Introduction

Well-being is a complex and multidimensional construction, and there is little agreement about its structure and content, as reflected by the number of theories and models that exist (Jayawickreme et al., 2012; Martela & Sheldon, 2019). Most well-being models conceptualize well-being as the presence or absence of certain qualities.

The two main theoretical approaches to well-being and its measurement are hedonism (hedonic well-being, HWB) and eudaimonia (eudaimonic well-being, EWB). The hedonic approach focuses on happiness, defining well-being in terms of pleasure attainment and pain avoidance (Diener, 1984; Kahneman et al., 1999; Lyubomirsky & Lepper, 1999; Huta & Waterman, 2014). The eudaimonic approach relates to meaning and self-realization, considering well-being as the person’s full functioning, as highlighted by positive psychological functioning and human development (Ryff, 1989a; Waterman, 1993; Ryan & Deci, 2001; Huta & Waterman, 2014).

Subjective well-being (SWB), developed from hedonia, encompasses an individual’s affective functioning and subjective appraisal of life (Diener, 1984; Diener et al., 2017). SWB is traditionally characterized by frequent experience of positive affect, rare experience of negative affect, and a feeling of satisfaction with life (Diener, 1984, 2012; Diener et al., 1999, 2017; Tov & Lee, 2016; Busseri, 2018). SWB consists of a cognitive component of appraisal in terms of life satisfaction and an affective component characterized by the prevalence of positive emotions rather than negative ones (Kahneman et al., 1999). Psychological well-being (PWB), evolved from eudaimonia, refers to goal pursuits and personal actualization (Ryan & Deci, 2001; Ryff & Singer, 2008), focusing on resources and strengths, the meaning of life, authenticity, and purposefulness (Waterman et al., 2010). PWB is empirically conceptualized as existential outcomes, including six dimensions: self-acceptance, positive relations with others, autonomy, environmental mastery, purpose in life, and personal growth (Ryff, 1989a; Ryff & Keyes, 1995). Much of the research investigating pathways to well-being has subscribed to either the hedonic or the eudaimonic perspective, utilizing a unilateral approach. In contrast, other research combines hedonic and eudaimonic domains. There is no best well-being model, but different conceptualizations can help to examine the abstract construct of well-being and provide specific domains that can be measured and developed.

While hedonic and eudaimonic conceptions of well-being have always been considered separately, in recent years, the unilateral approach of the study of well-being has aroused interest (Kashdan et al., 2008; Henderson & Knight, 2012), and most researchers now believe that well-being is a multidimensional construct (Diener, 2009; Gallagher et al., 2009; Huppert & So, 2013; Prilleltensky et al., 2015). Well-being is a multi-faceted construct that includes emotional, social, and functional components (Diener & Ryan, 2009). In many multidimensional theories of well-being, efforts are being made to integrate and investigate the main components of well-being (Forgeard et al., 2011; Hone et al., 2014; Butler & Kern, 2016).

One attempt to reconcile hedonic and eudaimonic traditions is the well-being theory or PERMA model, proposed by Seligman (2011). Seligman’s well-being theory integrates the hedonic and eudaimonic views of well-being, proposing that optimal well-being occurs when these two components of well-being are present within an individual at the same time. According to Seligman (2011), flourishing is the gold standard for measuring well-being, and the goal of Positive Psychology is to increase flourishing. This theory conceptualizes well-being holistically as flourishing and combines multiple hedonic and eudaimonic dimensions (Butler & Kern, 2016; Sun et al., 2018; Giangrasso, 2021). It is one of the main conceptualizations and operationalizations of the flourishing construct (Hone et al., 2014).

The PERMA model, as developed by Seligman (2011), outlines five pillars of well-being, which create the acronym PERMA: Positive Emotion (P), Engagement (E), Relationships (R), Meaning (M), and Accomplishment (A). The five elements are included in the model because they meet three criteria: first, the element independently leads to well-being; second, the element can be pursued for its own intrinsic value and not as a means to an end; and third, the element can be defined and measured independently of all others. Seligman (2011) advanced that these five pillars contribute to overall well-being, are important areas that people pursue for their own sake, and “suggests that these five domains can be defined and measured as separate but correlated constructs” (Butler & Kern, 2016, p. 21). The PERMA models suggests that we flourish through balancing the Pleasant Life (feeling good or hedonic well-being) with the Meaningful Life (having purpose, contribution and belonging, or eudaimonic well-being).

