www.jsaa.ac.za Journal of Student Affairs in Africa, 11(1), 109–128 DOI: 10.24085/jsaa.v11i1.4327 | ISSN 2307-6267 | www.jsaa.ac.za 109 Research article Can life satisfaction be measured fairly for different groups of South African first-year students? Testing the Satisfaction with Life Scale Clarisse van Rensburg1 & Karina Mostert2 1 Clarisse van Rensburg, JOBJACK, Cape Town, South Africa. Email: clarisse0206@gmail.com. ORCid: 0000- 0003-4573-6575 2 Prof. Karina Mostert, North-West University, Potchefstroom Campus, South Africa. Email: Karina.Mostert@ nwu.ac.za. ORCid: 0000-0001-5673-5784 Article history: Received 1 November 2022 | Accepted 31 May 2023 | Published 14 August 2023 KEYWORDS Satisfaction with Life Scale, item bias, differential item functioning, measurement invariance, first- year university students, student affairs ABSTRACT Student well-being has gradually become a topic of interest in higher education, and the accurate, valid, and reliable measure of well-being constructs is crucial in the South African context. This study examined item bias and configural, metric and scalar invariance of the Satisfaction with Life Scale (SWLS) for South African first-year university students. A cross- sectional design was used. A sample of 780 first-year South African university students was included. Confirmatory factor analysis, differential item functioning measurement invariance, and internal consistency were tested. A one-factor structure was confirmed. Item 1 of the SWLS was particularly problematic concerning bias (uniform and non-uniform bias). Measurement invariance was established; however, Item 1 was again problematic, resulting in partial metric and scalar invariance. The scale was reliable (Cronbach’s α was 0.83; McDonald’s omega (ω) was 0.83). This study contributes to the limited research on the specific psychometric properties of the SWLS in a diverse higher education setting. The results could assist with valid and reliable measurements when developing interventions to enhance student well-being. RÉSUMÉ Le bien-être des étudiants est progressivement devenu un sujet d’intérêt dans l’enseignement supérieur, et la mesure précise, valide et fiable des constructions liées au bien-être est cruciale dans le contexte sud-africain. Cette étude a examiné le biais des items et l’invariance de la configuration, de la métrique et de l’échelle de l’échelle de satisfaction de vie (SWLS) pour les étudiants de première année d’université en Afrique du Sud. Une conception transversale a été utilisée. Un échantillon de 780 étudiants sud-africains de première année a été choisi. Une analyse factorielle confirmatoire, le fonctionnement différentiel des items, l’invariance de mesure et la cohérence interne ont été testées. Une structure à un facteur a été confirmée. L’item 1 de la SWLS était particulièrement problématique en termes de biais (biais uniforme et non uniforme). L’invariance de mesure a été établie ; cependant, l’item 1 était à nouveau problématique, entraînant une invariance métrique et scalaire partielle. L’échelle était fiable http://www.jsaa.ac.za Journal of Student Affairs in Africa, 11(1), 109–128 DOI: 10.24085/jsaa.v11i1.4327 | ISSN 2307-6267 | www.jsaa.ac.za110 MOTS-CLÉS Biais des items, échelle de satisfaction de vie, étudiants de première année d’université, fonctionnement différentiel des items, invariance de mesure, œuvres estudiantines, services étudiants Introduction Life satisfaction is an essential indicator of individual and social well-being and includes the perception that one is moving towards accomplishing significant life goals (Esnaola et al., 2019; Jovanović, 2019). Life satisfaction is also crucial to first-year university students, as they face a period of uncertainty in which they idealise the values of their lives, prepare for the world of work, and actively explore their adult roles (Gökalp & Topal, 2019). Studies show significant relationships between high levels of life satisfaction, taking on more responsibility, experiencing less stress and emotional loneliness and more resilience in overcoming academic challenges (Gökalp & Topal, 2019; Rode et al., 2005). There is also a relationship between life satisfaction and satisfaction with educational experiences, healthy relationships, self-esteem (Chow, 2005), engagement, motivation and study satisfaction (Lewis et al., 2011; Wach et al., 2016). Conversely, there are also associations between low levels of life satisfaction, perceived stress, anxiety, and burnout (Alleyne et al., 2010; Serin et al., 2010), higher levels of impaired concentration and deteriorated academic performance (Rode et al., 2005). One of the most widely used scales in assessing life satisfaction is the Satisfaction with Life Scale (SWLS) (Diener et al., 1985). Periodic assessments are needed to accurately establish and measure well-being in the higher education sector, including measures of life satisfaction such as the SWLS (Băcilă et al., 2014). However, various factors challenge fair psychological testing in South Africa, such as the distribution of socio-economic resources, diversity in culture and language, and education and employment statuses (Foxcroft & Roodt, 2009). Psychological testing and other similar assessments