The sample is part of a study about the Quality of life in relation to Well-Being. The sample comprised 2272 Greek adults of the general population. Males were 37% and females 63% with an average age of M = 35.54 years (SD = 12.35). More than half of the sample was single (51%) or married (41%), followed by divorced (8%). The majority of the participants (59%) did not have children, or had from 1 (14%) to 2 children (22%), or more (5%). Most participants had a Bachelor (42%), finished high-school (24%), had a postgraduate degree (19%), were university students (14%), or received primary education (1%).

Data were collected with the assistance of 150 psychology students from 2 different psychology courses (Psychometrics and Research Methods in Psychology). Students forwarded a link to an electronic test battery (in Google Forms© format) to 15 - 20 adults from their social environment. Students were extra-credited for their participation in the study. All the fields in the form of the digital battery were required to eliminate missing values. A letter was included in the test battery to inform participants about the purpose of this study. The process of data collection was the following. First, students received a short, free workshop on the administration of digital psychology questionnaires by the research team. Then, a period of pilot-testing the digital test battery followed to track any flaws in the digital procedure and to record the time required to complete the battery (approximately 15 minutes). Finally, after successful pilot testing, students were provided with a link to the official study.

The sample was split in three parts to study the construct validity of SPANE-12 and SPANE-8 in 3 different subsamples (see Table 1). Research was applied in two levels: 1) on three subsamples (EFA, CFA1 and CFA2) to evaluate construct validity and confirm it; 2) on the full sample (Total sample), to evaluate strict measurement invariance across gender. This is a construct validation procedure we called the “3-faced construct validation method” (see also Kyriazos et al., in press ). More specifically, in the above method the sample is randomly split in three parts (20%, 40%, 40%). In all three emerging subsamples (20% EFA, 40% CFA1, 40% CFA2) the threshold for sample to variable ratio (N/p) is set to 5/1 for EFA (Osborne & Costello, 2004; Singh et al., 2016) and to 10/1 for CFA (DeVellis, 2017) . After splitting the sample, the method consists of the following phases. In the first phase, an EFA is carried-out in the 20% of the sample to establish a structure. Then, in the second phase, a CFA (CFA1) follows in the second part of the sample (40%) evaluating multiple models. By default, in this phase it is a good practice to evaluate at a minimum, a single-factor, multifactor, Bifactor, and higher-order CFA models. Next, in the third phase, the optimal model from the CFA 1 will be replicated in a different sample of equal power (40%). The CFA 2 is designed to crosscheck the findings of the CFA 1. Finally,

EFA = Exploratory Factor Analysis, ICM-CFA = Independent Cluster Model Confirmatory Factor Analysis. EFA Bifactor = Bifactor Exploratory Factor Analysis.

an MGCFA finalizes the validation procedure to establish measurement invariance across gender or/and age using the optimal model as a baseline model. If either the CFA 2 or the measurement invariance fails to revalidate the optimal CFA1 model, then the second best model of the CFA 1 is crosschecked etc. (see Table 1 for an overview of the method). Note that if the sample is not adequate, then only CFA is implemented (keeping the N/p ratio threshold > 10/1) and measurement invariance with the Multiple Indicators Multiple Causes method (MIMIC) follows. The above steps is a generally suggested process of construct validation (c.f. Kyriazos et al., in press ).

Regarding the factor analysis methods used in this study, in the first subsample (EFA subsample, N_EFA = 452), Exploratory Factor Analysis (EFA) and Bifactor Exploratory Factor Analysis (Bifactor EFA) were carried out. Independent Cluster Model Confirmatory Factor Analysis (ICM-CFA), Bifactor Confirmatory Factor Analysis (Bifactor CFA), Exploratory Structural Equation Modeling (ESEM), and Bifactor Exploratory Structural Equation Modeling (Bifactor ESEM) were applied in the second subsample (CFA1 subsample, N_CFA1 = 910), testing alternative solutions for SPANE-12 and SPANE-8. The optimal model that emerged from the CFA1 subsample was cross-validated in a different subsample of equal power (CFA2). Then, a multi-group CFA (MGCFA) was carried out in the entire sample (N = 2272) using the CFA 2 optimal model as a baseline model, to test for strict measurement invariance across gender (see Table 1 for an overview of this method). A reliability analysis (α and ω) followed in the entire sample. AVE Convergent validity and Convergent/Discriminant validity based on correlation analysis were performed in the total sample using measures of mental distress, well-being, positivity and quality of life. Next, a Bifactor CFA Subjective Well-being Model was evaluated, using SPANE to measure affect. Finally, normative data were calculated over the entire sample. Data were collected electronically on Google Forms® and were analyzed with SPSS Version 25 (IBM, 2017) , Stata Version 14.2 (StataCorp, 2015) and MPlus Version 7.0 (Muthen & Muthen, 2012) .

