Abstract
This paper reports the validation of the Greek Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in a mature student community-based sample (n = 734). The WEMWBS was administered as part of a battery of questionnaires, and the survey data were analyzed employing rigorous advanced multivariate methods to determine its reliability and validity. The findings revealed excellent internal consistency, a unidimensional structure substantiated by exploratory and confirmatory factor analyses, and adequate convergent validity, confirming its validity as a cohesive metric for assessing mental well-being. The validated WEMWBS has the potential to be an instrument, for researchers, healthcare professionals, and other individuals involved in assessing the current condition of mental well-being in Greek-speaking populations.
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Introduction
Mental health research has traditionally prioritized a pathology-centered paradigm, focused on the diagnosis, treatment, and management of psychological disorders (Seligman & Csikszentmihalyi, 2000). Within this framework, mental health has been conventionally defined as the absence of psychopathology (Westerhof & Keyes, 2010). As such, the primary quantifiable end points for many therapeutic interventions have been centered on the reduction of psychiatric symptoms. Mental health, however, transcends the mere absence of illness, potentially existing as a separate, albeit related, construct within the continuum of the human lifespan (Westerhof & Keyes, 2010). Indeed, there has been a growing recognition within contemporary scholarship that the historical perspective fails to capture the nuanced experiences of individuals and the subjective quality of mental well-being that extends beyond symptomatology (Slade, 2010). The global discourse on mental health has therefore been expanding in recent decades to include a recognition of mental well-being and its impact on health outcomes. This evolution is mirrored by a growing interest among medical professionals, policymakers, the scientific community, and public health organizations like the World Health Organization (WHO) and the Organisation for Economic Co-operation and Development (OECD), in exploring this concept (Fung, 2019; Stewart-Brown et al., 2009). Acknowledging its importance for pre-empting ill health, particularly in light of the growing burden of long-term conditions (Nolte & McKee, 2008), an emphasis on positive elements of mental health and functioning as opposed to deficits, problems, and symptoms has since permeated psychiatric research, mental health policy, and clinical practice (Gable & Haidt, 2005; Chida & Steptoe, 2008; OECD, 2013; Siahpush et al., 2008; Slade, 2010). In 2001, the WHO acknowledged mental well-being as a critical component of health prompting global health policy reforms. Bhutan’s Gross National Happiness Index (Ura et al., 2012), New Zealand’s Wellbeing Budget (Mintrom, 2019), and European Union’s Mental Health Action Plan (World Health Organization, 2015) all embed mental well-being into their development agendas. Similarly, in the UK, the Stiglitz Commissions report (Stiglitz et al., 2009) recommended tracking well-being in population surveys.
The assessment and integration of well-being metrics, in particular, has garnered considerable attention with numerous studies advocating for a deeper comprehension and more nuanced quantification of this concept. Surveilling well-being serves many public health objectives, given its far-reaching socioeconomic implications (Forsman et al., 2015; Huppert, 2014; Doherty & Kartalova O'Doherty, 2010; Dolan et al., 2011; Beddington et al., 2008; Oswald & Wu, 2010). Firstly, poor mental well-being is a predictor for various adverse health outcomes including general mortality rates (Keyes et al., 2010; Tennant et al., 2012) and all-cause mortality specifically (Keyes & Simoes, 2012). Conversely, higher levels of well-being are associated with decreased risks of mental and physical disorders, disability, and healthcare service utilization (Lyubomirsky et al., 2005; Song et al., 2023). Tracking well-being is therefore an effective strategy for enhancing health-related quality of life and for pre-emptive health management (Keyes, 2002; Keyes et al., 2012). For the aforementioned reasons, most instruments are designed to detect and measure mental illness or disorders with considerably less emphasis on measuring well-being. In practice, this means that many existing tools have limited utility in monitoring positive mental health or evaluating interventions aiming at promoting it. Ceiling effects, insufficient cultural sensitivity, and a predominant focus on negative indicators constrain traditional scales from providing such an assessment (Trousselard et al., 2016). Valid and reliable tools are therefore needed to appraise the status quo and population needs, and evidence shows that subjective well-being can be measured in valid and reliable ways (Waqas et al., 2015). For such measures to be useful, however, they must be theoretically rigorous, policy relevant, and empirically robust (Dolan et al., 2011; Lang & Bachiner 2017).
The concept of mental well-being emerged from a rich and sometimes controversial theoretical background marked by debates over the degree to which it is represented by psychological attributes (such as cognitive and functional aspects) or affective dimensions (such as positive or negative moods) (Ryan & Deci, 2001; Tennant et al., 2007). Consensus has largely formed around a dual-component model that encompasses positive psychological functioning, self-realization, and positive relationships—the eudaimonic perspective—and the subjective experience of happiness and life satisfaction including positive feelings, affect, and emotions—the hedonic perspective (MacKean et al., 2011; May, 2017; Taggart, 2015). Various tools have been used to track well-being including Ryff’s Scale of Psychological Well-being (Ryff & Keyes, 1995), the Satisfaction with Life Scale (Diener et al., 1985), the Positive and Negative Affect Scale (Watson et al., 1988), the Short Depression Happiness Scale (Joseph et al., 2004), and the World Health Organization (WHO) Well-being Index (Bech, 2004). However, these measures do not fully align with the dual-component model and therefore do not appraise the full spectrum of mental well-being as currently conceptualized (Castellví et al., 2014).
The Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) incorporates both perspectives, assessing affective-emotional aspects, cognitive-evaluative dimensions, and psychological functioning (May, 2017). It is among the most widely recognized tools for assessing mental well-being. Supported by NHS Scotland, it was developed and validated through a collaborative effort between the Universities of Warwick and Edinburgh and grounded in a mixed methods study of English and Scottish students (Clarke et al., 2010, 2011). The resulting scale, comprising 14 positively worded items, is concise and therefore suitable for use in large-scale population surveys and for the appraisal of well-being-related initiatives (Castellvi et al., 2014; Stewart-Brown, 2011, 2015a, 2015b; Tennant et al., 2007; Trousselard et al., 2016). The scale has been translated into more than 25 languages over the last decade and validated qualitatively and psychometrically in various populations including the Italian, Brazilian, Portuguese, Chinese, French, German, Urdu, Slovenian, Danish, and Norwegian. Research confirms its ease of administration, user-friendliness, robust psychometric validity (Clarke et al., 2011; Lloyd & Devine, 2012; Maheswaran et al., 2012; Taggart et al., 2013; Tennant et al., 2007), and sensitivity to change in different well-being promotion initiatives (Stewart-Brown, 2015a, 2015b). The validation also encompasses subgroups such as students, general populations, adolescents, clinical samples, and ethnic minority samples and has consequently gained widespread acceptance among medical professionals and practitioners, around the world (Orgeta et al., 2013). Despite its growing popularity and extensive validations, the scale has not yet been adapted and validated for use in the Greek population. Of particular importance is the ongoing national transition within the Greek mental health system towards a community-based paradigm, accompanied by an emphasis on preventative psychiatry (Anargyros et al., 2021). Along with evidence-based service development plans, these initiatives underscore the need for standardized tools that can track the success of such policy reforms.
Achieving and maintaining good mental well-being is not always easy and can have a significant impact on individuals’ quality of life (Ringdal et al., 2017). This is especially relevant for adults with multiple roles, such as adult university students studying through distance learning. These nontraditional students represent a diverse population who are attracted to open distance learning programs due to their ability to balance their careers and family life while pursuing a degree (Miller & King, 2003). Accordingly, this study aimed to assess the reliability and validity of the Greek version of the WEMWBS among a sample of adult students enrolled in undergraduate and postgraduate programs at the Hellenic Open University (HOU). The university follows an open distance learning approach without any registration constraints, making it accessible to adult residents anywhere in Greece. This investigation therefore provides insights into a portion of Greece’s adults, marking the first measurement of well-being among this population using the particular instrument. Our hope is that the validation of the Greek WEMWBS will provide mental health practitioners and national policymakers with a standardized measure of well-being and an accurate method for tracking the effectiveness of various primary and tertiary programs. The potential, for advancing health assessment and intervention strategies in Greek settings, is significant as demonstrated by the validation of the WEMWBS in a Danish sample (Koushede et al., 2019).