are governed in South Africa by the Employment Equity Act No. 55 of 1998, Section 8 (President of the Republic of South Africa, 1998), which states that assessments are prohibited unless they can scientifically be shown to be reliable and valid, can be applied fairly to all ethnic groups and cultures, and are not biased against any person or group. In addition, psychological assessments in South Africa are controversial due to past unfair, undiscerning, and biased use (Laher & Cockcroft, 2014). Historically, assessment practices in South Africa have been known to use measurement instruments from Western countries, often without any adaptation to South Africa’s multi-cultural and diverse context (Blokland, 2016). Consequently, the majority of South Africa’s population was excluded from these assessment practices, as they tend to cater mainly for the (l’α de Cronbach était de 0,83 ; l’oméga de McDonald était de 0,83). Cette étude contribue à la recherche limitée sur les propriétés psychométriques spécifiques de la SWLS dans un contexte diversifié d’enseignement supérieur. Les résultats pourraient aider à obtenir des mesures valides et fiables lors de l’élaboration d’interventions visant à améliorer le bien-être des étudiants. van Rensburg, C., & Mostert, K. (2023). Can life satisfaction be measured fairly for different groups of South African first-year students? Testing the Satisfaction with Life Scale 111 Western, educated, industrialised, rich, and developed population sectors (Laher & Cockcroft, 2013; Laher & Cockcroft, 2014). Therefore, questions related to test bias and equivalence are raised when applying adapted and imported measurement instruments in South Africa (Teresi & Fleishman, 2007; Van De Vijver & Rothmann, 2004). It is essential to distinguish between the concepts of item bias and equivalence. Bias refers to the presence of annoyance factors (items invoking added abilities or traits) when making cross-group comparisons (Schaap, 2011; Van De Vijver & Rothmann, 2004). In addition, another source of bias can be test items themselves – also referred to as item bias or differential item functioning (DIF) (Van De Vijver & Rothmann, 2004). Item bias signifies that the meaning of one or more scale items is not being understood identically across groups, is not applicable to a specific group or that semantic differences are present in how items are conceptualised (Cleary & Hilton, 1968). When respondents have the same standing on the underlying construct and are from different cultures but have different mean scores on the item, this could reflect actual differences in the construct or that item bias is present (Van De Vijver & Rothmann, 2004). A distinction should be made between uniform and non-uniform bias. Uniform bias refers to the likelihood of similar responses for one group being systematically higher or lower at specific endorsement levels (the underlying construct) compared to other groups (Swaminathan & Rogers, 1990; Teresi & Fleishman, 2007). Non-uniform bias refers to the difference in the likelihood of similar answers across groups varying across all levels of endorsement (Swaminathan & Rogers, 1990; Teresi & Fleishman, 2007). The most common sources of item bias include ambiguities in the original item, poor item translation, the influence of cultural specifics (connotations or nuisance factors) associated with the wording of the item, and low appropriateness and familiarity of the item content in some cultures (Van De Vijver & Rothmann, 2004). Invariance (or equivalence) indicates whether a construct is interpreted and understood similarly across different groups, which is essential for cross-group comparisons (Mellenbergh, 1989; Milfont & Fischer, 2010; Putnick & Bornstein, 2016; Van De Schoot et al., 2012). Configural invariance indicates the extent to which the factor structure of a measure can be replicated across different groups, which is crucial for meaningful comparisons (He & Van De Vijver, 2012; Schaap, 2011; Van De Vijver & Rothmann, 2004). Metric invariance involves equal factor loadings, across groups, for similar items (i.e. when individuals from different cultures who speak different languages complete the same questionnaire and conceptualise the construct the same way) (Milfont & Fisher, 2010; Morton et al., 2019). Scalar invariance establishes whether a test score has a similar meaning in its interpretation regardless of cultural background (He & Van De Vijver, 2012; Laher, 2008). In essence, concerning the psychometric properties of assessment instruments, item bias and invariance testing will aid in establishing whether measures are fair to use for different sub-groups in the specific South African context (Schaap, 2011). Therefore, this study emphasizes the concepts of bias and invariance testing to ensure the validation of existing instruments in cross-cultural groups to ensure meaningful comparisons across sub-groups (Van De Vijver & Rothmann, 2004). Journal of Student Affairs in Africa, 11(1), 109–128 DOI: 10.24085/jsaa.v11i1.4327 | ISSN 2307-6267 | www.jsaa.ac.za112 While scholars recently investigated the psychometric properties of the SWLS among South African samples, including an adult population (Schutte et al., 2021) and primary