The full sample contained N = 2272 cases. All variables had no missing values because all the digital test-battery fields were required (see details in Procedure section).

To validate the factor structure of SPANE, the total sample (N = 2272) was randomly split into three parts (20%, 40% and 40%) to implement a construct validation procedure we call the “3-faced construct validation method” (see also Kyriazos et al., in press ). Although splitting a sample is generally suggested for the development of new models (Schumacker & Lomax, 2015) , the above analysis strategy was adopted because this sample was large enough to maintain adequate power after splitting. Specifically, the sample-to-variable ratio (N/p) for the EFA subsample (N_EFA = 452), CFA1 subsample (N_CFA1 = 910) and CFA2 subsample (N_CFA2 = 910) was 37.67/1 and 75.83/1 respectively. An acceptable sample-to-variable ratio can range anywhere from 10/1 (Osborne & Costello, 2004; Singh et al., 2016) to 20/1 (Schumacker & Lomax, 2015) . Alternatively, a sample having 500 to 1000 cases is generally regarded either satisfactory or excellent (Comrey & Lee, 1992) .

The data both in the total sample and the three subsamples violated the normality assumption. Kolomogorov-Smirnov tests (Massey, 1951) on each individual item of SPANE-P and SPANE-N were statistically significant with no exception, p = .000, indicating that items were non-normally distributed for the total sample. Specifically, Kolomogorov-Smirnov for the total sample ranged from D (2272) = .172, to D (2272) = .279, for the EFA subsample from D (452) =. 183, to D (452) = .232, for the CFA1 subsample from D (910) = .187 to D (910) = .327 and finally for the CFA2 subsample from D (910) = .178 to D (910) = .287, p = .000 in all samples. Next, we evaluated multivariate normality with the following four tests: 1) Mardia’s multivariate kurtosis test (Mardia, 1970) ; 2) Mardia’s multivariate skewness test (Mardia, 1970) ; 3) Henz-Zirkler’s consistent test (Henze & Zirkler, 1990) , and 4) Doornik-Hansen omnibus test (Doornik & Hansen, 2008) . The null hypothesis was rejected for all four tests (all p values < .0001), suggesting a violation of multivariate normality in all four samples (Total, EFA subsample, CFA1 subsample, CFA2 subsample). The values of all multivariate tests are presented in Table 2.

EFA was applied with the MLR estimator (c.f. Muthen & Muthen, 2012 ). MLR is

All p values < .0001.

a rescaling-based estimation method appropriate for non-normal distributions and―unlike similar estimators―it produces standard errors and chi-square test (Wang & Wang, 2012) . Additionally, MLR is appropriate for sample-sizes ranging from for small to medium (Bentler & Yuan, 1999; Muthen & Asparouhov, 2002; Wang & Wang, 2012) , like this split sample. The factors were rotated with Geomin factor rotation in the standard EFA model. Additionally, for the EFA Bifactor model, the technique proposed by Jennrich and Bentler (2011) was applied. EFA model fit was evaluated by the standards proposed by Hu & Bentler (1999) and Brown (2015) : RMSEA (≤ .06, 90% CI ≤ .06), SRMR (≤ .08), CFI (≥ .95), TLI (≥ .95), and the chi-square / df ratio less than 3 (Kline, 2016) . Multiple indices used together, provided a more conservative and reliable estimation of the results (Brown, 2015) .