Methods
Participants
Prior to the study, ethical approval was secured from the HOU Governing Board (reference 161/GC-3/2013 and 634/1–8/2013). Data were sourced from a cohort of HOU adult students using a stratified sampling approach in recognition of differences in their academic level (undergraduate or postgraduate) and discipline (science, social science, or humanities). This approach aimed to discern potential influencers on results and ensure a representative sample, targeting 2% of the student body. This study was part of a larger project which aimed at exploring HOU students’ needs and recommendations regarding the development of a university-based counselling center. The Greek version of the WEMWBS was administered to 894 students attending various randomly selected classes from the aforementioned stratification categories. Students were informed of their voluntary and anonymous participation. Assurances were given that data would be handled in aggregate and confidentiality would be maintained. Participants were asked to fill in paper-based questionnaires spontaneously, which took approximately 15 min. Responses from participants with incomplete answers (N = 160) were discarded. The complete data from a final sample of 734 participants were anonymized and transferred into IBM SPSS (v 26) for analysis.
Measures
The Greek WEMWBS was administered as part of a battery of scales in the form of a printed booklet. The additional sections assessed various aspects of students’ psychological functioning including their mental state, needs, counselling preferences, and attitudes towards help seeking. The selection of measures was grounded in prior research concerning counselling services in Greece (e.g., Christidis et al. (2004), Christopoulos et al. (1997), Efstathiou et al. (2003), Efthymiou et al., (2001, 2007), Efthymiou (2003, 2007), Malikiosi-Louizou (1989), Navridis et al. (1990), and Papadioti-Athanasiou and Damigos (2003)). All measures were self-administered.
Warwick-Edinburgh Mental Well-Being Scale
The Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) (Tennant et al., 2007) was developed to measure well-being at the population level and appraise initiatives focused on its enhancement. It comprises 14 positively worded statements and is structured for comprehension across varied sociodemographic profiles. Respondents retrospect on their past fortnight, considering both affective experiences and personal growth. Items are scored on a 5-point scale ranging from “none of the time” to “all of the time.” Aggregate scores, ranging from 14 (minimal) to 70 (optimal), serve as an indicator of respondents’ well-being. While the WEMWBS was not designed for individual monitoring, research suggests its efficacy in tracking changes in well-being among adults and, in particular, in providing individuals with an overview of their well-being.
Greek Translation
A rigorous translation process was implemented in line with similar WEMWBS validation studies (e.g., Fung (2019), López et al. (2012), and Trousselard et al. (2016)). First, the scale was forward-translated by two native Greek speakers proficient in English, with translation difficulties resolved by consensus. Subsequently, a back-translation was undertaken by two other bilingual mental health experts who had no knowledge of the original content. Forward- and back-translations were examined by two bilingual professors of psychology to ensure conceptual clarity, cultural relevance, and linguistic accuracy, and a pilot version was formulated. The resulting questionnaire was pilot tested on a group of 50 HOU postgraduate students from a Master’s in Education cohort. Feedback from the pilot study was collected and analyzed qualitatively and informed the final questionnaire translation. The final version administered in the present study is available from the developers’ website (https://warwick.ac.uk/fac/sci/med/research/platform/wemwbs/using/translations). Pilot data are not included in this study’s dataset.
Other Measures
Additional measures, chosen on the basis that they measure similar well-being-related concepts, were included in the booklet for purposes of verifying the scale’s construct validity. These included the Depression, Anxiety, and Stress Scale–21 (DASS-21) (Lovibond & Lovibond, 1995), the Inventory of Attitudes towards Seeking Mental Health Services (IAMSH) (Mackenzie et al., 2004), and a set of intake questionnaires developed in-house to assess informants’ needs and rationale for seeking counselling services. Sociodemographic data including age, gender, educational level, marital status, and self-reported health status were also collected.
Depression, Anxiety, and Stress Scale–21 (DASS-21)
Psychological distress, encompassing stress, distress, irritation, and emotional sensitivity, has been strongly linked to physical morbidity, reduced quality and duration of life, and increased utilization of health services (e.g., Lahey (2009)). The DASS-21 was developed to assess and distinguish between the symptoms and intensity of three dimensions of mental well-being: depression, anxiety, and stress (Lovibond & Lovibond, 1995). The measure consists of three 7-item subscales that effectively discriminate between these constructs (González-Rivera et al., 2020), with demonstrated reliability and validity in both clinical and nonclinical adult populations (Antony et al., 1998; Bottesi et al., 2015; Sinclair et al., 2012; Vasconcelos-Raposo et al., 2013). Each subscale is rated on a scale from 0 (“did not apply to me at all”) to 3 (“applied to me very much, or most of the time”). Total scores for each subscale are calculated by summing the scores for each item and multiplying the result by a factor of 2. The sum scores on the subscales range from 0 to 42, while the total DASS scores range from 0 to 120. In the Greek population, the DASS-21 has been confirmed as a valid and reliable instrument for measuring depression, anxiety, and stress (Pezirkianidis et al., 2018).
Inventory of Attitudes Towards Seeking Mental Health Services (IASMH)
The IASMHS, developed by Mackenzie et al. (2004), is a 24-item scale that consists of three factors: psychological openness, help-seeking propensity, and indifference to stigma (Mackenzie et al., 2004). Psychological openness refers to the willingness to acknowledge psychological problems and seek help for them. Help-seeking propensity measures one’s willingness and ability to seek help. Indifference to stigma assesses the concern about how others in individuals’ lives would react to their seeking help. Responses are measured on a scale of 0 (“somewhat disagree”) to 4 (“agree”). Items require participants to rate their agreement with statements concerning societal perceptions of their psychological health. The scale has demonstrated strong internal consistency reliability coefficients for both the overall measure and the subscales (MacKenzie et al., 2004). Additionally, it has shown good convergent validity (MacKenzie et al., 2006).
Difficulties and Adverse Life Events
Stressful life events, moderated by the manner of their appraisal, coping mechanisms, resilience, and social support (Monroe & Simons, 1991), have profound implications for a range of health outcomes (Marlowe, 1998). These bear significant consequences for well-being, often disrupting daily functioning and life satisfaction (Diener et al., 1999; Seligman, 1972) and can precipitate episodes of mental illness like depression and anxiety (Kendler, Karkowski, & Prescott, 1999). To quantify the impact of such difficulties and adverse life events on students’ well-being and academic performance and evaluate the need for counselling services, we formulated two evaluative indexes informed by the literature (e.g., Barnett et al. (1983), Brown and Harris (1978), and Holmes and Rahe (1967)). The Academic Difficulties Index, comprising 10 items, assessed students’ academic concerns and their willingness to seek support for them. The Life Difficulties Index with 23 items captured a range of personal, social, family, and professional difficulties or adversities encountered in the past year. Both indexes were rated on a 5-point Likert scale, ranging from 1 (none) to 5 (very high), to determine the extent to which respondents would seek help from a specialist if possible. An Index of Difficulties was calculated from each list based on the count of academic and personal difficulties reported by respondents (range 0–10 for former and 0–23 for the latter). Additionally, a Needs Intensity Index (NII) was calculated, which reflected the estimated perceived impact of the recorded events based on the sum of scores indicated for each difficulty. The theoretical range for difficulties impact in academic study was 10–50, while for personal, social, and family difficulties, the range was 23–115. Higher scores indicated a greater need for support or more difficulties experienced.
Statistical Analyses
Statistical analyses were conducted using SPSS v.26 (IBM Chicago, IL, USA) and RStudio software (v1.4.1717 for Windows) using the Lavaan, GPArotation, and Psych packages. Participants’ data collected in this study were selected for further analysis only when all questions of the Greek version of the WEMWBS were answered, as recommended by Tennant and colleagues (2007). A number of psychometric testing tools and validated instruments were used to analyze the data, as described below.
Descriptive Statistics
The distribution of total WEMWBS scores was inspected visually using histograms and quantile–quantile (Q-Q) plots and by assessing absolute skewness and kurtosis in line with recommendations for our samples size (Kim, 2013). The Shapiro–Wilk and Kolmogorov–Smirnov (K-S) with the Lilliefors correction statistical tests were also computed as a mainstream strategy, although it is acknowledged that these tests are not always reliable with large sample sizes (n > 300) (Öztuna et al., 2006). Frequencies and descriptive statistics were calculated for sociodemographic variables and total WEMWBS scores.
Factor Analysis
Data suitability for factor analysis was checked using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity (Kaiser, 1974; Watkins, 2018). The single factor model was investigated using exploratory factor analysis using the principal component analysis (PCA) method as suggested in the literature (Brown, 2014; Dong et al., 2016; Fung, 2019; Stewart-Brown et al., 2009; Tennant et al., 2007). In PCA, factor extraction was investigated using Kaiser’s eigenvalue-over-one criterion (Kaiser, 1960), parallel analysis using eigenvalues that correspond to the desired percentile (95th) as recommended by O’Connor (2002), and the scree plot test (Cattell, 1966; Cattell & Vogelmann, 1977; Fabrigar et al., 1999). In this analysis, an item with a factor loading over 0.50 can be interpreted as having practical significance when the sample size is more than 350 (Bech, 2004).