and secondary school teachers (Pretorius & Padmanabhanunni, 2022), studies testing item bias and equivalence are limited. The present study explores the psychometric properties, specifically item bias and invariance (configural, metric and scalar), of the SWLS in a sample of first-year South African students. Literature Measurement and psychometric properties of the Satisfaction with Life Scale The SWLS measures a single life satisfaction construct that could indicate levels of satisfaction with life throughout one’s life span (Tomás et al., 2015). The scale displays favourable psychometric properties, has been validated in various countries and is translated into numerous languages, including Spanish, Portuguese, Dutch, and German (Diener et al., 1985; Gouveia et al., 2009). Studies testing bias and invariance for the SWLS are scarce and report mixed results. Regarding the item bias of the SWLS, a study conducted on a Turkish university student sample concluded that the items of the SWLS are unbiased across gender groups (Avcu, 2021). However, Hultell and Gustavsson (2008) found that Item 4 and Item 5 are sensitive to age. With regards to configural invariance, most researchers have found the SWLS to be invariant across gender, age (Glaesmer et al., 2011; Hinz et al., 2018; Lorenzo-Seva et al., 2019; Wu & Yao, 2006), and countries such as Spain and Portugal (Atienza González et al., 2016), Germany (Glaesmer et al., 2011), Columbia (Ruiz et al., 2019), the United States, and Brazil (Zanon et al., 2014). However, other studies report configural invariance for gender groups, albeit not for age groups (Shevlin et al., 1998; Wu et al., 2009). In addition, studies report metric invariance for different age groups (Pons et al., 2000; Glaesmer et al., 2011), gender groups (Emerson et al., 2017; Hinz et al., 2018; Jovanović, 2019; Moksnes et al., 2014; Ruiz et al., 2019), and cultures (Atienza González et al., 2016; Emerson et al., 2017; Jovanović & Brdar, 2018). However, Zanon et al. (2014) presented evidence against metric invariance between undergraduates from the United States and Brazil, specifically for Items 4 and 5. Studies on the SWLS support the notion that scalar invariance is supported across gender groups (Clench-Aas et al., 2011; Hultell & Gustavsson, 2008; Shevlin et al., 1998; Zanon et al., 2014), age groups (Durak et al., 2010; Gouveia et al., 2009; Tomás et al., 2015; Wu et al., 2009), and several European countries (e.g., Austria, Bosnia and Herzegovina, Croatia, Montenegro, and Serbia; Jovanović & Brdar, 2018). However, some studies reported insufficient evidence for scalar invariance across age groups (Clench-Aas et al., 2011; Hultell & Gustavsson, 2008) and countries (Atienza González et al., 2016; Whisman & Judd, 2016). van Rensburg, C., & Mostert, K. (2023). Can life satisfaction be measured fairly for different groups of South African first-year students? Testing the Satisfaction with Life Scale 113 Method Research procedure and participants Before data collection commenced, permission was obtained from the relevant university to conduct research. An ethics application was submitted and approved, focusing specifically on anonymity, confidentiality, and voluntary participation (ethics number: NWU-HS-2014-0165). A web-based survey link was sent via email and posted on the university’s online platform for first-year modules. The study’s goal, purpose, and value to the university were explained. The sample consisted of 780 first-year students aged 18 to 20. Of the 780 participants, 38.8% indicated that they spoke Afrikaans, 33.1% indicated that they spoke Setswana, and 6.2% indicated that they spoke Sesotho (three of the eleven official language groups in South Africa). The sample consisted of three campuses: Campus 1 (38.3%), Campus 2 (50.5%), and Campus 3 (9.7%). Concerning gender, 61.8% of the participants identified as women, and 38.2% identified as men. Measuring instrument The SWLS was developed by Diener and colleagues (1985) and aimed to measure a single life satisfaction construct that could indicate levels of satisfaction with life throughout one’s life span (Tomás et al., 2015). Participants are asked five questions (e.g. “The conditions of my life are excellent”). A seven-point Likert-type scale is used, ranging from 1 (strongly disagree) to 7 (strongly agree). Pavot and Diener (1993) confirmed the scale’s reliability, reporting Cronbach’s coefficient alphas ranging from 0.79 to 0.89. Statistical analysis The statistical analyses were conducted using Mplus 8.6 (Muthén & Muthén, 2021). Before bias and invariance were tested, confirmatory factor analysis (CFA) was used to confirm the one-factor structure of the SWLS. Maximum likelihood with robust standard errors (MLR) was used in the CFA due to the small samples in some groups to supplement the item bias analyses. The following fit indices and cut-off scores were used to estimate the measurement model’s goodness-of-fit: the traditional chi-square (χ²) statistic, the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), the root mean square error of approximation (RMSEA) and the standardised root mean square residual (SRMR). Values of 0.90 and above indicate an acceptable fit for CFI and TLI (Byrne, 2001). Regarding