For both SPANE-12 and SPANE-8, a total of 5 EFA models were evaluated in the EFA subsample (N = 472). For SPANE-12, the following models were tested. MODEL 1a was proposed by Diener et al. (2010) , and contains only the 6 positive items of SPANE-12 (SPANE-P). Respectively, MODEL 1b contains only the 6 negative items of SPANE-12 (SPANE-N; Diener et al., 2010 ) to test the assumption that Positive and Negative affect are independent measures of PA and NA ( Crawford & Henry, 2004) . MODEL 2 is a bi-dimensional EFA model with SPANE-P and SPANE-N in two separate factors (proposed by Singh et al., 2017 ; attributed to Diener et al., 2010 ). Generally, this EFA model also served as a benchmark for the subsequent Bifactor EFA model. MODEL 3, is a Bifactor EFA model (Jennrich & Bentler, 2011) . According to Reise et al. (2007) the evaluation of a Bifactor model is generally recommended when evaluating construct validity (c.f. Hammer & Toland, 2016 ). The Schmid-Leiman method (Schmid & Leiman, 1957) had been restricted to Bifactor CFA models until Jennrich & Bentler’s (2011) proposed the Exploratory Bifactor Analysis method applied here. Additionally, this Bifactor EFA model attempts to reproduce the hierarchical EFA structure for affect proposed by Tellegen et al. (1999) with a General Happiness/Sadness factor and PA and NA as specific factors. Generally, Bifactor models can successfully reproduce higher order structures (Howard et al., 2016) . SPANE-12 models showed the following fit. The fit for the models 1a and 1b was decent with all measures within acceptable or almost acceptable bounds. For the 2-factor EFA model (MODEL 2) and EFA Bifactor model (MODEL 3), fit measures (see Table 2) achieved the prerequisite limits, Chi-square = 135.82, Chi-square/df = 3.16, RMSE = .069, CFI = .952, and TLI = .926, SRMR = .033, factor loadings .358 - .850, and factor correlation −.724.

For SPANE-8 the following alternative models were tested. MODEL 1a and 1b contained only the 6 items of SPANE-P and SPANE-N respectively ( Diener et al., 2010 for SPANE-12), to test the assumption that PA and NA are independent constructs (Watson et al., 1988; Crawford & Henry, 2004) . MODEL 2 was the two factor structure with SPANE-P and SPANE-N in two different factors. Regarding the fit of the above SPANE-8 models, all fit indices showed acceptable fit. The 2-factor solution achieved the following fit measures (Chi-square = 17.64, Chi-square/df = 1.36, RMSE = .028, CFI = .995, and TLI = .989, SRMR = .018, factor loadings .390 - .893, and factor correlation −.584). Table 3 contains the fit statistics for all models.

In this phase of the 3-faced construct validation method, we examined both SPANE-12 and SPANE-8 dimensionality further with Confirmatory Factor Analysis in a different subsample (40%, N = 910). The following criteria were applied to evaluate CFA model fit (Hu & Bentler, 1999; Brown, 2015) : RMSEA (≤ .06, 90% CI ≤ .06), SRMR (≤ .08), CFI (≥ .95), TLI (≥ .95), and the chi-square /df ratio less than 3 (Kline, 2016) .

For SPANE-12 the following 9 models were tested (see Table 4). MODEL 1 has both positive and negative experiences collapsed into one factor. It is a standard practice to test the assumption of maximum parsimony with a unidimensional ICM-CFA model (Crawford & Henry, 2004; Brown, 2015) . However, in this case, the model is also based on the hypothesis that PA and NA are opposite ends of a single dimension (Russell & Carroll, 1999; Crawford & Henry, 2004) . MODEL 2 has positive and negative experiences separated into two factors (also proposed by Giuntoli et al., 2017; Singh et al., 2017; Rahm et al., 2017; Silva & Caetano, 2013; Sumi, 2014 ). MODEL 3 is a variation of MODEL 2 with three error covariances added. MODEL 4 is a model with multiple, theoretically based, error covariances (see Figure 1(a)) to reflect the four content categories of SPANE items: 1) SPANE-P General Positive (items 1, 3, 5); 2) SPANE-P Specific Positive (items 7, 10, 12); 3) SPANE-N General Negative (items 2, 4, 6); 4) SPANE-N Specific Negative (items 8, 9, 11), see Figure 1. A model with multiple error covariances was also proposed by Li et al. (2013) . MODEL 5 is an ESEM model with positive and negative experiences separated into two factors to reproduce the bipolar structure of SPANE (never tested before). ESEM (Asparouhov & Muthen, 2009) is an integration of EFA, CFA, and SEM. ESEM potentially resolves misspecifications and inflated factor loadings, inherent in ICM-CFA due to constraining secondary factor loadings to zero (Marsh et al., 2014) . MODEL 6 is a variation of MODEL 5 with error covariances added. MODEL 7 is a Bifactor (Schmid & Leiman, 1957) CFA model suggested by Li et al. (2013) .