In addition, confirmatory factory analysis (CFA) using SPSS and RStudio was applied to evaluate the scales’ construct validities (Joreskog, 1969; Li, 2016; Loewenthal, 2001). CFA was conducted using the diagonally weighted least squares (DWLS) estimation model using the Lavaan package in RStudio (Rosseel, 2012). The relevant literature suggests that the DWLS method is less biased, works well for ordinal variables that include items with two to seven categories, and provides more optimal fit and accurate parameter estimates when working with large sample sizes consisting of ordinal data with two to seven categories (Browne & Cudeck, 1992; DiStefano & Morgan, 2014; Flora & Curran, 2004; Mîndrilă, 2010; Li, 2016; Lionetti et al., 2016; Yang-Wallentin et al., 2010). The model fit and cut-off criteria were evaluated on the basis of the cut-off values indicated in the existing structural equation modelling (SEM) literature. As recommended by Kline (2015) and others (Bass et al., 2016; Bentler & Bonett, 1980; Hair, 2010; Hu & Bentler, 1999; Schreiber et al., 2006), several fit metrics were observed including the Comparative Fit Index (CFI > 0.95), a Tucker-Lewis Index (TLI > 0.95), a Root Mean Square Error of Approximation (RMSEA < 0.06), and the Standardized Root Mean Square Residuals (SRMSR < 0.08) (Hooper et al., 2008; Hu & Bentler, 1999; Kline, 2015; Williams et al., 2009) which were considered acceptable. Additionally, as the chi-square value is not completely reliable with sample sizes above 200 (Meyers et al., 2005), we elected to observe the value of chi-square divided by the degree of freedom in the model (χ2/df), with values less than 2.5 (Kline, 2015; Rosseel, 2012) indicating a good fit and values around 5.0 indicating an acceptable fit. Finally, we assessed the structure coefficients as indicators of model fit and sought coefficients that achieved statistical significance (p < 0.05) with loadings above the minimum 0.30 recommended by Meyers et al. (2005).
Reliability
Internal consistency was assessed using a suite of reliability coefficients including conventional and ordinal Cronbach’s alpha and omega coefficients (hierarchical and total) for which values above 0.70 were sought (Nunnally & Bernstein, 1994; George & Mallery, 2003; Cheung et al., 2023). An evaluation of all 14 items’ correlation coefficients was conducted, to analyze whether these items warranted scale construction. As the alpha coefficient alone is not a sufficient indicator of internal reliability, we also observed the impact of deleting any of the 14 items on the overall alpha coefficient and corrected item-total correlations as additional measures of reliability. Specifically, we sought alpha coefficient values similar to the overall alpha coefficient for the former and items that correlated well with the overall score from the scale (> 0.30) for the latter (Field, 2018).
Convergent Validity
The evaluation of convergent validity, a sub-type of criterion validity, was used to estimate the correlation coefficients of the WEMWBS scores with those of other well-established instruments. The WEMWBS has been reported to have significant moderate to high positive correlations with indicators of positive affect, life satisfaction, and overall health and negative correlations to symptoms of anxiety and depression. To evaluate our hypotheses, we performed Pearson’s correlation coefficient calculations between the overall WEMWBS scores and the DASS-21 and IASMHS subscales and overall scales, respectively.
Results
Sample Characteristics and Differences Across Subgroups in WEMWBS Scores
The sample characteristics and differences in WEMWBS scores across subgroups are detailed in Table 1. Briefly, of the final 734 adult students sampled, 40.3% were male and 59.7% were female. Most were employed (86.1%), aged 30 or older (88.26%), with ages ranging between 23 and 64 years of age and an average age (SD) of 37.59 (7.19). The majority (67%) faced no or occasional financial challenges, while the remaining (33.14%) experienced frequent difficulties. Most (71.8%) perceived themselves in good health. The distribution was fairly even across study level (59% undergraduate, 51.2% postgraduate), marital status (51.2% married, 43.9% single), and parental status (53.3% with children, 46.7% without).
No difference in WEMWBS total scores was observed between participants by age (t(105.28) = 1.085, p = 0.280), level of study (undergraduate or postgraduate) (t(697.78) = − 0.965, p = 0.335), gender (t(577.36) = 1.242, p = 0.215), parental status (t(197.63) = − 0.208, p = 0.836), or marital status (F(4, 729) = 1.66, p = 0.156). However, unemployed respondents had significantly lower total WEMWMBS scores (M = 49.80, SD = 7.27) compared to employed respondents (M = 52.70, SD = 7.65) (t(732) = 3.554, p < 0.001). Significant differences were observed based on the extent of financial problems reported (F(3, 723) = 14.10, p < 0.001), with those experiencing severe financial issues scoring lower. In our sample, scores decreased by 2.08 points on average with increasing levels of financial difficulties. A significant difference in scoring was observed between participants by health status (F(4, 728) = 70.15, p < 0.001) with those reporting “(often or always) unwell” scoring lower.
An examination of the academic difficulties faced by students revealed a heterogeneous distribution of challenges (Table 2). The majority of respondents reported no discontent with their field of study (67.3%), while time management (31.5% moderate difficulty) and study organization (30.4% low difficulty) were prevalent concerns. Notably, a significant proportion of students reported high levels of exam anxiety (25.9%) and learning difficulties (24.3% low difficulty). Fear of failure also emerged as a notable stressor, with 12.9% of students experiencing it to a very high degree, suggesting areas where academic support services could be beneficial. The NII for academic difficulties was 22.24 (SD = 8.44), with a distribution that was positively skewed, indicating that most participants reported fewer academic difficulties. Extreme difficulties (score of 50) were observed in only a single case.
The analysis of personal difficulties (Table 3) revealed a substantial prevalence of anxiety/stress issues with 51.3% of respondents reporting moderate to very high levels of difficulty. This suggests that mental health concerns, particularly related to anxiety and stress, were significant among this sample. The least reported difficulty pertained to addiction and substance abuse, with a significant majority (93.1%) reporting no problems in this area. Financial difficulties were also prominent, with a combined 54% reporting moderate to very high levels of financial distress.
Preliminary Analyses
The total WEMWBS scores had a mean (SD) of 52.31 (7.66) and a median of 53. While the histogram and Q-Q plots suggested a normal distribution (Fig. 1), the Shapiro–Wilk and Kolmogorov–Smirnov with the Lilliefors correction statistical tests indicated significant deviations from normality (W(734) = 0.993, p < 0.001 and W(734) = 0.047, p < 0.001, respectively). Both tests are conservative; however, the Kolmogorov–Smirnov test is highly sensitive to extreme values (Ghasemi & Zahediasl, 2012), while the Shapiro–Wilk is typically recommended for sample smaller than 50 (Gupta et al., 2019). Inspection of our data revealed no extreme outliers or anomalies. Given that deviations from normality are common in large sample sizes (Field, 2013; Kim, 2013) without affecting the results of parametric testing (Öztuna et al., 2006), along with skewness and kurtosis absolute values (− 0.171 and 0.122, respectively) aligning with expectations for our sample size (Kim, 2013), we deemed parametric testing to be appropriate for our data.
WEMWBS item-level mean scores spanned from 2.81 to 4.28. Standard deviations varied from 0.714 to 1.003, indicating moderate variability in the dataset. The highest mean score was attributed to item 4 (“I’ve been feeling interested in other people”), while item 3 (“I’ve been feeling relaxed”) received the lowest mean score (Table 4). Overall, the majority of items garnered mean scores exceeding 3.5.
Reliability
The Greek WEMWBS showed good internal consistency with a Cronbach’s alpha coefficient of 0.896, ordinal alpha of 0.920, and omega hierarchical and omega total values of 0.900. The reliability analysis showed that deleting any of the items would not improve the alpha coefficient meaningfully (Table 5).
The inter-item correlation coefficient matrix (Table 6) showed that all items were positively correlated with each other and the inter-item correlation average (0.385) was satisfactory (Clark & Watson, 1995). Based on these results, all items were henceforth included for further analyses.
Factor Structure
The Kaiser-Mayer-Olkin (KMO) measure of sampling adequacy was 0.91, exceeding the 0.6 value suggested by Pallant (2005). Bartlett’s test of sphericity was statistically significant (p < 0.001). These results showed that the data was suitable for exploratory factor analysis. Using the Kaiser-Guttman criterion, PCA identified three factors with eigenvalues above 1 accounting for 43.74%, 9.39%, and 7.33% of total variance explained (Table 7).