the RMSEA scores, various researchers suggest using a cut-off score below 0.05 as the ‘golden rule of thumb’ to indicate model fit; however, values between 0.05 and 0.08 are considered to be an acceptable fit (Browne & Cudeck, 1993; Chen et al., 2008; Hu & Bentler, 1999; Steiger, 1989). Concerning SRMR, a cut-off value of 0.05 was used (Browne & Cudeck, 1993; Hu & Bentler, 1999). Differential item functioning (DIF) was used to test for the presence of item bias by using the lordif package (Choi et al., 2011) in RStudio (https://www.rstudio.com/). The following formulas were used and compared to test for uniform and non-uniform bias, using ordinal logistic regression to generate three likelihood-ratio χ² statistics (Choi et al., 2011): Journal of Student Affairs in Africa, 11(1), 109–128 DOI: 10.24085/jsaa.v11i1.4327 | ISSN 2307-6267 | www.jsaa.ac.za114 Model 0: logit P(uⅈ ≥ k) = αk Model 1: logit P(uⅈ ≥ k) = αk + β1 * ability Model 2: logit P(uⅈ ≥ k) = αk + β1 * ability + β2 * group Model 3: logit P(uⅈ ≥ k) = αk + β1 * ability + β2 * group + β2 * ability * group Based on the formulas mentioned above, uniform bias can be indicated with a significant difference at p < 0.01 when comparing logistic models 1 and 2 (x212; df = 1), and non-uniform bias, when comparing models 2 and 3 (x223 df = 1) (Choi et al., 2011). Total DIF is indicated with a significant difference at p < 0.01 when comparing models 1 and 3 (x213; df = 2) (Choi et al., 2011). The magnitude of DIF can be quantified using the pseudo-McFadden R2 statistic, which can be classified as either negligible (< 0.13), moderate (between 0.13 and 0.26), or large (> 0.26) (Zumbo, 1999). However, DIF can be under-identified by only using the pseudo-McFadden R2 statistic (Jodoin & Gierl, 2001; Kim et al., 2007). Therefore, to identify uniform DIF, the difference in the coefficient from Models 1 and 2 was used – with differences of 10% indicating a practically meaningful effect (Crane et al., 2004; Maldonado & Greenland, 1993). Lower than 5% and even 1% thresholds are also considered (Crane et al., 2007). In this study, a threshold of 5% was considered. Measurement invariance was investigated for three different groupings: (1) language (Afrikaans, Sesotho, and Setswana, three indigenous South African language groups), (2) campus (three campuses were included), and (3) gender (men and women). A multi-group analysis framework was used to test for the configural invariance model (analogous factor structure), metric invariance model (similar factor loadings), and scalar invariance model (similar intercepts). CFI and RMSEA values were used to indicate measurement invariance. CFI is considered a good fit with values > 0.90 and better if the values are > 0.95. Regarding RMSEA, cut-off values of 0.05 and 0.08 are considered acceptable (Van De Schoot, 2012). Changes in CFI (ΔCFI) were used as they are less susceptible to the effects of changes in df (Shi et al., 2019). A ΔCFI value (> -0.01; that is a worsening of the model fit according to CFI) between two nested models indicates that the added group constraints have led to a poorer fit; in other words, invariance has not been achieved, and the more constrained model is rejected. Additionally, it is essential to note that there are many small variances among groups regarding factor loadings or intercepts; therefore, partial measurement invariance (whether metric or scalar) can be achieved by freeing the loading or intercepts of items (Cheung & Rensvold, 2002; Preti et al., 2013; Van De Schoot et al., 2015). Finally, the internal consistency of the SWLS was determined using Cronbach’s alpha coefficients (McCrae et al., 2011; Revicki, 2014). Values where α ≥ 0.70 were considered acceptable (Nunnally, 1978). In addition, McDonald’s omega was calculated and reported for a more accurate estimation of internal consistency (Cortina et al., 2020). Reliability coefficients ≥ 0.80 indicate good internal consistency (Kline, 2015). van Rensburg, C., & Mostert, K. (2023). Can life satisfaction be measured fairly for different groups of South African first-year students? Testing the Satisfaction with Life Scale 115 Results Factorial validity Before DIF and invariance testing, the factorial validity of the SWLS was tested with confirmatory factor analysis (CFA) to determine the model’s goodness-of-fit. Even though it is best practice to test alternative measurement models (Marsh et al., 1998), given the limited items of the SWLS and based on the various studies supporting the five-item SWLS measuring one underlying factor, a one-factor measuring model was tested in this study. Also, with only five items, two factors would have one just-identified factor (3 items) and another under-identified factor (2 items). The results indicate that the unidimensional structure of the SWLS is deemed to be a good fit for the data (χ² = 806.844; df = 10; CFI = 0.964; TLI = 0.928; RMSEA = 0.086; SRMR = 0.035). The CFI value was the preferred index of choice to determine goodness- of-fit (Shi et al., 2019). Table 1 indicates the results for the standardised loadings of the items for the latent variables of the SWLS. Table 1: Standardised factor loadings for the latent variables of the SWLS Item Item text Loading S.E. p