Estimator = MLR, EFA Factor rotation = Geomin, EFA Bifactor rotation = BiGeomon. *G = General Factor, S = Specific factor, H/S = Happiness/Sadness. **SPANE-8 N (N4) = Bad, Sad, Afraid, Angry. **SPANE-8 P (P4) = Pleasant, Happy, Joyful, Contented.

Estimator = MLR. *G = General Affect Factor, S = Specific Positive and Negative factors, H/S = Happiness/Sadness. **SPANE-8 N (N4) = Bad, Sad, Afraid, Angry. **SPANE-8 P (P4) = Pleasant, Happy, Joyful, Contented.

MODEL 8 is a Bifactor ESEM (c.f. Reise, 2012; Marsh et al., 2013 ), model (never tested before). MODEL 9 is a 4-factor structure (never tested before). Factor 1 has 3 general positive items, factor 2 has 3 specific positive items, factor 3 has 3 general negative items and factor 4 has 3 specific negative items. This structure incorporates the additional differentiation of items in general and specific (Diener et al., 2010) , apart from positive and negative (see Figure 1(b)). A higher order model was not tested due to misspecification, i.e. under-identification (Wang & Wang, 2012; Brown, 2015) .

Regarding the fit of the SPANE-12 models tested, MODEL 1 had a poor fit. MODEL 2 showed a tolerably acceptable fit. MODEL 3 was the variation of MODEL 2 with error covariances permitted in items 3 - 5, 7 - 12, 2 - 8, showing a very good fit (Chi-square = 150.20, Chi-square/df = 3.07, RMSE = .048, CFI = .975, and TLI = .967, SRMR = .026, factor loadings for SPANE-12 P .647 - .801, and for SPANE-N .384 - .828, and factor correlation −.724). In MODEL 4 error covariances were permitted in specific SPANE-P and SPANE-N items, based on the categorization of items either as General or Specific (see Figure 1(a)). The fit of this model was acceptable, however MODEL 4 was less parsimonious than MODEL 3. MODELS 5 and 6 were the ESEM models, with also an acceptable fit. The 12 items of SPANE-12 were allowed to load on both Positive and Negative Experiences factors in these two ESEM (Asparouhov & Muthen, 2009) models. Nevertheless, in this case there were no cross-loadings and the fit of ICM and ESEM models was equally satisfactory. Note, the primary factor loadings of 2-factor ESEM model ranged from .647 - .805 (SPANE-P) and .418 - .895 (SPANE-N) and factor correlation was −.628. Therefore, ESEM is most suitable than a corresponding ICM-CFA model when fitting the data better, otherwise the ICM-CFA model is preferable as more parsimonious (Marsh et al., 2014) . MODELS 7 and 8 were a Bifactor (Schmid & Leiman, 1957) CFA and a Bifactor ESEM (c.f. Reise, 2012 ) model respectively (Figure 1) with also adequate fit to the data. However, Bifactor models always tend to show acceptable fit, thus they have been criticized as doubtful (Joshanloo, Jose, & Kielpikowski, 2017; Joshanloo & Jovanovic, 2016) . MODEL 9 showed a decent fit with measures within acceptable limits but with less parsimonious structure (see Figure 1(b)).

Regarding the 2-factor ICM-CFA model tested for SPANE-8, it showed a good fit to the data, Chi-square = 26.05, Chi-square/df = 1.37, RMSE = .020, CFI = .996, and TLI = .994, SRMR = .020. The factor loadings for SPANE-8 P ranged from .639 to .771, and for SPANE-8 N from .413 to .811, with a factor correlation of −.662. Table 4 contains all the fit statistics for the 10 alternative CFA models tested for both SPANE versions.