Based on Seligman’s theory, Butler and Kern (2016) developed a multidimensional measuring instrument called the PERMA-Profiler to evaluate well-being in multiple fields. These authors define flourishing as “a dynamic optimal state of psychosocial functioning that arises from functioning well across multiple psychosocial domains. We suggest that there is no single best model of well-being, but different conceptualizations can be helpful for taking the abstract construct of well-being and providing concrete domains that can be measured, developed, and sustained. Specifically, we focus here on the five domains defined by Seligman’s (2011) PERMA theory: positive emotion (P), engagement (E), relationships (R), meaning (M), and accomplishment (A)” (Butler & Kern, 2016, p. 2).

According to Seligman’s (2011) model, positive emotions refer to hedonic feelings of happiness (e.g., feeling joyful, content, and cheerful), understood as the affective component or feeling good, in combination with a positive appraisal. As a cornerstone of the well-being model, experiencing positive emotions such as hope, compassion, contentment, empathy, gratitude, joy, or love is considered the essential element contributing to well-being. Engagement refers to a psychological connection to activities or organizations (e.g., feeling absorbed, interested, and engaged in life). Seligman (2011) states that engagement is about flow, or being one with an activity, experiencing a sense of time stopping, and the loss of self-consciousness during an activity. Relationship indicates a feeling of belongingness, support from, and connection with others; it implies the belief that one is cared for, loved, and valued, positive relationships include feeling socially integrated, cared about and supported by others, and satisfied with one’s social connections (Seligman, 2011). Meaning refers to the belief that that one’s life is valuable and feeling connected to something greater than oneself (Seligman, 2011). Accomplishment involves making progress toward goals, feeling capable of doing daily activities, and having a sense of achievement (Seligman, 2011).

After undergoing a comprehensive theoretical and empirical process for two years, the PERMA-Profiler, a 23-item instrument that assesses well-being across five domains, captures the unique psychometric properties of the five PERMA domains by demonstrating “acceptable reliability, cross-time stability, and evidence for convergent and divergent validity” (Butler & Kern, 2016, p.22) in a diverse sample of over 30,000 participants (For details, see Butler & Kern, 2016).

Despite its recent publication, the reliability and validity of the PERMA-Profiler have also been established in other cultural contexts. It has been translated into foreign languages for use with different populations in several countries: Australia (Iasiello et al., 2017), Turkey (Demirci et al., 2017; Ayşe, 2018), Ecuador (Lima-Castro et al., 2017), Indonesia (Hidayat et al., 2018), Germany (Wammerl et al., 2019), Chile (Cobo-Rendón et al., 2020), China (Yang & Mohd, 2021), Italy (Giangrasso, 2021), Greece (Pezirkianidis et al., 2021), Brazilian (de Carvalho et al., 2023) and Mexico (Chaves et al., 2023).

The studies of the PERMA-Profiler carried out in Indonesia (Hidayat et al., 2018), Germany (Wammerl et al., 2019), China (Yang & Mohd, 2021), Greece (Pezirkianidis et al., 2021), Brazilian (de Carvalho et al., 2023) and Mexico (Chaves et al., 2023), have also tested different structural models to compare them and verify which one obtained better fit indexes.

The main objective of this study is to examine the psychometric properties of PERMA-Profiler in a Spanish context. To achieve this objective, we translated and adapted the PERMA-Profiler, analyzed its reliability, validity based on an internal structure with three confirmatory factor analyses, gender and age invariance, and convergent and discriminant validity based on relationships with other constructs.

Materials and Methods

Participants and Procedure

A total of 2525 participants completed all the measures. Their mean age was 33.83 years (SD = 11.28), ranging between 18 and 74 years. There were 495 (19.6%) men in the sample, mean age of 36.62 years (SD = 11.95), age range between 18 and 74 years, and 2030 (80.4%) women, mean age 33.15 years (SD = 11.00), age range between 18 and 69 years. All of them are university students of the first psychology course from the National University of Distance Education (UNED). Due to the characteristics of this university, the students present a great diversity, some only study, others study and work in different areas and professions, have a wide age range, live in different geographical areas of the Spanish territory, both in rural and urban areas, and the number of women enrolled keeps the same percentage as the number of women participants in the study. The participants were contacted through an e-mail that they had individually assigned as students of this university. They were asked to access the platform, web page, of a first-year course to download a PDF file containing a series of questionnaires and the possibility of completing them. The performance of this task was voluntary and they would not receive any incentive. Once completed, they had to send them to an e-mail address created for this purpose. Table 1 describes the participants.