Item 1 In most ways my life is close to my ideal 0.543 0.037 0.001 Item 2 The conditions of my life are excellent 0.831 0.024 0.001 Item 3 I am satisfied with my life 0.879 0.016 0.001 Item 4 So far I have gotten the important things I want in life 0.684 0.031 0.001 Item 5 If I could live my life over, I would change almost nothing 0.533 0.035 0.001 Notes: S.E. = standard error; all p-values < 0.001 Factor loadings (λ) can either be classified as high (0.70), medium (0.50), or small ( 0.30) (Shevlin et al., 1998). The results show that the factor loadings for the SWLS ranged from medium to high. Item bias DIF was used to determine the item bias of the SWLS. Uniform and non-uniform bias were tested across different language, campus, and gender groups. Table 2 indicates the DIF of the SWLS. Table 2 indicates that Item 1 was problematic for language and campus groups, and Item 3 for language groups. No DIF was detected across gender groups. Table 2 shows that Item 1 has uniform, non-uniform and total bias, based on the likelihood-ratio χ² difference testing across models 1, 2 and 3 (p < 0.01). Figure 1 illustrates the bias present in Item 1 across the different language groups. Journal of Student Affairs in Africa, 11(1), 109–128 DOI: 10.24085/jsaa.v11i1.4327 | ISSN 2307-6267 | www.jsaa.ac.za116 Table 2: Summary of DIF for the SWLS Group Item x212 x 2 13 x 2 23 β1 R 2 12 R 2 13 R 2 23 Language Item 1 0.0000 0.0000 0.0000 0.2085 0.0673 0.0893 0.0220 Item 2 0.7623 0.7867 0.5545 0.0030 0.0003 0.0011 0.0007 Item 3 0.0000 0.0000 0.1010 0.0483 0.0135 0.0160 0.0025 Item 4 0.6712 0.1448 0.0488 0.0020 0.0004 0.0036 0.0032 Item 5 0.5904 0.7034 0.5706 0.0063 0.0005 0.0010 0.0005 Campus Item 1 0.0000 0.0000 0.0000 0.1201 0.0459 0.0627 0.0167 Item 2 0.6982 0.8404 0.7035 0.0001 0.0004 0.0008 0.0004 Item 3 0.1220 0.2405 0.5263 0.0102 0.0031 0.0041 0.0010 Item 4 0.4245 0.7227 0.8363 0.0013 0.0013 0.0016 0.0003 Item 5 0.0446 0.1706 0.9105 0.0051 0.0037 0.0039 0.0001 Gender Item 1 0.0823 0.0202 0.0287 0.0052 0.0015 0.0038 0.0023 Item 2 0.6606 0.8625 0.7482 0.0000 0.0001 0.0001 0.0000 Item 3 0.9325 0.4037 0.1789 0.0000 0.0000 0.0008 0.0008 Item 4 0.9949 0.9358 0.7158 0.0000 0.0000 0.0001 0.0001 Item 5 0.0239 0.0772 0.8894 0.0004 0.0021 0.0021 0.0000 Notes: = chi-square of model 1 compared to model 2; x 213 = chi-square of model 1 compared to model 3; x 223 = chi-square of model 2 compared to model 3; β1 = change in beta coefficient; R212 = pseudo-Mcfadden R 2 of model 1 compared to model 2; R 213 = pseudo-Mcfadden R 2 of model 1 compared to model 3; R223 = pseudo-Mcfadden R 2 of model 2 compared to model 3 420 0.0 0.5 1.0 1.5 2.0 It em S co re theta Pr(χ212,2) = 0, R 2 12 = 0.0673, Δ(β1) = 0.2085 Pr(χ213,4) = 0, R 2 13 = 0.0893 Pr(χ223,2) = 0, R 2 23 = 0.022 AFR SOT TSW Item True Score Functions – Item 1 -2-4 420 0.0 0.5 1.0 1.5 2.0 It em S co re theta Differences in Item True Score Functions -2-4 420 0.0 0.5 1.0 1.5 2.0 Pr ob ab ili ty theta Item Response Functions -2-4 2.37, 0.33, 1.54 1.2, -0.58, 1.48 0.96, -0.86, 1.13 420 0.0 0.5 1.0 1.5 2.0 Si ze theta Impact (Weighted by Density) -2-4 Figure 1: Graphical display of Item 1, which shows uniform and non-uniform DIF for language groups van Rensburg, C., & Mostert, K. (2023). Can life satisfaction be measured fairly for different groups of South African first-year students? Testing the Satisfaction with Life Scale 117 The top left plot in Figure 1 indicates the item-true score functions based on group- specific item parameter estimates. The slope of the function for the Afrikaans group was significantly higher than that of the Sesotho and Setswana groups, indicating non-uniform DIF. The bottom left plot in Figure 1 compared the item response functions across the three language groups, which are noticeably different between the three language groups. The expected impact of DIF on scores is indicated by the top left plot in Figure 1 as the absolute difference between the item true-score functions (Kim et al., 2007). A difference can be seen at approximately = 0.50; however, the density- weighted impact (as indicated by the bottom right plot) can be interpreted as small. Even though the effect of bias can be regarded as small when taking the pseudo-McFadden R2 statistics (R2 < 0.13) into account, the change in beta coefficient is larger than 5% ( = 20.85%), suggesting the effect is practically meaningful. Item 1 was also problematic for campus groups. Based on the results in Table 2 and the plots in Figure 2, all likelihood-ratio χ² tests were statistically significant (< 0.01; < 0.01; < 0.01), which indicates the presence of both uniform and non-uniform DIF. Similar to the results for DIF between language groups, the results in Table 2 and the graphs in Figure 2 demonstrate that even though the impact of bias can be regarded as small (pseudo-McFadden R2 statistics < 0.13), the change in the beta coefficient ( = 12.01%) indicates that the impact of the bias has a practically meaningful effect. In addition, statistically significant bias was detected in Item 3 (Figure 3) across the different language groups, with significant likelihood ratio χ² tests when comparing Models 1 and 2 ( < 0.01) and Models 1 and 3 ( < 0.01), indicating mainly uniform bias. Noticeable