To summarize the findings in the CFA 1 subsample (N = 910), the following models showed an overall optimal fit after considering fit measures, factor loadings and factor intercorrelations: 1) for SPANE-12, the 2-factor ICM-CFA with 3 error covariances permitted in items 3 - 5, 7 - 12 and 2 - 8 (MODEL 3); 2) for SPANE-8, the 2-factor ICM-CFA. This bi-dimensional structure has also been proposed by many studies as optimal (Giuntoli et al., 2017; Singh et al., 2017; Rahm et al., 2017; Silva & Caetano, 2013; Sumi, 2014) , supporting these preliminary findings further.

After determining that the 2-factor ICM-CFA model was the optimal structure emerging from the CFA 1 subsample (N = 910), for both SPANE-12 and SPANE-8, a crosscheck of this model followed to verify model fit in a second subsample (CFA2, N = 910) of equal power. See Table 1 for details about the “3-faced construct validation method” (see also Kyriazos et al., in press ).

Table 5 contains the results of this crosscheck CFA for SPANE-12 and SPANE-8. The 2-factor ICM-MODEL model showed adequate fit with all indices

Estimator = MLR. *SPANE-8 N (N4) = Bad, Sad, Afraid, Angry. *SPANE-8 P (P4) = Pleasant, Happy, Joyful, Contented.

falling within the acceptable range with good fit for SPANE-12 (see Figure 2(a)) and achieving an equally good fit on all indices for SPANE-8 (see Figure 2(b)). Moreover, fit measures for both versions were also very stable across the two subsamples. Factor loadings were also adequate, ranging for SPANE-12 P from .656 to .811 and for SPANE-12 N from .467 to .815, with factors intercorrelated at −.676. SPANE-8 factor loadings were equally adequate, both for SPANE-8 P (.678 - .800) and N (.493 - .802), with factors intercorrelated at −.617 (see Figure 2). All items loaded on the intended latent factors. After taking into consideration the above findings (fit statistics and factor loadings) for both SPANE versions, we used this successfully cross-validated 2-factor structure as a baseline model to evaluate measurement invariance across gender.

For both SPANE versions, we examined measurement invariance across gender in the entire sample (N = 2272). The criteria used were ΔCFI ≤ −.01, and ΔRMSEA ≤ .015 (Chen, 2007) . First, for SPANE-12, gender invariance of the 2-factor ICM CFA model with error covariances was tested separately in each gender group (males, N = 832 versus females, N = 1440), to establish a baseline model. This model had a good fit for males (Chi-square = 185.57, Chi-square/df = 3.71, RMSEA = .057, CFI = .965) and equally good for females (Chi-square = 202.77, Chi-square/df = 4.22, RMSEA = .047, CFI = .978). Then, this bi-dimensional baseline solution was tested in both gender groups simultaneously (M1) showing adequate fit (see Table 6), thus configural invariance was supported. Next, to examine weak invariance, factor loadings were constrained to equality. As shown in Table 6, ΔCFI and ΔRMSEA for this constrained model (M2) confirmed weak invariance. Then, intercepts were forced to be equal (M3), and both ΔCFI and ΔRMSEA suggested that strong invariance was supported. Finally, to test strict invariance, the ultimate test of measurement invariance (Wang & Wang, 2012) , error variances were forced to be equal. ΔCFI and ΔRMSEA suggested strict measurement invariance.

MLR estimator was used in all models. Error covariances for men and women differ across models (c.f. Wang & Wang, 2012 ).

Then, to test measurement invariance for SPANE-8 the above procedure was repeated. The baseline bi-dimensional optimal solution presented very good fit for males (Chi-square 31.69, Chi-square /df = 1.76, RMSEA = .030, CFI = .992), and equally adequate fit for females (Chi-square = 43.694, Chi-square /df = 2.30, RMSEA = .030, CFI = .993). Then, this model was evaluated in both gender groups at the same time. The fit of this model (M1) confirmed configural invariance (see Table 6). Then, factor loadings (MODEL 2), intercepts (MODEL 3) and error variances (MODEL 4) were consecutively constrained to equality. Model comparison indicated that fit measures were adequate with ΔCFI and ΔRMSEA within acceptable range for MODEL 2 to 1 (weak invariance), tolerably acceptable range for MODEL 3 to 2 (strong invariance), and equally acceptable for MODEL 4 to 3 (strict invariance).