Table 1 Participant
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All individuals gave their written informed consent to participate in the study. The data provided were anonymous and were treated according to Spanish law regarding general data protection. This study followed the Declaration of Helsinki (World Medical Association, 2013) and ethical guidelines.

The PERMA-Profiler was translated into Spanish following the International Test Commission Guidelines for Translating and Adapting Tests (International Test Commission [ITC], 2017). The final Spanish version of the PERMA-Profiler is presented in Table 2.

Table 2 Spanish version of the PERMA-Profiler
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Measures

PERMA-Profiler

The PERMA-Profiler was developed by Butler and Kern (2016) to measure Seligman’s (2011) PERMA model of flourishing. This model advocates that flourishing arises from five well-being pillars or domains: Positive Emotion (P), Engagement (E), Relationships (R), Meaning (M), and Accomplishment (A), abbreviated as the acronym PERMA, which groups together the model’s five main factors. Focusing on the five domains defined by Seligman’s PERMA theory and through an extensive theoretical and empirical process, Butler and Kern (2016) developed and validated the PERMA-Profiler, a measure that evaluates well-being in the five domains.

The PERMA-Profiler is a 23-item multidimensional questionnaire, of which 15 items represent the five PERMA domains, and each domain is evaluated through 3 items. Positive Emotion measures general tendencies toward feeling contentment and joy. Engagement refers to being absorbed, interested, and involved in an activity or the world itself; very high levels of engagement are known as a state called “flow,” where one is so completely absorbed by an activity that one loses all sense of time. Relationships refer to feeling loved, supported, and valued by others. Meaning refers to having a sense of purpose in life, a direction for one’s life, feeling that life is valuable and worth living, or connecting to something greater than onsself. Accomplishment measures subjective feelings of achievement and managing daily responsibilities; it involves working toward and reaching goals and feeling capable of completing tasks and daily responsibilities.

Besides PERMA’s 15 items, the measure includes 8 filler items, which aim to disrupt response trends and provide additional information about participants. The filler items comprise an element called Happiness, which evaluates overall happiness; three elements called Negative Emotion, which evaluate a tendency to feel sad, anxious, and angry; an element called Loneliness, which evaluates a state of being solitary, and three elements called Physical Health, which evaluate self-perceived physical health and vitality.

Each item is scored on an 11-point Likert-type scale anchored by 0 (never) to 10 (always), 0 (not at all) to 10 (completely), or 0 (terrible) to 10 (excellent), depending on the item content. Scores are calculated as the average of the items comprising each domain.

A general well-being score, Overall Well-being (PERMA), is calculated by adding items from the five PERMA domains and the Happiness item as indicated Butler and Kern (2016).

Butler and Kern pointed out that the 15 PERMA questions or items (3 items for each PERMA domain) could be used as a brief form, but they recommend applying the full measure with the 23 items. “A particular benefit of the measure is that it assesses well-being across multiple domains. We suggest that in presenting individual or group results, the multidimensional structure of the measure should be retained, rather than condensing responses to a single flourishing score” (Butler & Kern, p.21).

The PERMA-Profiler has shown acceptable psychometric properties in evaluations performed with several different international samples, and most of the available data concerning the psychometric properties are found in the original study of its development and validation. The authors concluded that “through an intensive process, we created a measure that, at both content and analytical levels, captures the five PERMA domains. The measure demonstrates acceptable reliability, cross-time stability, and evidence for convergent and divergent validity” (Butler & Kern, 2016, p. 22). We used the Spanish version of the PERMA-Profiler translated into Spanish (see Table 2).

