‘Subjective well-being’ is a widely used term, particularly in psychology and sociology. It emerged in the late 1950s when it began to use as an indicator of quality of life (Keyes et al., 2002). Subjective well-being consists of three measurable components, which include the presence of positive and negative emotions as affective components, and a cognitive-evaluative component represented by life satisfaction (Andrews & Withey, 1976; Diener, 1984). Life satisfaction can be defined as "cognitive and global evaluation of the quality of one´s life as a whole” (Pavot & Diener, 1993). Life satisfaction is positively related to positive affect (e.g., Pavot & Diener, 2008), social support (e.g., Glaesmer et al., 2011), health (e.g., Hamplová, 2015; Ngamaba et al., 2017), and extroversion (e.g., Hayes & Joseph, 2003), and negatively related to negative affect (e.g., Larsen et al., 1985), depression (e.g., Cerezo et al., 2021; Glaesmer et al., 2011; Cerezo et al., 2021), anxiety (e.g., Arrindell et al., 1991), and stress (e.g., Arrindell et al., 1991; Blais et al., 1989; Chang & Sanna, 2001). Since relationships are considered to be one of the most important predictors of subjective well-being (e.g., Ben-Shahar, 2021; Seligman, 2012), this study also tested the relation between life satisfaction and relationships as measured by dimensions from the Aspiration Index (AI) to determine if there is a positive correlation between them.

In cross-national research, life satisfaction is mostly measured with one simple question. For example, in the European Social Survey (ESS) or the International Social Survey Programme (ISSP): “All things considered, how satisfied are you with your life as a whole nowadays?” with response options on an 11-point scale from 0 (extremely dissatisfied) to 10 (extremely satisfied); or in Barometer “On the whole, are you very satisfied, fairly satisfied, not very satisfied, or not at all satisfied with the life you lead?”.

However, a single-item measurement is associated with many methodological problems such as low reliability, the influence of contextual factors, or its position in the questionnaire. Additionally, theoretical problems arise due to conceptualization of the concept itself affecting content validity. Therefore, multi-item measurement through a battery of questions should be preferred (Diener, 1984; Diener et al., 1985).

Item generation and selection

This documented instrument is a Czech-language adaptation of the original five-item English-language Satisfaction with Life Scale (SWLS; Diener et al., 1985). These five items were developed from 48 self-report items related to global life satisfaction alongside positive and negative affects. Based on factor analysis, the instrument was reduced to five items measuring only global life satisfaction (Diener et al., 1985). The five-item version of the SWLS was then tested in three different samples with very good results in terms of reliability and validity. From the results, it was concluded that the instrument is suitable for measuring global satisfaction (Diener et al., 1985). The SWLS was subsequently translated into more than 30 languages and can be used in own studies without copyright restrictions.

The translation process from English to Czech was carried out using the TRAPD approach (Translation, Review, Adjudication, Pretesting, and Documentation; Harkness et al., 2003). This documented Czech version is based on the original translation by Lewis et al. (1999) and Navrátil and Lewis (2006), but minor changes have been made to the wording of the items and to the name of the middle category on the response scale as a result of the translation process and pilot testing. The translation into Czech language was carried out by three independent experts and then the most suitable wording was assessed and selected. The Czech translation was then be pilot tested on a non-representative sample of the Czech population (N = 220) via an online questionnaire. All stages of the translation process were documented.

Samples

The Czech-language adaptation of the SWLS was based on data from an online survey (CASI) of the Czech population aged 18 to 69 and carried out within the MARVEL project. The survey was conducted from the panel of the research agency Data Collect s.r.o. Fielding took place in August 2021. Respondents were selected by the quota method according to gender, age, education, region, and size of the place of residence. Included in the analysis were respondents who answered all five items and who were not identified as invalid cases based on a scale-wise method according to longstrings in other instruments in the survey; namely Life Goal Inventory (LGI), Aspiration Index (AI), Human Values Scale (HVS). Specifically, cases where the respondent reported the same value in the LGI, AI, or HVS for all scale items were identified as invalid. This approach resulted in the total exclusion of 40 cases (4.0 %). The final research sample was N = 960 in the main survey and N = 286 in the retest, which was performed approximately two weeks after the main collection (M = 16 days).