differences between the language groups can be seen in the plots. However, 420 0.0 0.5 1.0 1.5 2.0 It em S co re theta Pr(χ212,2) = 0, R 2 12 = 0.0459, Δ(β1) = 0.1201 Pr(χ213,4) = 0, R 2 13 = 0.0627 Pr(χ223,2) = 0, R 2 23 = 0.167 AFR SOT TSW Item True Score Functions – Item 1 -2-4 420 0.0 0.5 1.0 1.5 2.0 It em S co re theta Differences in Item True Score Functions -2-4 420 0.0 0.5 1.0 1.5 2.0 Pr ob ab ili ty theta Item Response Functions -2-4 1.03, -0.79, 1.23 2.22, 0.18, 1.47 1.23, -0.03, 1.37 420 0.0 0.5 1.0 1.5 2.0 Si ze theta Impact (Weighted by Density) -2-4 Figure 2: Graphical display of Item 1, which shows uniform and non-uniform DIF for campus groups Journal of Student Affairs in Africa, 11(1), 109–128 DOI: 10.24085/jsaa.v11i1.4327 | ISSN 2307-6267 | www.jsaa.ac.za118 regarding the magnitude of these items, the density-weighted impact seen in the bottom right plot and pseudo-McFadden R2 statistic values < 0.13 and Δβ1 coefficient smaller than 5% indicate that the significant practical effect is negligible. Measurement invariance Table 3 shows the measurement invariance across the language, campus, and gender groups included in this study. Table 3: Summary of measurement invariance analysis for the SWLS Group χ2 df CFI ΔCFI RMSEA ΔRMSEA Language Configural 52.85 20 0.962 ― 0.102 ― Metric 89.58 32 0.933 -0.029 0.106 0.004 Scalar 178.36 44 0.843 -0.090 0.138 0.032 Partial metric 60.67 31 0.965 0.003 0.078 -0.024 Partial scalar 67.39 39 0.967 0.002 0.068 -0.010 Campus Configural 37.29 15 0.975 ― 0.076 ― Metric 70.70 23 0.946 -0.029 0.090 0.014 Scalar 154.54 31 0.861 -0.085 0.125 0.035 Partial metric 47.84 22 0.971 -0.004 0.068 -0.008 Partial scalar 50.28 25 0.972 0.001 0.063 -0.005 Gender Configural 41.36 10 0.963 ― 0.090 ― 420 0.0 0.5 1.0 1.5 2.0 It em S co re theta Pr(χ212,2) = 0, R 2 12 = 0.0135, Δ(β1) = 0.0483 Pr(χ213,4) = 0, R 2 13 = 0.016 Pr(χ223,2) = 0.101, R 2 23 = 0.0025 AFR SOT TSW Item True Score Functions – Item 1 -2-4 420 0.0 0.5 1.0 1.5 2.0 It em S co re theta Differences in Item True Score Functions -2-4 420 0.0 0.5 1.0 1.5 2.0 Pr ob ab ili ty theta Item Response Functions -2-4 5.16, -1.02, -0.68, -0.14, 0.9 2.91, -1.07, -0.7, -0.27, 0.6 2.6, -1.36, -0.83, -0.19, 0.59 420 0.0 0.5 1.0 1.5 2.0 Si ze theta Impact (Weighted by Density) -2-4 Figure 3: Graphical display of Item 3, which shows uniform DIF for language groups van Rensburg, C., & Mostert, K. (2023). Can life satisfaction be measured fairly for different groups of South African first-year students? Testing the Satisfaction with Life Scale 119 Group χ2 df CFI ΔCFI RMSEA ΔRMSEA Metric 47.89 14 0.960 -0.003 0.079 -0.011 Scalar 59.98 18 0.950 0.010 0.078 -0.001 Notes: χ2 = chi-square; df = degrees of freedom; CFI = Comparative fit index; ΔCFI = delta (change in) CFI; RMSEA = Root mean square error of approximation; ΔRMSEA = delta (change in) RMSEA First, configural invariance was tested. Table 3 indicates that the SWLS has configural invariance, with CFI scores ranging from 0.962 to 0.975, meaning that the scale consists of the same factor structure across all language, campus, and gender groups. Concerning metric invariance, the results in Table 3 show that the SWLS has metric invariance across the gender groups (ΔCFI = -0.003) but not across the language (ΔCFI = -0.029) or campus groups (ΔCFI = -0.029). By releasing the intercept of Item 1 in the Afrikaans groups and Campus 2, partial metric invariance was achieved for the SWLS across the different language and campus groups. Concerning scalar invariance, Table 3 indicates that the SWLS achieved scalar invariance across gender groups (ΔCFI = 0.010) but not across language (ΔCFI = -0.090) and campus groups (ΔCFI = -0.085). Therefore, to improve model fit, the intercepts of Item 1 and Item 3 were freed for the Afrikaans group to achieve partial scalar invariance. Similarly, partial scalar invariance was reached across different campus groups by releasing Items 1 and 5 intercepts for Campus 1 and Items 1 and 4 for Campuses 2 and 3. Internal consistency Cronbach’s alpha coefficients were calculated as a measure of internal consistency. A Cronbach’s alpha coefficient of 0.83 was found for the SWLS, indicating an acceptable internal consistency (α ≥ 0.70) (Nunnally, 1978). In addition, McDonald’s omega (ω) was 0.83, showing good internal consistency (Kline, 2015). Discussion Essentially, any assessment used in a diverse and cross-cultural higher education institution must be tested and analysed to ensure the scale measures the same constructs across diverse groups to be considered fair and unbiased (Hill et al., 2013). Therefore, this study presented preliminary evidence on the psychometric properties of the Satisfaction with Life Scale for first-year university students at a specific South African university, with a particular focus on item bias and invariance (including configural invariance, metric invariance and scalar invariance) between specific language, campus and gender groups. Before differential item functioning and invariance testing, confirmatory factor analysis was used to provide evidence for a one-factor structure. This indicates that satisfaction with life can be measured as one general factor (Diener et al., 1985). Differential item functioning