Satisfaction with Life Scale (SWLS)

The Satisfaction with Life Scale (SWLS) was developed by Diener et al. (1985) as a measure to assess an individual’s global judgment of their life satisfaction (Diener et al., 1985). “The SWLS items are global rather than specific in nature, allowing respondents to weigh domains of their lives in terms of their own values, in arriving at a global judgment of life satisfaction” (Pavot & Diener, 1993, p. 164).

This 5-item unifactorial instrument assesses global cognitive aspects of subjective well-being. Participants rate their degree of agreement with each statement using a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). All 5 items are expressed positively. Scores can range from 5 to 35 points, with higher scores indicating greater life satisfaction. We used the Spanish version located in the public domain of Ed Diener, the main author of the original scale. The scale’s internal consistency in the present study was good (α = 0.87).

Life Orientation Test-Revised (LOT-R)

The Life Orientation Test-Revised (LOT-R) was developed by Scheier et al. (1994). This10-item self-report scale assesses generalized expectations of positive and negative outcomes. Only 6 of the 10 items are used to derive an optimism score. Three items are drafted positively (direction of optimism) and three negatively (direction of pessimism). The remaining 4 items are filler items.

Participants rate their degree of agreement with each item on a five-point Likert scale ranging from 0 (strongly disagree) to 4 (strongly agree). Negatively drafted items are score-reversed, and their score is added to the positively drafted items, leading to a total score oriented towards the optimism pole. Scores can range from 0 to 24, with higher scores indicating higher levels of dispositional optimism. In a review, the test’s authors recommend that the LOT-R be used as a one-dimensional scale (Carver et al., 2010).

We used the LOT-R version adapted for the Spanish population (Otero et al., 1998; Ferrando et al., 2002). In the present study, the scale demonstrated good internal consistency (α = 0.78).

Positive and Negative Affect Schedule (PANAS)

The Positive and Negative Affect Schedule (PANAS) was developed by Watson et al. (1988), providing a brief measure of positive and negative affect. It consists of 20 items (words) describing affect. It measures two independent and uncorrelated dimensions: 10 words assess positive affect (Positive Affect Scale), and 10 words assess negative affect (Negative Affect Scale). The PANAS can be used to assess affective states (e.g., present moment, today, past few days), moods (e.g., past week, past month), and traits, depending on the time frame provided by the instructions (Watson & Clark, 1997).

Participants rate the extent to which the words represent how they feel on a 5-point Likert-type scale ranging from 1 (very slightly or never) to 5 (extremely). Participants can respond according to one or more instructions: (a) at this moment, (b) today, (c) in the past few days, (d) in the past week, (e) in the past few weeks, (f) in the past month, (g) in the past year, (h) in general.

“Positive Affect (PA) reflects the extent to which a person feels enthusiastic, active, and alert. High PA is a state of high energy, full concentration, and pleasurable engagement, whereas low PA is characterized by sadness and lethargy. In contrast, Negative Affect (NA) is a general dimension of subjective distress and unpleasurable engagement that subsumes a variety of aversive mood states, including anger, contempt, disgust, guilt, fear, and nervousness, with low NA being a state of calmness and serenity” (Watson et al., 1988, p. 1063).

We used the Spanish adaptation of Sandín et al. (1999). In this investigation, we asked participants to respond according to how they feel in the past few days (Watson & Clark, 1997). In the present study, the PANAS demonstrated good internal consistency: α = 0.91 for the Positive Affect and α = 0.88 for the Negative Affect.

Psychological Well-being Scales (PWB)

The Psychological Well-being Scales (PWB) or Ryff’s Psychological Well-being Scales (PWB) were designed by Ryff (1989a, b) to test her six-component model of personal growth and psychological well-being. It was specifically designed to measure positive aspects of psychological functioning on six theoretically-motivated dimensions: Self-Acceptance (positive attitude towards oneself and one’s past life), Positive Relations with Others (having satisfying, high-quality relationships), Autonomy (independence and self-determination), Environmental Mastery (ability to manage one’s life), Personal Growth (being open to new experiences) and Purpose in Life (believing that one’s life is meaningful) (Ryff, 1989a, b; Ryff & Keyes, 1995).

The original version consists of six dimensions of 20 items each. Van Dierendonck (2004) proposed a short 39-item version for the six scales. The scales’ length varied between six items (Self-Acceptance, Positive Relations with Others, Environmental Mastery, Purpose in Life), seven items (Personal Growth), and eight items (Autonomy). A six-point response scale was used for all scales, ranging from 1 (totally disagree) to 6 (totally agree).