Next, we evaluated the reliability and validity of SPANE-12 and SPANE-8 over the entire sample (N = 2272) using the following measures; 1) Cronbach’s alpha (α; Cronbach, 1951 ) to examine internal consistency of the responses. Alpha values above .70 are generally acceptable (Hair et al., 2010) , and above .80 adequate (Kline, 2000; Nunnally & Berstein, 1994) ; 2) Omega Total coefficient (ω total; McDonald, 1999; Werts, Lim, & Joreskog, 1974 ) to examine construct reliability (Hoque et al., 2017) . Omega corresponds either to variance accounted by all factors or by each latent factor separately (Brunner et al., 2012). For omega a, value of .70 or greater is acceptable (Hair et al., 2010) ; 3) Average Variance Extracted (AVE; Fornell & Larcker, 1981 ) to evaluate convergent validity. Malhotra & Dash (2011) note that ω alone is weak, potentially allowing an error variance as high as 50%. Therefore, AVE in combination with ω coefficient offers a more conservative estimation of convergent validity (Malhotra & Dash, 2011). The threshold for AVE is .50 (Fornell & Larcker, 1981; Hair et al., 2010; Awang et al., 2015) .

The internal reliability of SPANE-12 total was significant. On average, for each SPANE-12 factor, alpha coefficients were adequate (M = .88). Similarly, for SPANE-8 total internal reliability was satisfactory and on average alpha coefficients for the two factors were also good (M = .80); see Table 7. Likewise, omega reliability coefficient for the total SPANE-12 was substantial (.94) and for each factor on average satisfactory, and equal to alpha, M = .88. This value suggests that mean percentage variance explained by each SPANE-12 factor score is 88%. For the total SPANE-8, omega coefficient was also significant (.90) and for each factor on average sufficient (M = .81), indicating a mean percentage of explained variance per factor score of 81% (see Table 7). Regarding AVE, Total AVE was significant for both versions. For SPANE-12 per factor AVE was on average adequate, M = . 56, and for SPANE-8 equally adequate, M = .53 (Table 7).

The relationship between SPANE with other measures was examined in the entire sample (N = 2272). Dimensions evaluated are separated into four groups: 1) mental distress (with the 3 dimensions of DASS-9; Yusoff, 2013; Kyriazos, Stalikas, Prassa, & Yotsidi, in press ); 2) well-being, including WEMWBS (Tennant et al., 2007) , MHC-SF (Keyes et al., 2008) , Flourishing Scale (FS; Diener et al., 2010 ), and Satisfaction with life Scale (SWLS; Diener et al., 1985 ); 3) positivity

SPANE-8 N = Bad, Sad, Afraid, Angry. SPANE-8 P = Pleasant, Happy, Joyful, Contented.

scales comprising trait HOPE (Snyder et al., 1991) , Brief Resilience Scale (BRS; Smith et al., 2008 ), Meaning in life Questionnaire (MLQ; Steger et al., 2006 ) and Gratitude 6 Questionnaire (McCullough et al., 2002) ; 4) aspects of life quality like Physical health, Psychological health, Social Relations and Environment (from WHOQOL-BFEF; WHOQOL Group, 1998a, 1988b ). We examined results both for SPANE-12 and SPANE-8 as well as correlation between them (presented in Table 8). Concerning the correlations between SPANE and Group 1 (Mental Distress Scales), correlations of SPANE-12 P, SPANE-12 N and SPANE-12 B with DASS-9 ranged from moderate to strong, M = −.38, M = .45 and M = −.47 respectively. The correlations of SPANE-8 P, SPANE-8 N and SPANE-8 B with DASS-9 were also moderately strong (M = −.37, M = .44, M = −.46).

The correlations between SPANE-12 P and Group 2 (Well-Being Scales) were on average strong (M = .60). Note that WEMWB and Emotional WB showed the strongest correlations. The correlations between SPANE-12 N and Group 2 were on average moderate to strong (M = −.44). SPANE-12 B had a strong, positive correlation with Group 2 (M = .56). Similarly, correlations between SPANE-8 P and Group 2 were on average strong (M = .58), with WEMWBS and Emotional WB being the strongest. The correlations between SPANE-8 N and Group 2 were negative and on average moderately strong (M = −.40). The correlations between SPANE-8 B and Group 2 were positive and on average strong (M = .55).