Parallel analysis of principal components indicated that only two factors should be retained (Fig. 2). Item loadings on component one ranging from 0.421 to 0.810 were all higher compared to loadings on components 2 and 3, with the exception of item 4 which loaded preferentially on component 2. The sharp elbow of the scree plot showed a substantial drop in magnitude of the eigenvalues. Taken together, these results suggest a single underlying factor to the scale.
CFA fit indices were acceptable (χ2/df = 3.48, CFI = 0.972, TLI = 0.976, RMSEA = 0.058, SRMR = 0.067; Fig. 3) without any model respecification. All factor structure coefficients achieved statistical significance (p < 0.05) with coefficients greater than 0.30 ranging from 0.37 (item 4) and 0.80 (item 8). The chi-squared test was significant (χ2 = 268.113, df = 77, p < 0.0005), although, as argued by Lloyd and Devine (2012), this may be an artifact of the large sample size. The χ2/df, however, was acceptable. Overall, these results indicate that the Greek version of the WEMBS had a good fit for a unidimensional structure.
Internal Consistency and Item Validity
Cronbach’s alpha for the Greek WEMWBS (0.896) was above the recommended acceptable value of 0.70 (DeVellis, 2016; Polit & Beck, 2012). Corrected item-total correlations ranging from 0.366 for item 4 (I’ve been feeling interested in other people) to 0.746 for item 8 (I’ve been feeling good about myself) were above 0.3, showing that all items correlate well with the overall scale (Table 7).
Convergent Validity
Pearson correlation coefficients between the overall scores on the Greek WEMWBS are detailed in Table 8. There were statistically significant negative correlations between the WEMWBS scores and the DASS-21 subscales for anxiety (r = − 0.430, p < 0.01), depression (r = − 0.585, p < 0.01), and stress (r = − 0.542, p < 0.01), as well as the total DASS-21 score (r = − 0.570, p < 0.01). Furthermore, significant positive correlations were found between the WEMWBS and the IASMHS subscales for help-seeking propensity (r = 0.207, p < 0.01), indifference to stigma (r = 0.262, p < 0.01), and the total IASMHS score (r = 0.219, p < 0.01). The Index of Difficulties showed a significant negative correlation with the Greek WEMWBS (r = − 0.402, p < 0.01), as did the Index of Difficulties for moderate to high scores (r = − 0.381, p < 0.01), and the Needs Intensity Index (r = − 0.493, p < 0.01).
Discussion
This study examined the psychometric properties of the Greek WEMWBS using a sample of adult students drawn from the HOU. Findings highlighted excellent internal consistency, a unidimensional structure substantiated by exploratory and confirmatory factor analyses, and adequate convergent validity, confirming its validity as a cohesive metric for assessing mental well-being. The validation of this questionnaire provides the Greek research community and policymakers with a means for assessing well-being among its population, especially pertinent given the country’s evolving emphasis on mental health prevention strategies (Anargyros, et al., 2021).
The development of the WEMWBS was based on research conducted with student demographics in England and Scotland (Tennant et al., 2007). Drawing parallels, our study attempted to validate the Greek adaptation of the scale with a similar student population. Cronbach’s alpha exceeded the conventional threshold and aligned closely with values reported by the original scale developers and other translated versions. The fidelity of alpha, however, has been intensely scrutinized in recent years, with many apt critiques offered elsewhere (McNeish, 2018; Sijtsma, 2009). Briefly, conventional alpha is critiqued for its misinterpretation as a measure of internal consistency, dependence on item quantity, and inability to discern unidimensionality, necessitating alternative metrics for the assessment of internal consistency reliability. While we have provided the traditional alpha for comparative purposes with existing publications, our evaluation prioritizes ordinal alpha, omega hierarchical, and omega total indices, all of which demonstrated excellent internal scale consistency. The omega coefficient, grounded in the congeneric model of classical test theory, accommodates variable item means and variances unlike the tau-equivalent model employed by alpha which assumes that these are constant across test items (Dunn et al., 2014). Our obtained value (ω = 0.90) aligns closely to that reported by Konaszewski et al. (2021) (ω = 0.91) from a recent validation study conducted in the Polish population. Lastly, ordinal alpha is a more accurate estimate as it is based on the polychoric correlation matrix rather than the Pearson covariance matrix (Gadermann, Guhn, & Zumbo, 2012).
The mean scale score of the Greek WEMWBS for the sample (52.31) was similar to those reported in others studies including those conducted in France (51.88), Slovenia (56.30), Spain (53.5), Denmark (52.2), Austria (54.5), England, and China (47.41) (e.g., Koushede et al. (2019), Trousselard et al. (2016), Castellví et al. (2014), Fung et al. (2019), and López et al. (2012)). The scores had a broad range, spanning 27 to 70, although others have observed lower minimum values of 14 (Clarke et al., 2011). The PCA suggested a three-factor structure, yet the dominance of the first factor—which accounted for roughly half of the total variance—coupled with the marginal contributions of the subsequent factors supported a unidimensional mode. This inference was reinforced by the sharp “elbow” of the scree plot which showed a pronounced discontinuity between the leading eigenvalue and second factor which was very close to 1. Additionally, most items loaded preferentially onto the first factor. Notably, the CFA demonstrated satisfactory model fit indices for a unidimensional construct without model respecification. It is common practice among scholars to use post-estimation alternations, such as the addition of covariances or allowing cross-loading of indicators, that improve the global model fit (Tennant et al., 2007; Fung, 2019; López et al., 2012; Ringdal et al., 2017). However, the presence of many and sizeable modifications risk compromising the model’s theoretical validity through overfitting and can therefore diminish generalizability and replicability across samples. The good model fit observed in this study without re-estimations therefore suggests that the Greek WEMWBS has a robust unidimensional structure.
Item mean scores ranged from 2.81 to 4.28, with most averaging above 3.5. In terms of individual item performance, item 4 (“I’ve been feeling interested in other people”) of the Greek WEMWBS exhibited a low factor structure coefficient in the CFA. Interestingly, this item achieved the highest mean response score. This finding merits further scrutiny, particularly in light of the stable Cronbach’s alpha coefficient remaining unaffected by the item’s removal. Item 4 has shown poor performance in other similar previous studies in various analyses (e.g., Fung et al. (2019) and Waqas et al. (2015)). It is possible that its low factor loading could be attributed to linguistic issues such as ambiguity, double meaning, sensitivity, or bias. Indeed, Taggart et al., (2013) found that the statement was misinterpreted among Chinese and Pakistani cohorts in the UK where it was construed as indicating romantic interest. Gender-based differential item functioning might also contribute to the item’s performance (Steward-Brown et al., 2009). Indeed, Stewart-Brown et al., (2009), the proponents of the shortened WEMWBS (SWEMWBS), suggested that this item may be a candidate for removal based on a Rasch analysis of data from the Scottish Health Education Population Survey. Indeed, the SWEMWBS is preferred by some for its psychometric properties and convenience. Nonetheless, the shortened version of the scale appears to relate more to functioning than affect and the authors advocate for the use of the full 14-item inventory as part of further validation studies and dimensionality analyses.
In line with previous findings, the present study discovered that the WEMWBS questionnaire was straightforward to complete and a tool capturing both emotions and functioning (Compton et al., 1996; Keyes et al., 2002; Waterman, 1993). The analyses revealed that the questionnaire fitted with the data well and there were no variations in results based on gender, age, or marital status. The variations in overall WEMWBS scores according to health and financial status are consistent with the extant literature. Indeed, health status correlates robustly with subjective well-being across diverse clinical and general population cohorts (Ngamaba et al., 2017), and financial strain has been linked to poorer psychological and physiological outcomes (McCloud & Bann, 2019; Roberts et al., 2000). These issues can be exacerbated among nontraditional mature students (Lauder & Cuthbertson, 1998) who have more life roles and responsibilities compared to traditional college students (Ely, 1997; Fairchild, 2003; Kasworm, 2003; Kim, 2002; Villela & Hu, 1991). This dual burden of academic objectives and external responsibilities likely impacts not only their educational attainment and continuity but also their mental well-being (Scott et al., 1996; Muilenburg & Berge, 2001; Tressman, 2002). The role of being a student requires a redefinition of priorities and the effective management of resources among various obligations, including time, personal-psychological well-being, and finances. As the focus shifts towards positive health measures, it is important to measure the subjective mental well-being of adult learners in Greece to better understand their experiences. Nonetheless, it is important to take into account factors such as unemployment and poor health status when interpreting these findings. These socioeconomic determinants not only inform the heterogeneity of responses but likely also signal the need for targeted interventions within vulnerable subpopulations. Overall, these findings support the applicability of the Greek-translated version of the WEMWBS for measuring mental well-being in different cultural and cross-cultural contexts.