We used the Van Dierendonck version adapted by Díaz et al. (2006) to the Spanish population. The instrument has 29 items, which participants rate on a 6-point response format with scores ranging between 1 (disagree strongly) and 6 (agree strongly). The six scales contain four to six items. The internal consistency of the scales in the present study was good, Self-Acceptance α = 0.84, Positive Relations with Others α = 0.82, Autonomy α = 0.78, Environmental Mastery α = 0.71, Personal Growth α = 0.72 and Purpose in Life α = 0.84.

Beck Depression Inventory-II (BDI-II)

The Beck Depression Inventory-II (BDI-II) was developed by Beck et al. (1996). It is a self-report questionnaire designed to measure the severity of depressive symptoms in adolescents and adults (Beck et al., 1996). The BDI-II consists of 21 items concerning symptoms of depression during the past two weeks, including today. The items are rated on a 4-point scale ranging from 0 to 3. The absence (or “as usual”) of depressive symptoms in each item is scored as “0,” and the presence of symptoms is scored between 1 and 3. Two items present seven options to indicate either an increase or decrease of appetite and sleep. Items are summed to create a total score ranging from 0 to 63, with higher scores indicating more intense symptom severity.

Sanz et al. (2003a), in the Spanish population, identified a general dimension of depression and two related factors, Cognitive-Affective and Somatic-Motivational, similar to the factor structure reported in other studies with samples from different countries. We used the Spanish adaptation of Sanz et al. (2003b), obtaining a total score of the BDI-II and the scores of the two factors. The internal consistency of the questionnaire in the present study was good: Total Depression α = 0.92, and also that of the two factors, Cognitive-Affective α = 0.89 and Somatic-Motivational α = 0.83.

Health Survey SF-36 Questionnaire (SF-36)

This instrument was developed from the Medical Outcome Study (MOS; Ware & Sherbourne, 1992) and measures concepts that represent excellent basic human values for health. It is applicable to the general population as well as to clinical groups (McHorney et al., 1992, 1994). The SF-36 is a generic measure of health status as opposed to one that targets a specific age, disease, or treatment group (Ware & Gandek, 1998).

The SF-36 comprises 36 items that report positive and negative states of physical health and emotional well-being. It measures 8 health dimensions: Physical Functioning, Role Physical, Bodily Pain, General Health, Vitality, Social Functioning, Role Emotional, and Mental Health. Subsequently, a new dimension, called Health Transition, has been included, which refers to the changes in the perception of the present state of health compared to how it was a year ago.

Each item is used to score only one dimension. Items are rated on a Likert-type scale that varies for each dimension. We used the direct score for each dimension. Higher scores indicate better health and/or better quality of life in different areas. Summary scores for a Physical Health Component (Physical Functioning, Role Physical, Bodily Pain, and General Health) and a Mental Health Component (Vitality, Social Functioning, Role Emotional, and Mental Health) can also be derived (Ware et al., 1993).

We applied the version that asks participants about all the health dimensions of the past 4 weeks, except for those of Physical Functioning and General Health. We used a Spanish version, which has shown good internal consistency, reliability, and validity in clinical samples (Alonso et al., 1995, 1998). The internal consistency of the questionnaire in the present study was good in the dimensions of Physical Functioning α = 0.87, Role Physical α = 0.94, Bodily Pain α = 0.82, General Health α = 0.80, Vitality α = 0.86, Social Functioning α = 0.84, Role Emotional α = 0.93, and Mental Health α = 0.86, and also in the Physical Health Component α = 0.91, and the Mental Health Component α = 0.94.

Statistical Analysis

For all data analyses, we used the IBM SPSS AMOS Statistics for Windows, version 27.0 (IBM Corp. & Released, 2020).

For evaluating the psychometric properties of PERMA-Profiler, we calculated:

  • Descriptive statistics to test the normality of the PERMA-Profiler items and their means, standard deviations, minimum values, maximum values, skewness, and kurtosis. Skewness and kurtosis values between ± 2 would prove normal univariate distribution (Field, 2009; Gravetter & Wallnau, 2014).