Table 2 depicts in detail the socio-demographic characteristics of the research sample. 

Table 2

Socio-demographic Characteristic of the Research Sample

Item analyses

The factor structure of the SWLS was tested with a confirmatory factor analysis (CFA). Parameters were estimated using the robust maximum likelihood estimator (MLR) in Mplus 7.2. The SWLS is mostly considered to be a unidimensional construct (model 1). However, many studies have shown that a single-factor structure with correlated residuals between the fourth and fifth items is preferable (model 2), because these items refer semantically to the past, while the first three refer to the present (Clench-Aas et al., 2011; Jovanović, 2017). Therefore, both models with fixed latent variance to 1 were tested. The result for model 1 is presented in Figure 1 and for model 2 in Figure 2. The results indicated that both models fit the data well (Model 1: RMSEA = 0.080, CFI = 0.980, χ² (5) = 35.542, p < .001; Model 2: RMSEA = 0.045, CFI = 0.995, χ² (4) = 11.781, p < .020), although model 2 achieves better results and is preferred. 

Figure 1

Model 1: One-factor Structure of the Construct

Note. Standardized coefficients, RMSEA = .080, CFI = .980, SRMR = .022, c²(5) = 35.542, p = <.001, N = 960.

Figure 2

Model 2: Modified One-factor Structure of the Construct with Correlated Residuals between swl04 and swl05

Note. Standardized coefficients, RMSEA = .045, CFI = .995, SRMR = .011, c²(4) = 11.781, p = .020, N = 960.

The psychometric properties of the SWLS were also tested through item response theory (IRT) in R 4.1.2 software (package mirt). Item parameters (in Table 4) were estimated by graded response model (GRM; Samejima, 1969). The results show (Figure 3), that the SWLS performs very good functioning, especially between −2 and 2.5 of the latent trait continuum (θ). All items have a high value for the discrimination parameter (a), which, according to Baker (2001), can be described as “very high.” However, it is evident from Figure 4, as well as from Table 4, that the discrimination parameter for swl05 is lower, which corresponds with the results of the CFA, and its lower factor loading in comparison with other items. From the category characteristic curves (CCC) in Figure 5, it can be concluded that the number of response scale is redundant, as respondents are unable to distinguish sufficiently between the middle categories (this is most evident for swl05). For further research, I would recommend testing the SWLS with a response scale with five or six categories.

Figure 3

The Total Information Function (TIF) and Standard Error (SE) for the SWLS

Figure 4

The Item Information Function (IIF) for Each Item of the SWLS

Figure 5

The Category Characteristic Curve (CCC) for Each Item of the SWLS

Item parameters

Table 3 displays the means, standard deviations, skewness, and kurtosis for each SWLS item. IRT item parameters estimated by GRM are shown in Table 4.

Table 3

Means, Standard Deviations, Skewness, and Kurtosis of the Manifest Items

Note. Scale ranging from 1 (strongly disagree) to 7 (strongly agree), N = 960.

Table 4

Item Parameters for the SWLS (GRM IRT)

Objectivity

The SWLS is a standardized measurement instrument presented to respondents as a battery of items with the fixed response categories and written instructions for completion. The calculation and interpretation of the total score is set by the authors (Pavot & Diener, 2008). Moreover, the entire questionnaire was standardized and pilot-tested. As a result, a high objectivity of application, evaluation, and interpretation of the SWLS is achieved.

Reliability

The reliability of the SWLS was tested using Cronbach's alpha, McDonald's omega and test-retest correlations. As Table 5 shows, the estimates for Cronbach’s alpha and McDonald’s omega was .88 and test-retest correlations was .79, which in all cases are high (desirable) values corresponding to results in other studies (Clench-Aas et al., 2011; Pavot & Diener, 2008) that allow the use of this scale for research purposes.

Table 5

Reliability Estimates for the SWLS

Note. N = 960, retest N = 286. The time interval between test and retest ranged between 11 and 21 days (M = 16 days).