was used to test for uniform and non-uniform bias. Item bias was detected in both language and campus groups but not across gender groups. Item 1 (“In most ways my life is close to my ideal”) was problematic for language Journal of Student Affairs in Africa, 11(1), 109–128 DOI: 10.24085/jsaa.v11i1.4327 | ISSN 2307-6267 | www.jsaa.ac.za120 and campus groups. Uniform bias was present between the Sesotho and Setswana groups, which indicates that the probability of a specific response from these two language groups was systematically higher or lower across all levels of endorsement (Swaminathan & Rogers, 1990; Teresi & Fleishman, 2007). However, non-uniform bias was present in the Afrikaans group, which indicates that at certain levels of endorsement, the relation between the Afrikaans group and the response to the item was dissimilar compared to the Sesotho and Setswana groups (Mellenbergh, 1989; Sireci & Rios, 2013). With regards to campus, the findings indicate that uniform bias is present between Campus 1 and 3, with non-uniform bias being present for Campus 2 – indicating that at specific levels of endorsement, the relation between Campus 2 and the response to the item was dissimilar compared to those for Campus 1 and 3 (Mellenbergh, 1989; Sireci & Rios, 2013). In addition, uniform bias was observed in Item 3 (“I am satisfied with my life”). Even though this finding implies that the probability of a specific response to this item is found at all trait levels across the three language groups on a statistically significant level, based on the pseudo-McFadden R2 statistic as well as the changes in beta coefficient, this impact was negligible and therefore not of practical significance (Crane et al., 2007; Teresi & Fleishman, 2007). No bias was detected for any of the SWLS items for gender groups. Measurement invariance included testing for configural, metric, and scalar invariance across the different language, campus and gender groups included in this study (Preti et al., 2013). The results showed that the SWLS has configural invariance across the different language, campus, and gender groups, indicating that the same factor structure is present across the groups included in this study. Regarding metric invariance, the SWLS has metric invariance across gender groups but not across language and campus groups. When full invariance was not achieved, partial metric invariance was tested by assessing the factor structure of the SWLS based on changes in CFI (ΔCFI) (Clench-Aas et al., 2011). Partial metric invariance was achieved by freeing the loading of Item 1 in both the Afrikaans group and Campus 2. Although some parameters can vary across groups (rejected constraints), at least two intercepts and factor loadings should be equally constrained across groups to make valid inferences (Byrne et al., 1989; Laguna et al., 2017). Therefore, factor loadings can still be fairly compared across language and campus groups with partial metric invariance (Van De Schoot et al., 2012). Full metric invariance was achieved across the gender groups included in this study, which indicates that each item similarly contributes to the latent construct of the SWLS across gender groups (Putnick & Bornstein, 2016). Evidence was found for scalar invariance across gender groups but not across language and campus groups; hence, partial scalar invariance was tested. Constraints were rejected in both language and campus groups. Concerning the language groups, the intercepts of both Item 1 and Item 3 were freed for the Afrikaans group to improve model fit. Regarding the campus groups, partial scalar invariance was achieved by releasing the intercepts of Item 1 and Item 5 for the Campus 1 group as well as Item 1 and Item 4 for Campuses 2 and 3. This implies that fair comparisons across language van Rensburg, C., & Mostert, K. (2023). Can life satisfaction be measured fairly for different groups of South African first-year students? Testing the Satisfaction with Life Scale 121 and campus groups can still be made (Van De Schoot et al., 2012). Full scalar invariance was achieved across the gender groups, which indicates that the factor loadings and intercepts of the five items of the SWLS can be meaningfully compared across gender groups (Putnick & Bornstein, 2016). These results are in line with other studies, where some items did not appear to be equivalent across cultures. More specifically, with regards to cross-cultural studies, variance was reported for Item 2, Item 3, Item 4 and Item 5 (Atienza González et al., 2016; Dimitrova & Domínguez, 2015; Whisman & Judd, 2016; Zanon et al., 2014). Cronbach’s alpha coefficients and McDonald’s omega (ω) were used to test for the internal consistency (a measure of reliability) of the Satisfaction with Life Scale. The findings indicate a Cronbach’s alpha coefficient of 0.83 and McDonald’s omega of 0.83, indicating that the Satisfaction with Life Scale is reliable. Limitations and recommendations The findings indicate that across the different language, campus, and gender groups included in the study, bias was detected in both Item 1 (language and campus groups) and Item 3 (language groups). In addition, evidence was provided for configural invariance across all groups but not