  • Reliability, internal consistency with the Cronbach’s alpha coefficient (α) and Guttman’s lambda 6 (λ6). Although Cronbach’s alpha coefficient is the most used to assess internal consistency, there is no consensus on its interpretation. Alpha reliability above 0.70 is the generally accepted standard (Nunnally & Bernstein, 1994; Hair et al., 2010; Tavakol & Dennick, 2011).

  • Relationships between the domains and between domains and the Overall Well-being (PERMA) score with Pearson product-moment correlations (two-tailed).

  • Validity based on internal structure with three confirmatory factor analysis. Firstly, we created a first-order model of five factors (domains) including three items each, five PERMA correlated factors (domains), every PERMA factor (domains) is defined through the three specific items. Secondly, we created a second-order model, where the five PERMA factors (domains) defined through the three specific items, load on a higher-order latent factor, representing Overall Well-being. Thirdly, we created a second-order model, where the five PERMA factors (domains) defined through the three specific items, and the item for general happiness, load on a higher-order latent factor, representing Overall Well-being (PERMA). We adjusted for non-normality through the robust maximum likelihood estimator (MLR) for standard errors (Yuan & Bentler, 2000; Brown, 2015; Kline, 2016). To assess the overall model fit of the three models, we evaluated different goodness-of-fit indices based on the cutoff values suggested by Hu and Bentler (1999) and Beauducel and Wittmann (2005). Chi-square (χ²) and the χ2 ratio (χ2/degrees of freedom) was used. For χ2/df, values smaller than 2 indicate model fit (Ullman, 2001). However, the χ2 statistic is highly sensitive to sample size (Schermelleh-Engel et al., 2003; Kline, 2016). The Bentler Comparative Fit Index (CFI) (Bentler, 1990). The Tucker-Lewis Index (TLI) (Tucker & Lewis, 1973). The Root Mean Square Error of Approximation (RMSEA) (Steiger, 1990; Browne & Cudeck, 1993) and its 90% confidence interval. The Standardized Root Mean Square Residual (SRMR) (Hu & Bentler, 1995). The Goodness of Fit Index (GFI) (Jöreskog & Sörbom, 1984). The Expected Cross Validation Index (ECVI) (Browne & Cudeck, 1992, 1993).

According to Hu and Bentler (1999), good model fit is inferred when values of CFI and TLI are higher than 0.90, and RMSEA is close to 0.06. However, some authors (Cangur & Ercan, 2015) consider that an RMSEA value between 0.05 and 0.08 indicates an adaptation close to good. SRMR values below 0.08 indicate a good fit, but some authors (Cangur & Ercan, 2015) suggest a value lower than 0.05 to indicate a good fit. GFI values greater than 0.90 indicate acceptable model fit. Finally, a smaller ECVI value indicates better model fit (Browne & Cudeck, 1992). The model having the smallest ECVI value exhibits the greatest potential for replication (Byrne, 2016).

  • Measurement invariance of the PERMA model separately across gender (males and females) and age groups (18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 74) using the best model that emerges from the three confirmatory factor analyzes. Our calculations are based on the well-established procedure for multi-group CFA’s with increasingly restricted nested models (Kline, 2016). We have tested configural invariance (equality of factor structure), metric or weak invariance (equality of factor loadings), scalar or strong invariance (equality of item intercepts), and strict invariance (equality of indicator residuals, equality in error variances and covariances across the groups), to make sure that factor structure and loadings are equivalent across multiple groups. Because of the sensitivity of the Chi-Square difference test in large sample sizes (Meade et al., 2008), for model comparisons we used the changes in the CFI (ΔCFI) and RMSEA (ΔRMSEA). The following criteria were used to detect a practical lack of invariance: ΔCFI ≥ − 0.01 (Cheung & Rensvold, 2002; Chen, 2007) and ΔRMSEA ≥ 0.015 (Chen, 2007). The pairwise comparisons were made with its less restrictive predecessor.

  • Convergent and discriminant validity based on relationships with other constructs, with Pearson product-moment correlations (two-tailed).