The correlations between WEMWBS scores and scores on measures of psychological variables used in this study were in line with expectations. There were statistically significant correlations between the DASS-21 anxiety, depression, and stress subscales, as well as the IASMHS help-seeking propensity (HSP) and indifference to stigma (IS) subscales, and the total scores on these scales. Consistent with other studies that employed similar scales to measure related constructs, the findings of the current study indicated that individuals with higher levels of mental well-being experienced less stress and anxiety, reported fewer and less impactful stressful events, and were more inclined to seek help when needed (e.g., Gulliver et al. (2010) and Rickwood et al. (2007)). These results support the notion that higher psychological well-being is associated with positive outcomes in various domains.
Despite its contribution, this study is not without limitations. Firstly, although adequate in size, participants were drawn using a convenience sampling strategy which limits the broader generalizability of the results (Trousselard et al., 2016). Secondly, the cross-sectional design did not allow for test–retest reliability which is crucial for establishing temporal stability. Finally, this study did not include comparisons with scales such as the Positive and Negative Affect Scale (PANAS), the Scale of Psychological Well-Being (SPWB), the Short Depression Happiness Scale (SDHS), and the Global Life Satisfaction (GLS) scale, as Tennant et al. (2007) did when validating the original scale. This omission was either due to the unavailability of reliable Greek-translated versions of these scales or concerns about the length of the questionnaire potentially discouraging participants. To address this limitation, the present study compared the WEMWBS with other scales that measure related constructs to evaluate the criterion validity of the WEMWBS following the example of previous studies (Castellvi et al., 2014; Clarke et al., 2011; Dong et al., 2016; Ringdal et al., 2017; Santos et al., 2015; Taggart et al., 2013).
Conclusions
The findings of this study substantiate the use of the Greek WEMWBS among university students, demonstrating satisfactory internal consistency and convergent and factorial validity, thereby affirming its utility for assessing mental well-being in this demographic. However, further research is required to enhance our understanding of the instrument’s validity across various Greek subpopulations including individuals of different age groups and both clinical and nonclinical populations. Additionally, future studies assessing well-being using WEMWBS and employing a longitudinal design may offer valuable insights towards enhancing the assessment of well-being and improvement strategies over time. Mental well-being has gained recognition as a component of psychological well-being. Consequently, there is a growing need for increased attention to well-being within counselling services and mental health initiatives, in schools, universities, workplaces, hospitals, and other relevant settings. The validated WEMWBS has the potential to be an instrument, for researchers, healthcare professionals, and other individuals involved in assessing the present condition of mental well-being, in Greece.
Data Availability
Data is available upon request.
References
Anargyros, K. P., Lappas, A. S., & Christodoulou, N. G. (2021). Community mental health services in Greece: Development, challenges and future directions. Consortium Psychiatricum, 2(4), 62–67. https://doi.org/10.17816/CP111
Antony, M. M., Bieling, P. J., Cox, B. J., Enns, M. W., & Swinson, R. P. (1998). Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychological Assessment, 10(2), 176–181. https://doi.org/10.1037/1040-3590.10.2.176
Barnett, B. E. W., Hanna, B., & Parker, G. (1983). Life event scales for obstetric groups. Journal of Psychosomatic Research, 27(4), 313–320. https://doi.org/10.1016/0022-3999(83)90054-5
Bass, M., Dawkin, M., Muncer, S., Vigurs, S., & Bostock, J. (2016). Validation of Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in a population of people using secondary care mental health services. Journal of Mental Health, 25(4), 323–329. https://doi.org/10.3109/09638237.2015.1124401
Bech, P. (2004). Measuring the dimension of psychological general well-being by the WHO-5. Quality of Life Newsletter, 32, 15–16.
Beddington, J., Cooper, C. L., Field, J., Goswami, U., Huppert, F. A., Jenkins, R., Jones, H. S., Kirkwood, T. B. L., Sahakian, B. J., & Thomas, S. M. (2008). The mental wealth of nations. Nature, 455(7216), 1057–1060. https://doi.org/10.1038/4551057a
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588
Bottesi, G., Ghisi, M., Altoè, G., Conforti, E., Melli, G., & Sica, C. (2015). The Italian version of the Depression Anxiety Stress Scales-21: Factor structure and psychometric properties on community and clinical samples. Comprehensive Psychiatry, 60, 170–181. https://doi.org/10.1016/j.comppsych.2015.04.005
Brown, T. A. (2014). Confirmatory factor analysis for applied research (2nd ed.). Guilford Publications.
Brown, G. W., & Harris, T. (1978). Social origins of depression. Tavistock.
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005
Castellví, P., Forero, C. G., Codony, M., Vilagut, G., Brugulat, P., Medina, A., Gabilondo, A., Mompart, A., Colom, J., Tresserras, R., Ferrer, M., Stewart-Brown, S., & Alonso, J. (2014). The Spanish version of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) is valid for use in the general population. Quality of Life Research, 23(3), 857–868. https://doi.org/10.1007/s11136-013-0513-7
Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. https://doi.org/10.1207/s15327906mbr0102_10
Cattell, R. B., & Vogelmann, S. (1977). A comprehensive trial of the scree and Kg criteria for determining the number of factors. Multivariate Behavioral Research, 12(3), 289–325. https://doi.org/10.1207/s15327906mbr1203_2
Chida, Y., & Steptoe, A. (2008). Positive psychological well-being and mortality: A quantitative review of prospective observational studies. Psychosomatic Medicine, 70(7), 741–756. https://doi.org/10.1097/psy.0b013e31818105ba
Christidis, D.A., Yaglis, G.D., & Konstantinidou, A. (2004). The basic research for the establishment of the Centre for Counselling and Psychological Support of the Aristotle University of Thessaloniki. Scientific Yearbook of the Department of Psychology, the Faculty of Philosophy, 6. http://www.relax-now.gr/host/christidis/Elinikes_selides/Dimosiefseis/kesipsi/kesipsi.html. [in Greek].
Christopoulos, A. L., Konstantinidou, M., Lambiri, V., Leventidou, M., Manou, T., Mavroidi, K., Pappas, V., & Tzoumalakis, L. (1997). University students in Athens: Mental health and attitudes toward psychotherapeutic intervention. In A. Kalantzi-Azizi, G. Rott, & D. Aherne (Eds.), Psychological counselling in higher education: Practice and research (pp. 176–191). FEDORA - Ellinika Grammata.
Cilar, L., Pajnkihar, M., & Štiglic, G. (2020). Validation of the Warwick‐Edinburgh Mental Well‐being Scale among nursing students in Slovenia. Journal of Nursing Management. Portico. https://doi.org/10.1111/jonm.13087
Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
Clarke, A., Putz, R., Friede, T., Ashdown, J., Adi, Y., Martin, S., Flynn, P., Blake, A., Stewart-Brown, S., & Platt, S. (2010). Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) acceptability and validation in English and Scottish secondary school students. NHS Health Scotland. http://www.healthscotland.scot/media/1720/16796-wavesfinalreport.pdf
Clarke, A., Friede, T., Putz, R., Ashdown, J., Martin, S., Blake, A., Adi, Y., Parkinson, J., Flynn, P., Platt, S., & Stewart-Brown, S. (2011). Warwick-Edinburgh Mental Well-Being Scale (WEMWBS): Validated for teenage school students in England and Scotland. A mixed methods assessment. BMC Public Health, 11(1), 487. https://doi.org/10.1186/1471-2458-11-487
Compton, W. C., Smith, M. L., Cornish, K. A., & Qualls, D. L. (1996). Factor structure of mental health measures. Journal of Personality & Social Psychology, 71(2), 406–413. https://doi.org/10.1037/0022-3514.71.2.406
DeVellis, R.F. (2016). Scale development: Theory and applications (Vol. 26). SAGE.
Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71–75. https://doi.org/10.1207/s15327752jpa4901_13
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125(2), 276–302. https://doi.org/10.1037/0033-2909.125.2.276
Diener, E., Oishi, S., & Lucas, R. E. (2009). Subjective well-being: The science of happiness and life satisfaction. The Oxford handbook of positive psychology (pp. 186–194). https://doi.org/10.1093/oxfordhb/9780195187243.013.0017
DiStefano, C., & Morgan, G. B. (2014). A comparison of diagonal weighted least squares robust estimation techniques for ordinal data. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 425–438. https://doi.org/10.1080/10705511.2014.915373
Doherty, D. T., & Kartalova-O’Doherty, Y. (2010). Gender and self-reported mental health problems: Predictors of help seeking from a general practitioner. British Journal of Health Psychology, 15(1), 213–228. https://doi.org/10.1348/135910709x457423
Dolan, P., Layard, R., & Metcalfe, R. (2011). Measuring subjective well-being for public policy. Office for National Statistics. https://eprints.lse.ac.uk/47518/1/CEPSP23.pdf
Dong, A., Chen, X., Zhu, L., Shi, L., Cai, Y., Shi, B., & Guo, W. (2016). Translation and validation of a Chinese version of the Warwick- Edinburgh Mental Well-being Scale with undergraduate nursing trainees. Journal of Psychiatric and Mental Health Nursing, 23, 554–560.
dos Santos, J. J. A., da Costa, T. A., Guilherme, J. H., da Silva, W. C., Abentroth, L. R. L., Krebs, J. A., & Sotoriva, P. (2015). Adaptation and cross-cultural validation of the Brazilian version of the Warwick-Edinburgh Mental Well-Being Scale. Revista Da Associação Médica Brasileira, 61(3), 209–214. https://doi.org/10.1590/1806-9282.61.03.209
Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105, 399–412.
Efstathiou, G., Efthymiou, K., & Kalantzi-Azizi, A. (2003). Psychological needs, attitudes and expectations of students regarding the operation of student counselling centres. Proceedings of the 3rd Psychological Conference of Cyprus «Psychology Today: Cyprus and European Reality» (pp. 87–110). Cypriot Federation of Psychologists [in Greek].
Efthymiou, K. (2003). Design of a social policy programme in an Athenian municipality: Preliminary results of an epidemiological survey. Proceedings of the 3rd Psychological Conference of Cyprus «Psychology Today: Cyprus and European Reality» (pp. 73–86). Cypriot Federation of Psychologists [in Greek].
Efthymiou, K. (2007). An epidemiological needs assessment for the establishment of a community mental health service: The case of an Athenian municipality. Unpublished doctoral dissertation. Department of Philosophy, Pedagogy, & Psychology, Psychology Program, University of Athens. [in Greek].
Efthymiou, K., Efstathiou, G., & Kalantzi-Azizi, A. (2001). The need for counselling: A survey of the university student population. Proceedings of the 1st International Conference of the National Center for Vocational Guidance «Developments in Counselling and Career Guidance at the dawn of the 21st Century» (pp. 234–244). National Center for Vocational Guidance.
Efthymiou, K., Efstathiou, G., & Kalantzi-Azizi, A. (2007). Panhellenic epidemiological survey of mental health in the student population. Topos Publications [in Greek].
Ely, E. (1997, April). The non-traditional student. Paper presented at the American Association of Community Colleges 77th Annual Conference, Anaheim, CA. http://eric.ed.gov/ERICDocs/data/ericdocs2sql/content_storage_01/0000019b/80/14/fa/be.pdf
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989x.4.3.272
Fairchild, E. E. (2003). Multiple roles of adult learners. New Directions for Student Services, 102, 11–16. https://doi.org/10.1002/ss.84
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE.
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications Ltd.
Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491. https://doi.org/10.1037/1082-989x.9.4.466
Forsman, A. K., Wahlbeck, K., Aaro, L. E., Alonso, J., Barry, M. M., Brunn, M., Cardoso, G., Cattan, M., de Girolamo, G., Eberhard-Gran, M., Evans-Lacko, S., Fiorillo, A., Hansson, L., Haro, J. M., Hazo, J.-B., Hegerl, U., Katschnig, H., Knappe, S., & Luciano, M. (2015). Research priorities for public mental health in Europe: Recommendations of the ROAMER project. The European Journal of Public Health, 25(2), 249–254. https://doi.org/10.1093/eurpub/cku232
Fung, S. F. (2019). Psychometric evaluation of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) with Chinese University Students. Health and Quality of Life Outcomes, 17(1). https://doi.org/10.1186/s12955-019-1113-1
Gable, S. L., & Haidt, J. (2005). What (and why) is positive psychology? Review of General Psychology, 9(2), 103–110. https://doi.org/10.1037/1089-2680.9.2.103
Gadermann, A. M., Guhn, M., & Zumbo, B. D. (2012). Estimating ordinal reliability for Likert-type and ordinal itemresponse data: A conceptual, empirical, and practical guide. Practical Assessment, Research, and Evaluation, 17(1), 3. https://doi.org/10.7275/n560-j767
George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference: 11.0 update, (4th ed.). Allyn & Bacon.
Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505
González-Rivera, J. A., Pagán-Torres, O. M., & Pérez-Torres, E. M. (2020). Depression, Anxiety and Stress Scales (DASS-21): Construct validity problem in Hispanics. European Journal of Investigation in Health, Psychology and Education, 10(1), 375–389. https://doi.org/10.3390/ejihpe10010028
Gulliver, A., Griffiths, K. M., & Christensen, H. (2010). Perceived barriers and facilitators to mental health help-seeking in young people: A systematic review. BMC Psychiatry, 10(1). https://doi.org/10.1186/1471-244x-10-113
Gupta, A., Mishra, P., Pandey, C., Singh, U., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67. https://doi.org/10.4103/aca.aca_157_18
Hair, J. F. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
Holmes, T. H., & Rahe, R. H. (1967). The social readjustment rating scale. Journal of Psychosomatic Research, 11(2), 213–218. https://doi.org/10.1016/0022-3999(67)90010-4
Holmes, T. H., & Rahe, R. H. (1996). Life is stress. Introducing Psychological Research, 355–359. https://doi.org/10.1007/978-1-349-24483-6_53
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6, 53–60.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Huppert, F. (2014). The state of wellbeing science: Concepts, measures, interventions, and policies. In F. A. Huppert & C. L. Cooper (Eds.), Interventions and policies to enhance wellbeing (pp. 1–49). John Wiley & Sons. https://doi.org/10.1002/9781118539415.wbwell036
Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. https://doi.org/10.1007/bf02289343
Joseph, S., Linley, P. A., Harwood, J., Lewis, C. A., & McCollam, P. (2004). Rapid assessment of well-being: The Short Depression-Happiness Scale (SDHS). Psychology and Psychotherapy: Theory, Research and Practice, 77(4), 463–478. https://doi.org/10.1348/1476083042555406
Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. https://doi.org/10.1177/001316446002000116
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/bf02291575
Kasworm, C. E. (2003). Setting the stage: Adults in higher education. New Directions for Student Services, 102, 3–10. https://doi.org/10.1002/ss.83
Kendler, K. S., Karkowski, L. M., & Prescott, C. A. (1999). Causal relationship between stressful life events and the onset of major depression. The American Journal of Psychiatry, 156(6), 837–841. https://doi.org/10.1176/ajp.156.6.837
Keyes, C. L. M. (2002). The mental health continuum: From languishing to flourishing in life. Journal of Health and Social Behavior, 43(2), 207. https://doi.org/10.2307/3090197
Keyes, C. L. M., & Simoes, E. J. (2012). To flourish or not: Positive mental health and all-cause mortality. American Journal of Public Health, 102(11), 2164–2172. https://doi.org/10.2105/ajph.2012.300918
Keyes, C. L. M., Shmotkin, D., & Ryff, C. D. (2002). Optimizing well-being: The empirical encounter of two traditions. Journal of Personality & Social Psychology, 82(6), 1007–1022. https://doi.org/10.1037/0022-3514.82.6.1007
Keyes, C. L. M., Dhingra, S. S., & Simoes, E. J. (2010). Change in level of positive mental health as a predictor of future risk of mental illness. American Journal of Public Health, 100(12), 2366–2371. https://doi.org/10.2105/ajph.2010.192245
Kim, K. A. (2002). ERIC review: Exploring the meaning of “nontraditional” at the community college. Community College Review, 30(1), 74–89. https://doi.org/10.1177/009155210203000104
Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52. https://doi.org/10.5395/rde.2013.38.1.52
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
Konaszewski, K., Niesiobędzka, M., & Surzykiewicz, J. (2021). Factor structure and psychometric properties of a Polish adaptation of the Warwick-Edinburgh Mental Wellbeing Scale. Health and Quality of Life Outcomes, 19, 70. https://doi.org/10.1186/s12955-021-01716-w
Koushede, V., Lasgaard, M., Hinrichsen, C., Meilstrup, C., Nielsen, L., Rayce, S. B., Torres-Sahli, M., Gudmundsdottir, D. G., Stewart-Brown, S., & Santini, Z. I. (2019). 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, 271, 502–509. https://doi.org/10.1016/j.psychres.2018.12.003
Lang, G., & Bachinger, A. (2017). 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, 25(2), 135–146. https://doi.org/10.1007/s10389-016-0778-8
Lauder, W., & Cuthbertson, P. (1998). Course-related family and financial problems of mature nursing students. Nurse Education Today, 18(5), 419–425. https://doi.org/10.1016/0021-8634(92)80013-i
Li, C.-H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48(3), 936–949. https://doi.org/10.3758/s13428-015-0619-7
Lionetti, F., Keijsers, L., Dellagiulia, A., & Pastore, M. (2016). Evidence of factorial validity of parental knowledge, control and solicitation, and adolescent disclosure scales: When the ordered nature of Likert scales matters. Frontiers in Psychology, 7, 941. https://doi.org/10.3389/fpsyg.2016.00941
Lloyd, K., & Devine, P. (2012). Psychometric properties of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) in Northern Ireland. Journal of Mental Health, 21(3), 257–263. https://doi.org/10.3109/09638237.2012.670883
Loewenthal, K. M. (2001). An introduction to psychological tests and scales. (2nd ed.). Psychology Press.