metric and scalar invariance. As a result, partial, metric and scalar invariance was detected across language and campus groups (probably due to Item 1 being problematic). Although the SWLS has been validated across many countries, languages, and cultures, future researchers should validate the psychometric properties of the SWLS to ensure it is valid and reliable for use across different diverse groups and settings of university students. The present study serves as preliminary evidence of item bias and invariance of the SWLS. However, future research should focus on its differential prediction for different academic outcomes, such as test and academic performance, because the slope and intercept of relations still need to be determined (Berry 2015; Theron, 2006). Furthermore, future research could inform the nomological network of the SWLS by exploring its relationships to other variables related to student well-being, success, and goal commitment (Jonker et al., 2015; Van Lill et al., 2020). Robust maximum likelihood (MLR) was used as the CFA measurement model’s estimation technique and to test the SWLS’s invariance based on the small sample sizes in this study. Even though MLR has been introduced into CFA models when the normality assumption of data distribution is moderately violated, as can be the case when using small sample sizes (Knief & Forstmeier, 2021), future studies with sufficient sample sizes could use a weighted least squares with mean- and variance adjusted (WLSMV) method of estimation when the data are of ordinal nature (see Li, 2016). Indeed, the results of the current partial measurement invariance might, with larger sample sizes in some groups, either reach the threshold of non-partial invariance or point out new nuanced differences in the interpretation of items between languages. Practical implications Globally, the multicultural nature of populations has become more salient (Van De Vijver & Rothmann, 2004). For example, the increasing demographic diversity in the United Journal of Student Affairs in Africa, 11(1), 109–128 DOI: 10.24085/jsaa.v11i1.4327 | ISSN 2307-6267 | www.jsaa.ac.za122 States has been well documented, specifically among the population that does not use English as their native or primary language (Nwosu et al., 2014; Pascarella, 2006). Liu et al. (2019) stipulate that literature regarding language diversity among college students in the United States is scarce and under-researched – the reason being that students with diverse language backgrounds are often combined in the discussion of low-income students, racial minorities, and other underrepresented student groups (Kanno & Cromley, 2013). In addition, non-native English speakers’ cultural values and norms are not perfectly aligned with the English-only college environment (Liu et al., 2019). Through the two mentioned examples, educators and researchers should be cautious when applying any instrument, in our case, the SWLS, to university settings without paying special attention to student diversity and testing for psychometric properties like item bias and invariance. Additionally, the current study only included Afrikaans, Sesotho, and Setswana language groups; therefore, future researchers should include English as a language group for cross-cultural comparisons. On a practical level, psychologists and practitioners should take great care when applying concepts and instruments from Western countries without testing the applicability of those measures in a diverse setting. Without adequate testing, systematic measurement variability can cause several challenges, including flawed population forecasts, errors in hypothesis testing, planning and implementation of policies, and misguided research on discrepancies (Perkins et al., 2006). Instruments developed in other countries could either be culturally biased, produce inconsistent results when groups are compared, and might be unable to adequately measure a construct when the culture or language differs from the country of origin (Blokland, 2016; Moletsane, 2016; Van De Vijver & Rothmann, 2004). Therefore, it is essential to ensure equivalent measurement before comparing groups or individuals to avoid ambiguous comparisons (Gregorich, 2006; Teresi & Fleishman, 2007). Acknowledgement The authors would like to thank Prof. L.T. de Beer for his assistance with the statistical analysis and interpretation of the results. Ethics statement The study was approved by the Ethics Committee, Faculty of Economic and Management Sciences (EMS-REC) (Ethics no.: NWU-HS-2014-0165-A4). Potential conflict of interest The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article. 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DOI: 10.24085/jsaa.v11i1.4327 https://doi.org/10.1080/13803611.2013.767621 https://doi.org/10.1080/13803611.2013.767621 https://www.jstor.org/stable/1434855 https://www.jstor.org/stable/1434855 https://doi.org/10.1007/s11136-007-9184-6 https://doi.org/10.4102/sajip.v33i1.260 https://doi.org/10.1016/j.paid.2015.05.008 http://dx.doi.org/10.1080/17405629.2012.686740 http://dx.doi.org/10.1080/17405629.2012.686740 https://doi.org/10.3389/fpsyg.2015.01064 https://doi.org/10.3389/fpsyg.2015.01064 https://doi.org/10.1007/s11205-013-0478-5