  • Exploratory factor analysis (EFA) was carried out on the scores of the domains of the PERMA-Profiler, the scores of the Satisfaction with Life Scale (SWLS), the Positive and Negative Affect Scales (PANAS), and the six scores of the Psychological Well-being scale (PWB). We performed EFA using the correlation matrix of the Pearson product-moment of the variables involved. Prior to the extraction of the factors, we obtained the values of the Kaiser-Meyer-Olkin Test (KMO; Kaiser, 1970), the measure of sampling adequacy, and Bartlett’s Test of Sphericity (Bartlett, 1950). KMO values lower than 0.50 are considered inadequate for EFA, mediocre if they range between.60 and 0.69, whereas values of 0.80 and above are considered satisfactory (Kaiser, 1970). We applied Maximum Likelihood (ML) factor extraction methods (Lawley & Maxwell, 1971; Tucker & Lewis, 1973; Fabrigar et al., 1999; Flora et al., 2012; Beavers et al., 2013; Lloret-Segura et al., 2014), followed by Oblique (direct oblimin) rotations (Mulaik, 1972; McDonald, 1985; Fabrigar et al., 1999; Tabachnick & Fidell, 2013; Lloret-Segura et al., 2014). Following the recommendations of Tabachnick and Fidell (2013), only variables with loadings of 0.32 and above are interpreted.

Results

Descriptive Statistics and Reliability

Descriptive statistics of the items for the Spanish version of the PERMA-Profiler are presented in Table 3, which shows the number of items, mean, median, standard deviation, minimum, maximum, skewness and kurtosis. Skewness values ranged from − 1.58 to 0.38, and kurtosis values were between − 1.10 and 2.99. The index for acceptable limits of skewness and kurtosis of ± 2 (Field, 2009; Gravetter & Wallnau, 2014) showed that all the items were within the acceptable values in skewness, but in kurtosis, outliers appeared in Item 21, with a value of 2.99, belonging to the Engagement domain, and Item 16, with a value of 2.32, belonging to the Accomplishment domain.

Table 3 Descriptive statistics of the items for the Spanish version of the PERMA-Profiler
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Descriptive statistics and reliability coefficients of the domains for the Spanish version of the PERMA-Profiler are presented in Table 4, which shows the mean, median, standard deviation, minimum value, maximum value, skewness, kurtosis and the two reliability indices, Cronbach’s alpha coefficient (α) and Guttman’s lambda 6 (λ6). The index for acceptable limits of skewness and kurtosis of ± 2 (Field, 2009; Gravetter & Wallnau, 2014) showed that all domains for the Spanish version of the PERMA-Profiler obtained acceptable values in skewness (-1.14 to 0.38) and kurtosis (-1.10 to 1.84), there were no extreme outliers in our sample. The results of internal consistency, Cronbach’s alpha coefficient (α) and Guttman’s lambda 6 (λ6), indicated very acceptable reliability in all domains and Overall Well-being (PERMA), except for the Engagement domain (α = 0.56, λ6 = 0.47). Within the PERMA domains, the highest reliability was found in the domains of Meaning (α = 0.90, λ6 = 0.86) and Positive Emotion (α = 0.90, λ6 = 0.85), with similar reliability and next, in the domains of Relationships (α = 0.82, λ6 = 0.76) and Accomplishment ((α = 0.82, λ6 = 0.76), with equal reliability. The Overall Well-being (PERMA) showed high reliability (α = 0.94, λ6 = 0.95). The domains of Negative Emotion (α = 0.76, λ6 = 0.69) and Physical Health (α = 0.91, λ6 = 0.87) also presented acceptable reliability, they are not among the five domains that make up the PERMA, but they do belong to the PERMA-Profiler.

Table 4 Descriptive statistics and reliability coefficients of the domains for the Spanish version of the PERMA-Profiler
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Relationships Between the Domains

The relationships between the domains and between the domains and the Overall Well-being (PERMA) score can be seen in Table 5. All correlations were significant (p < 0.001). The relationships established between the five domains that make up the PERMA—Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment—were all positive correlations, and so were those of these five domains with the Overall Well-being (PERMA) score.

Table 5 PERMA-Profiler inter-domains correlations
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Regarding the relationships between the five PERMA domains and the other PERMA-Profiler domains, the results show positive correlations of the five PERMA domains with Physical Health and Happiness and a negative and significant correlation with Negative Emotion and Loneliness.

