G. Testing the factor structure of the Warwick-Edinburgh Mental Well-Being Scale in adolescents: A bi-factor modelling methodology. Psychiatry research. 2020;293(113393).\u003c/li\u003e\n\u003cli\u003eLang G, Bachinger A. Validation of the German Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in a community-based sample of adults in Austria: a bi-factor modelling approach. Journal of Public Health. 2017;25(2):135-46.\u003c/li\u003e\n\u003cli\u003eDiener E, Emmons RA, Larsen RJ, Griffin S. The Satisfaction With Life Scale. J Pers Assess. 1985;49(1):71-5.\u003c/li\u003e\n\u003cli\u003eJanke S, Gl\u0026ouml;ckner-Rist A. Deutsche Version der Satisfaction with Life Scale (SWLS). Zusammenstellung sozialwissenschaftlicher Items und Skalen (ZIS). 2012.\u003c/li\u003e\n\u003cli\u003eSteger MF, Frazier P, Oishi S, Kaler M. The meaning in life questionnaire: Assessing the presence of and search for meaning in life. Journal of Counseling Psychology. 2006;53(1):80-93.\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2023.\u003c/li\u003e\n\u003cli\u003eR Core Team. RStudio: Integrated development environment for R. Boston, MA: Posit Software, PBC; 2023.\u003c/li\u003e\n\u003cli\u003eRosseel Y. lavaan: An R Package for Structural Equation Modeling. J Stat Soft. 2012;48(2):1-36.\u003c/li\u003e\n\u003cli\u003eSavalei V. Improving Fit Indices in Structural Equation Modeling with Categorical Data. Multivariate Behavioral Research. 2020;56(3):390\u0026ndash;407.\u003c/li\u003e\n\u003cli\u003eWang YA, Rhemtulla M. Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial. Adv Methods Pract Psychol Sci. 2021;4(1).\u003c/li\u003e\n\u003cli\u003eMacCallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure modeling. Psychol Methods. 1996;1(2):130-49.\u003c/li\u003e\n\u003cli\u003eSchermelleh-Engel K, Moosbrugger H, M\u0026uuml;ller H. Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Psychol Methods. 2003;8(2):23-74.\u003c/li\u003e\n\u003cli\u003eEisinga R, Te Grotenhuis M, Pelzer B. The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? International journal of public health. 2013;58(4):637-42.\u003c/li\u003e\n\u003cli\u003eMcDonald RP. Test theory: A unified treatment. . Mahwah, NJ: Lawrence Erlbaum; 2013.\u003c/li\u003e\n\u003cli\u003eRevelle W. psych: Procedures for Psychological, Psychometric, and Personality Research. R package version 2.3.12 Evanston, Illinois: Northwestern University; 2023 [Available from: https://CRAN.R-project.org/package=psych.\u003c/li\u003e\n\u003cli\u003eChalmers RP. On misconceptions and the limited usefulness of ordinal alpha. Educational and Psychological Measurement. 2018;78(6):1056-71.\u003c/li\u003e\n\u003cli\u003eEid M, Geiser C, Koch T, Heene M. Anomalous results in G-factor models: Explanations and alternatives. Psychol Methods. 2017;22(3):541-62.\u003c/li\u003e\n\u003cli\u003eEid M. Multi-faceted constructs in abnormal psychology: Implications of the Bifactor S - 1 Model for individual clinical assessment. J Abnorm Child Psychol. 2020;48:895\u0026ndash;900.\u003c/li\u003e\n\u003cli\u003eSteyer R, Schmitt M, Eid M. Latent state\u0026ndash;trait theory and research in personality and individual differences. European Journal of Personality. 1999;13(5):389-408.\u003c/li\u003e\n\u003cli\u003eEid M, Diener E. Global judgments of subjective well-being: Situational variability and long-term stability. Social Indicators Research. 2004;65(3):245-77.\u003c/li\u003e\n\u003cli\u003eRuggeri K, Garcia-Garzon E, Maguire \u0026Aacute;, Matz S, Huppert FA. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries Health Qual Life Outcomes. 2020;18(192).\u003c/li\u003e\n\u003cli\u003eRyff CD. Psychological well-being revisited: advances in the science and practice of eudaimonia. Psychotherapy and Psychosomatics. 2014;83(1):10\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eSteger MF, Fitch-Martin AR, Donnelly J, Rickard KM. Meaning in life and health: Proactive health orientation links meaning in life to health variables among american undergraduates. Journal of Happiness Studies. 2015;16(3):583-97.\u003c/li\u003e\n\u003cli\u003eKrause N. Meaning in Life and Mortality. The Journals of Gerontology: Series B. 2009;64B(4):517-27.\u003c/li\u003e\n\u003cli\u003eKleiman EM, Beaver JK. A meaningful life is worth living: Meaning in life as a suicide resiliency factor. Psychiatry Research. 2013;210(3):934-9.\u003c/li\u003e\n\u003cli\u003ePavot W, Diener E. The Satisfaction With Life Scale and the emerging construct of life satisfaction. The Journal of Positive Psychology. 2008;3(2):137-52.\u003c/li\u003e\n\u003cli\u003eSeligman M, Csikszentmihalyi M. Positive psychology. An introduction. American Psychologist. 2000;55(1):5-14.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"public health surveillance, positive mental health, well-being, life satisfaction, meaning of life, happiness, measurement, indicator","lastPublishedDoi":"10.21203/rs.3.rs-6513849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6513849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMonitoring mental well-being at thenational level has become increasingly important and widespread. However, the type and scope of mental well-being measurement are highly diverse, to the disadvantage of comparability. By taking actual recommendations on how to measure well-being at the national level into account, the present study aimed to identify a multicomponent mental well-being measure suitable for regular nationwide surveillance and cross-national comparability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a sample of 2,614 participants who were equally balanced across age (18-29, 30-44, 45-59, 60-74 years), sex (male, female) and educational groups (low, moderate, high), we investigated the additional value of combining the seven-item Short Warwick-Edinburgh Well-being Scale (SWEMWBS) with the most frequently used single-item measures of mental well-being (i.e., happiness, satisfaction with life and meaning of life, which reflect the components of hedonia, eudaimonia and interpersonal well-being, respectively). Additionally, we investigated the factor structure of the ten-item composite measure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results replicated the assumed three-factor structure of the SWEMWBS and suggested a fourth factor comprising the three single items as well as a higher-order general well-being factor. Overall, the results indicated the appropriateness of a composite score as a macroindicatorof mental well-being as well as a decomposed analysis of the hedonic and eudaimonic components or single-item measurements of mental well-being, which would enable more differentiated insights and maximal comparability. Meaning of life showed the highest added value, thus indicating its distinctiveness among the components of mental well-being.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results show how a highly comparable comprehensive yet economical measurement of mental well-being is possible in large-scale assessments. The addition of three of the most popular single items - happiness, satisfaction with life and meaning of life - to the more rigorous measure of wellbeing via the WEMWBS not only ensures comparability but also allows for a more comprehensive understanding of wellbeing. Regular public surveillance of mental well-being indicators can provide insights regarding the application, differentiation, reliability and sensitivity to change of these measurements over time and help guide public health action measures.\u003c/p\u003e","manuscriptTitle":"Shall we add some meaning? Investigating useful single-item extensions to the short Warwick-Edinburgh Mental Well-Being Scale for national public health surveillance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 06:47:25","doi":"10.21203/rs.3.rs-6513849/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":"67b0d475-5413-45f1-af06-21bd54a3fe87","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-12T02:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 06:47:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6513849","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6513849","identity":"rs-6513849","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}