López, M. A., Gabilondo, A., Codony, M., García-Forero, C., Vilagut, G., Castellví, P., Ferrer, M., & Alonso, J. (2012). Adaptation into Spanish of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) and preliminary validation in a student sample. Quality of Life Research, 22(5), 1099–1104. https://doi.org/10.1007/s11136-012-0238-z
Lovibond, P., & Lovibond, S. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research & Therapy, 33(3), 335–343. https://doi.org/10.1016/0005-7967(94)00075-u
Lyubomirsky, S., King, L., & Diener, E. (2005). The benefits of frequent positive affect: Does happiness lead to success? Psychological Bulletin, 131(6), 803–855. https://doi.org/10.1037/0033-2909.131.6.803
MacKean, G. (2011). Mental health and well-being in postsecondary education settings: A literature and environmental scan to support planning and action in Canada. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.737.6978&rep=rep1&type=pdf
Mackenzie, C. S., Knox, V. J., Gekoski, W. L., & Macaulay, H. L. (2004). An adaptation and extension of the attitudes toward seeking professional psychological help scale1. Journal of Applied Social Psychology, 34(11), 2410–2433. https://doi.org/10.1111/j.1559-1816.2004.tb01984.x
Mackenzie, C. S., Gekoski, W. L., & Knox, V. J. (2006). Age, gender, and the underutilization of mental health services: The influence of help-seeking attitudes. Aging & Mental Health, 10(6), 574–582. https://doi.org/10.1080/13607860600641200
Maheswaran, H., Weich, S., Powell, J., & Stewart-Brown, S. (2012). Evaluating the responsiveness of the Warwick Edinburgh Mental Well-Being Scale (WEMWBS): Group and individual level analysis. Health and Quality of Life Outcomes, 10(1), 156. https://doi.org/10.1186/1477-7525-10-156
Malikiosi-Louizou, M. (1989). Psychological - educational - social problem of students of higher education. Counselling - Guidance Review, 10–11, 23–31. [in Greek].
Marlowe, N. (1998). Stressful events, appraisal, coping, and recurrent headache. Journal of Clinical Psychology, 54(2), 247–256. https://doi.org/10.1002/(SICI)1097-4679(199802)54:2
May, T (2017). Mental health problems are everyone’s problem: Article by Theresa May. GOV.UK. https://www.gov.uk/government/speeches/mental-health-problems-are-everyones-problem-article-by-theresa-may
McCloud, T., & Bann, D. (2019). Financial stress and mental health among higher education students in the UK up to 2018: Rapid review of evidence. Journal of Epidemiology and Community Health, 73(10), 977–984. https://doi.org/10.1136/jech-2019-212154
McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23(3), 412–433. https://doi.org/10.1037/met0000144
Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2005). Applied multivariate research: Design and interpretation. SAGE Publications.
Miller, T., & King, F. (2003). Distance education: Pedagogy and best practices in the new millennium. International Journal of Leadership in Education, 6(3), 283–297. https://doi.org/10.1080/1360312032000118225
Mîndrilă, D. (2010). Maximum likelihood (ML) and diagonally weighted least squares (DWLS) estimation procedures: A comparison of estimation bias with ordinal and multivariate non-normal data. International Journal for Digital Society, 1(1), 60–66. https://doi.org/10.20533/ijds.2040.2570.2010.0010
Mintrom, M. (2019). New Zealand’s Wellbeing Budget invests in population health. The Milbank Quarterly, 97(4), 893–896. http://www.jstor.org/stable/45237115
Monroe, S. M., & Simons, A. D. (1991). Diathesis-stress theories in the context of life stress research: Implications for the depressive disorders. Psychological Bulletin, 110(3), 406–425. https://doi.org/10.1037/0033-2909.110.3.406
Muilenburg, L., & Berge, Z. L. (2001). Barriers to distance education: A factor-analytic study. American Journal of Distance Education, 15(2), 7–22. https://doi.org/10.1080/08923640109527081
Navridis, K., Dragona, Th., Miliarini, B., & Damigos, D. (1990). Counseling center for students of the University of Ioannina: A transitional framework for a transitional age. University of Ioannina Publications. [in Greek]
Ngamaba, K. H., Panagioti, M., & Armitage, C. J. (2017). How strongly related are health status and subjective well-being? Systematic review and meta-analysis. European Journal of Public Health, 27(5), 879–885. https://doi.org/10.1093/eurpub/ckx081
Nolte, E., & McKee, M. (2008). Caring for people with chronic conditions. A health system perspective. Open University Press. http://www.euro.who.int/—data/assets/pdf-file/0006/96468/E91878.pdf
Nunnally, J. C., & Bernstein, I. H. (1994). The Assessment of Reliability. Psychometric Theory, 3, 248–292.
O’Connor, B. P. (2002). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, & Computers, 32(3), 396–402. https://doi.org/10.3758/bf03200807
OECD (2013). OECD guidelines on measuring subjective well-being. https://doi.org/10.1787/9789264191655-en
Orgeta, V., Lo Sterzo, E., & Orrell, M. (2013). Assessing mental well-being in family carers of people with dementia using the Warwick-Edinburgh Mental Well-Being Scale. International Psychogeriatrics, 25(9), 1443–1451. https://doi.org/10.1017/s1041610213000835
Oswald, A. J., & Wu, S. (2010). Objective confirmation of subjective measures of human well-being: Evidence from the U.S.A. Science, 327(5965), 576–579. https://doi.org/10.1126/science.1180606
Öztuna, D., Elhan, A.H., & Tuccar, E. (2006). Investigation of four different normality tests in terms of type 1 error rate and power under different distributions. Turkish Journal of Medical Science. https://journals.tubitak.gov.tr/medical/issues/sag-06-36-3/sag-36-3-7-0510-10.pdf
Pallant, J. (2005). SPSS survival manual. Open University Press.
Papadioti-Athanasiou, V., & Damigos, D. (2003). Needs and psychosocial adaptation of students of the University of Ioannina. Scientific Yearbook of the Department of Philosophy, Pedagogy and Psychology of the Faculty of Philosophy of the University of Ioannina (pp. 231–262). Dodoni. [in Greek].
Pezirkianidis, C., Karakasidou, E., Lakioti, A., Stalikas, A., & Galanakis, M. (2018). Psychometric properties of the Depression, Anxiety, Stress Scales-21 (DASS-21) in a Greek sample. Psychology, 9(15), 2933–2950. https://doi.org/10.4236/psych.2018.915170
Polit, D. F., & Beck, C. T. (2012). Nursing research: Generating and assessing evidence for nursing practice. Wolters Kluwer Health.