Concerning the correlations between SPANE with Group 3 (Positivity Scales), SPANE-12 P, SPANE-12 N and SPANE-12 B had on average a moderate to strong correlation of .40, −.31, and .38 respectively. SPANE-8 P, SPANE-8 N and SPANE-B had on average moderate correlation with Group 3 (M = .39, M = −.28, M = .37). A consistent pattern of relations emerged in this Group for both SPANE versions, since HOPE and Search for Meaning where invariably at the highest and lowest positive range for both SPANE P and SPANE B. Likewise, Resilience and Search for Meaning had the strongest negative and weakest positive relation with SPANE-N for both SPANE versions.

All p values were < .01. P = SPANE POSITIVE, N = SPANE NEGATIVE, B = SPANE AFFECT BALANCE. Bold indicates correlations of equivalent factors on SPANE-12 and SPANE-8.

Regarding the correlations of SPANE with Group 4 (Quality of life Scales), SPANE-12 P, SPANE-12 N and SPANE-12 B had on average strong correlations (M = .51, M = −.42, M = .51). SPANE-8 P, SPANE-8 N and SPANE-8 B also had on average a strong correlation with Group 4 (M = .50, M = −.39, M = .50). Finally, about the correlations between the two SPANE versions, SPANE-12 P, SPANE-12 N and SPANE-12 B were strongly correlated with their equivalent SPANE-8 factors with values as high as .97, .97 and .98 respectively, all p < .01 (see Table 8).

For the measurement of subjective well-being, empirical literature (Linley et al., 2009) suggests using two constructs: 1) positive/negative affect, and 2) life satisfaction. The current study examined if the above empirical hierarchical model of subjective well-being (empirically by Linley et al., 2009 ; also postulated by Diener, Suh, Lucas, & Smith, 1999 as quoted in Ruini, 2017 ) is confirmable in the current sample with a Bifactor CFA structure. Bifactor structure is an alternative to original proposed higher-order CFA model (Linley et al., 2009) because it successfully reproduces higher order structures (Howard et al., 2016) which in this case is inapplicable due to misspecification (Wang & Wang, 2012; Brown, 2015) .

To test this Bifactor SWB model (see Figure 3) we used the Satisfaction with Life Scale (Diener et al., 1985) to measure life satisfaction and the two newly validated SPANE versions (SPANE-12 and SPANE-8) to measure affect. Specifically, we tested two Bifactor CFA models. The first had a general subjective well-being factor and SWLS, SPANE-12 P and SPANE12-N as specific factors (Figure 3(a)). The second had a general subjective well-being factor and SWLS, SPANE-8 P and SPANE 8-N as specific factors (Figure 3(b)). The SWB model of both SPANE versions had an adequate fit (see Table 9), with all measures within acceptable limits, and chi-square/df on the verge of acceptability. The satisfactory fit of the model to the data suggests that the proposed subjective well-being model is tenable using SPANE-12 or SPANE-8 to measure the affect component (see Figure 3).

For both SPANE versions tested the means and ranges for the SPANE-P, SPANE-N and SPANE-B dimensions for the total sample (N = 2272) are presented in Table 10.

Means are not informative of individual scores, given the non-normality of the data (Crawford & Henry, 2004) . Therefore, Table 10 converts SPANE-12 and SPANE-8 scores to percentiles. For SPANE-12 the 50% of the respondents of this sample in Positive Experiences, Negative Experiences and Affect Balance scored ≤ 23, ≤ 15 and ≤ 8 respectively. For the original SPANE, more than half of the respondents in US scored 22 on SPANE-P (51%), 15 on SPANE-N (52%)

Estimator = MLR, *SPANE-8 N = Bad, Sad, Afraid, Angry, *SPANE-8 P = Pleasant, Happy, Joyful, Contented.

and 7 on SPANE-B (53%), as reported by Diener et al. (2010) . In turn, for SPANE-8 the 50% of the respondents of our sample had a score ≤ 15, ≤ 10 and ≤ 5 in Positive Experiences, Negative Experiences and Affect Balance respectively (see Table 10).