Validity

The validity of the Czech version of the SWLS scale was achieved by testing the relation (correlation) between scores on the scale and on other relevant scales. Specifically, the following scales: (a) Positive and Negative Affect Schedule (PANAS) (Watson et al., 1988); (b) the Aspiration index (AI) dimensions of Personal growth, Relationships, Community, Health, Wealth, Fame, and Image (Klusmann et al., 2005; Utvær et al., 2014); and (c) the Human Values Scale (HVS) dimensions of Self-direction, Power, Universalism, Achievement, Security, Stimulation, Conformity, Tradition, and Hedonism (Schwartz, 1992). The results are shown in Table 6. 

Table 6

Correlations of the SWLS with Relevant Scales

Note.  Pearson correlation coefficient. ^*p < .05, ^**p < .01.

The practical significance of the validity correlation coefficients can be evaluated according to Gignac and Szodorai (2016) as small (r = .10), medium (r = .20), and large (r = .30). Regarding the relation with the PANAS scale, the most relevant in terms of the validity of the concept of life satisfaction, a large positive correlation was found between life satisfaction and positive affect, and a large negative correlation with negative affect. These results are consistent with the results of other studies (e.g. Cerezo et al., 2022; Di Fabio & Gori, 2021; Gouveia et al., 2009; Howell et al., 2010; Janke & Glöckner-Rist, 2012; Watson et al., 1988).

In terms of the relation between life satisfaction and relationships, the hypothesis of their positive correlation was supported: A medium to large positive correlation was found between life satisfaction and the dimensions of Relationships, and Community of the Aspiration Index (AI). Moreover, the positive correlation was found with all other dimension of the AI: a medium positive correlation with Personal Growth, Health, and Image; and a small positive correlation with Fame, and Wealth.

In addition, a positive relationship occurred with some dimensions from the Human Values Scale (HVS). Specifically, a medium positive correlation was found between life satisfaction and the dimensions of Hedonism, and Stimulation; a small to medium positive correlation with the dimension of Achievement; and a small positive correlation with the dimensions of Self-Direction, Benevolence, Power, and Universalism.

Descriptive statistics

Table 7 provides the means, standard deviations, skewness, and kurtosis of the SWLS total score for the total population and separately for gender, age groups and education levels.

Table 7

Reference Ranges of the SWLS total score for the Total Population and Separately for Gender, Age Groups and Education Levels

Note. SWLS total score ranging from 5 to 35, N = 960.

Further quality criteria

To test fairness between gender, age groups and education levels for the SWLS, measurement invariance was investigated by confirmatory factor analysis. Three hierarchical levels of measurement invariance between groups was tested: (1) configural, which indicates that all groups have the same factor structure; (2) metric, which requires that groups have equivalent factor loadings; and (3) scalar, which requires both equivalent factor loadings and intercepts (Meredith, 1993; Steenkamp & Baumgartner, 1998; Vandenberg & Lance, 2000).

Evaluation of measurement invariance was based on criteria suggested by Chen (2007). If the change (Δ) between two invariance levels is > .010 for CFI and > 0.015 for RMSEA, it indicates that the higher level of invariance should be rejected.

Table 8 shows the results of measurement invariance across groups, which was done in software Mplus 7.2. Modified one-factor structure of the construct with correlated residuals between swl04 and swl05 was identified by fixing the factor loading of swl01 to 1. Scalar invariance was achieved in all cases as the CFI and RMSEA values reached desirable values (CFI >= .900, RMSEA <= .080; West et al., 2012) and, in particular, as their difference between invariance levels was .010 for CFI and 0.006 for RMSEA (or even less). This means that the latent means and (co)variances of life satisfaction can be compared across gender, age groups and education levels (Chen, 2008; Vandenberg & Lance, 2000).

Table 8

Measurement Invariance for the SWLS Across Groups

Note. Model 2: modified one-factor structure of the construct with correlated residuals between swl04 and swl05. Maximum Likelihood Robust (MLR) estimation method. RMSEA = Root Mean Square Error of Approximation, CFI = Comparative Fit Index, Δ = change/difference, X² = Chi-Square, df = Degrees of freedom, N = 960.

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Acknowledgement

This work was supported by the grant SVV 2020 – 2022 (260556) realized at the Charles University, Faculty of Arts.

• Radka Hanzlová, Faculty of Arts, Charles University / Institute of Sociology of the Czech Academy of Sciences, Jilská 1, 110 00 Prague, Czech Republic, e-mail: radka.hanzlova@soc.cas.cz