Rickwood, D. J., Deane, F. P., Wilson, C. J. (2007). When and how do young people seek professional help for mental health problems? Medical Journal of Australia, 187(S7) Portico. https://doi.org/10.5694/j.1326-5377.2007.tb01334.x
Ringdal, R., Bradley Eilertsen, M. E., Bjørnsen, H. N., Espnes, G. A., & Moksnes, U. K. (2017). Validation of two versions of the Warwick-Edinburgh Mental Well-Being Scale among Norwegian adolescents. Scandinavian Journal of Public Health, 46(7), 718–725. https://doi.org/10.1177/1403494817735391
Roberts, R., Golding, J., Towell, T., Reid, S., Woodford, S., Vetere, A., & Weinreb, I. (2000). Mental and physical health in students: The role of economic circumstances. British Journal of Health Psychology, 5(3), 289–297. https://doi.org/10.1348/135910700168928
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2). https://doi.org/10.18637/jss.v048.i02
Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52(1), 141–166. https://doi.org/10.1146/annurev.psych.52.1.141
Ryff, C. D., & Keyes, C. L. M. (1995). The structure of psychological well-being revisited. Journal of Personality and Social Psychology, 69(4), 719–727. https://doi.org/10.1037/0022-3514.69.4.719
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–338. https://doi.org/10.3200/joer.99.6.323-338
Scott, C., Burns, A., & Cooney, G. (1996). Reasons for discontinuing study: The case of mature age female students with children. Higher Education, 31(2), 233–253. https://doi.org/10.1007/bf02390446
Seligman, M. E. (1972). Learned helplessness. Annual Review of Medicine, 23, 407–412. https://doi.org/10.1146/annurev.me.23.020172.002203
Seligman, M. E. P., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55(1), 5–14. https://doi.org/10.1037/0003-066X.55.1.5
Siahpush, M., Spittal, M., & Singh, G. K. (2008). Happiness and life satisfaction prospectively predict self-rated health, physical health, and the presence of limiting, long-term health conditions. American Journal of Health Promotion, 23(1), 18–26. https://doi.org/10.4278/ajhp.061023137
Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107–120. https://doi.org/10.1007/s11336-008-9101-0
Sinclair, S. J., Siefert, C. J., Slavin-Mulford, J. M., Stein, M. B., Renna, M., & Blais, M. A. (2012). Psychometric evaluation and normative data for the Depression, Anxiety, and Stress Scales-21 (DASS-21) in a nonclinical sample of U.S adults. Evaluation & the Health Professions, 35(3), 259–279. https://doi.org/10.1177/0163278711424282
Slade, M. (2010). Mental illness and well-being: The central importance of positive psychology and recovery approaches. BMC Health Services Research, 10(1). https://doi.org/10.1186/1472-6963-10-26
Song, C. F., Tay, P. K. C., Gwee, X., Wee, S. L., & Ng, T. P. (2023). Happy people live longer because they are healthy people. BMC Geriatrics, 23(1). https://doi.org/10.1186/s12877-023-04030-w
Stewart-Brown, S. (2015a). Guidance on scoring. http://www2.warwick.ac.uk/fac/med/research/platform/wemwbs/researchers/guidance/
Stewart-Brown, S. (2015b). Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS). http://www2.warwick.ac.uk/fac/med/research/platform/wemwbs
Stewart-Brown, S., Tennant, A., Tennant, R., Platt, S., Parkinson, J., & Weich, S. (2009). 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 and Quality of Life Outcomes, 7(1). https://doi.org/10.1186/1477-7525-7-15
Stewart-Brown, S., Platt, S., Tennant, A., Maheswaran, H., Parkinson, J., Weich, S., Tennant, R., Taggart, F., & Clarke, A. (2011). The Warwick-Edinburgh Mental Well-Being Scale (WEMWBS): A valid and reliable tool for measuring mental well-being in diverse populations and projects. Journal of Epidemiology & Community Health, 65(Suppl 2), A38–A39. https://doi.org/10.1136/jech.2011.143586.86
Stiglitz, J., Sen, A. & Fitoussi, J-P. (2009). Report by the Commission on the Measurement of Economic Performance and Social Progress. http://www.stiglitz-sen-fitoussi.fr/documents/rapport_anglais.pdf
Taggart, F. (2015). WEMWBS in other languages. http://www2.warwick.ac.uk/fac/med/research/platform/wemwbs/researchers/languages/
Taggart, F., Friede, T., Weich, S., Clarke, A., Johnson, M., & Stewart-Brown, S. (2013). Cross cultural evaluation of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) -A mixed methods study. Health and Quality of Life Outcomes, 11(1), 27. https://doi.org/10.1186/1477-7525-11-27
Tennant, R., Hiller, L., Fishwick, R., Platt, S., Joseph, S., Weich, S., Parkinson, J., Secker, J., & Stewart-Brown, S. (2007). The Warwick-Edinburgh Mental Well-Being Scale (WEMWBS): Development and UK validation. Health and Quality of Life Outcomes, 5(1), 63. https://doi.org/10.1186/1477-7525-5-63
Tennant, R., Fishwick, R., Platt, S., Joseph, S., & Stewart-Brown, S. (2012). Monitoring positive mental health in Scotland: Validating the Affectometer 2 scale and developing the Warwick- Edinburgh Mental Well-Being Scale for the UK. NHS Health Scotland.
Tresman, S. (2002). Towards a strategy for improved student retention in programmes of open, distance education: A case study from the Open University UK. The International Review of Research in Open and Distributed Learning, 3(1). https://doi.org/10.19173/irrodl.v3i1.75
Trousselard, M., Steiler, D., Dutheil, F., Claverie, D., Canini, F., Fenouillet, F., Naughton, G., Stewart-Brown, S., & Franck, N. (2016). Validation of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in French psychiatric and general populations. Psychiatry Research, 245, 282–290. https://doi.org/10.1016/j.psychres.2016.08.050
Ura, K., Alkire, S., Zangmo, T., & Wangdi, K. (2012). An extensive analysis of GNH index. Centre for Bhutan Studies. Retrieved October 1, 2023 from http://www.grossnationalhappiness.com/wp-content/uploads/2012/10/An%20Extensive%20Analysis%20of%20GNH%20Index.pdf
Vasconcelos-Raposo, J., Fernandes, H. M., & Teixeira, C. M. (2013). Factor structure and reliability of the Depression, Anxiety and Stress Scales in a large Portuguese community sample. The Spanish Journal of Psychology, 16. https://doi.org/10.1017/sjp.2013.15
Villella, E. F., & Hu, M. (1991). A factor analysis of variables affecting the retention decision of nontraditional college students. NASPA Journal, 28(4), 334–341. https://doi.org/10.1080/00220973.1991.11072229
Waqas, A., Ahmad, W., Haddad, M., Taggart, F. M., Muhammad, Z., Bukhari, M. H., Sami, S. A., Batool, S. M., Najeeb, F., Hanif, A., Rizvi, Z. A., & Ejaz, S. (2015). Measuring the well-being of health care professionals in the Punjab: A psychometric evaluation of the Warwick-Edinburgh Mental Well-being Scale in a Pakistani population. Peer Journal, 3, e1264. https://doi.org/10.7717/peerj.1264
Waterman, A. S. (1993). Two conceptions of happiness: Contrasts of personal expressiveness (eudaimonia) and hedonic enjoyment. Journal of Personality and Social Psychology, 64(4), 678–691. https://doi.org/10.1037/0022-3514.64.4.678
Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063
Westerhof, G. J., & Keyes, C. L. M. (2009). Mental illness and mental health: The two continua model across the lifespan. Journal of Adult Development, 17(2), 110–119. https://doi.org/10.1007/s10804-009-9082-y
Williams, L. J., Vandenberg, R. J., & Edwards, J. R. (2009). 12 Structural equation modeling in management research: A guide for improved analysis. Academy of Management Annals, 3(1), 543–604. https://doi.org/10.5465/19416520903065683
World Health Organization. (2001). The world health report. Mental health: New understanding, new hope. World Health Organization. http://www.who.int/whr2001/
World Health Organisation. (2004). Promoting mental health: Concepts, emerging evidence, practice: Summary report. World Health Organization. http://apps.who.int/iris/bitstream/10665/42940/1/9241591595.pdf?ua=1
World Health Organization, Regional Office for Europe. (2015). The European mental health action plan 2013–2020. WHO Regional Office for Europe. https://www.euro.who.int/__data/assets/pdf_file/0004/287976/European-Mental-Health-Action-Plan-2013-2020.pdf
Yang-Wallentin, F., Joreskog, K., & Luo, H. (2010). Confirmatory factor analysis of ordinal variables with misspecified models. Structural Equation Modeling: A Multidisciplinary Journal, 17(3), 392–423. https://doi.org/10.1080/10705511.2010.489003
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Konstantinos Petrogiannis and Irina Sangeorzan. The first draft of the manuscript was written by Konstantinos Petrogiannis, Irina Sangeorzan, and Panoraia Andriopoulou, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Petrogiannis, K., Sangeorzan, I. & Andriopoulou, P. Validation of the Greek Warwick-Edinburgh Mental Well-Being Scale (WEMWBS) in a Mature Student Community-Based Sample. ADV RES SCI (2024). https://doi.org/10.1007/s42844-024-00134-3
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DOI: https://doi.org/10.1007/s42844-024-00134-3