Confirmatory Factor Analysis

Confirmatory factor analyses were performed to test the instrument’s structural adequacy. Three alternative models were tested. Firstly, we tested a first-order model of five PERMA factors (domains), as in the original version by Butler and Kern (2016) based on Seligman’s (2011) model. In this model, each PERMA factor (domain) is defined through the three specific items, and the five PERMA factors (domains) are inter-correlated but separate constructs. Secondly, we tested a second-order model, where the five PERMA factors (domains), explaining their respective items, load on a higher-order latent factor, representing Overall Well-being. Thirdly, we tested a second-order model, where the five PERMA factors (domains), explaining their respective items, and the item for general Happiness, load on a higher-order latent factor, representing Overall Well-being (PERMA). The results of the different goodness-of-fit indices of the three models analyzed can be seen in Table 6.

Table 6 Fit indices in confirmatory factor analysis for a first-order and a second-order PERMA models
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In the first model, the results show a significant chi-square ratio of 18.126, which is unacceptable. However, this value is justified by the large sample size, so we focused the evaluation of the global model fit on the six goodness-of-fit indices The rest of the fit indices that were tested indicated a good fit of the data to the model, CFI = 0.948, TLI = 0.932, RMSEA = 0.082, SRMR = 0.036, GFI = 0.927. In the second model, the results showed a significant chi-square ratio of 22.909. The following three fit indices were acceptable, CFI = 0.930, TLI = 0.913, and SRMR = 0.043, but the RMSEA = 0.093 and GFI = 0.896 fit indices were unacceptable. In the third model, the results showed a significant chi-square ratio of 27.576. The former fit indices were acceptable, CFI = 0.914 and SRMR = 0.047, but the TLI = 0.896, RMSEA = 0.103, and GFI = 0.870 fit indices were unacceptable. The smallest ECVI value of the first model compared to the other two models indicated a better model and the one with the greatest potential for replication.

Comparing the three models, it is evident that the first model, a first-order model of five PERMA factors (domains), is better than the other two models. The confirmatory factor analyses indicate high construct validity of the Spanish version of the questionnaire using a model of five PERMA domains. The graphic representation can be seen in Fig. 1 Standardized solution of the Spanish PERMA-Profiler’s five factor model.

Fig. 1
figure 1

Standardized solution of the Spanish PERMA-Profiler’s five factor model

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Measurement Invariance

We tested the factorial invariance of the PERMA model separately across gender and age groups, using the first-order model of five factors (domains) with three items in each domain, as the best model that emerges from the three confirmatory factor analyses. The simplest model was configural invariance (factorial loads pattern), which was evaluated by adding constraints, metric (factorial loads magnitude), scalar (intercepts or covariances), and strict (residual variances) invariance models.

To accept configural, metric, scalar, and strict invariance, we used a double criterion between two immediate models, the changes in CFI (ΔCFI) and RMSEA (ΔRMSEA). The results showed that the CFI and the RMSEA differences between configural, metric, scalar, and strict invariance across gender and age groups was lower than the applied cutoff values, ΔCFI ≥ − 0.01 and ΔRMSEA ≥ 0.015 for all model comparisons (Table 7). Thus, the results suggest that all item loadings, intercepts, and residual variances were equivalent across gender and age groups; that is, the PERMA model shows good factorial invariance. The means and standard deviations of the domains of the Spanish version of the PERMA-Profiler in men and women and the age groups can be seen in Table 8.

Table 7 Factorial invariance across gender and age groups for the PERMA model
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Table 8 Means and standard deviations of the domains of the Spanish version of the PERMA-Profiler, gender and age
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Convergent and Discriminant Validity

To evaluate convergent and discriminant validity of the PERMA-Profiler domains, we examined their correlations with other measures. We tested the correlations between PERMA-Profiler domains and the positive psychology constructs as measured on the following scales: Satisfaction with Life Scale (SWLS), Life Orientation Test-Revised (LOT-R), Positive Affect Scale (PANAS), and Psychological Well-Being Scale (PWB). We also examined correlations with negative constructs on the scales Negative Affect Scale (PANAS), Beck Depression Inventory-II (BDI-II), and general physical and mental health (SF-36).

Concerning the positive psychology constructs, the results showed that all five PERMA domains, the Overall Well-being (PERMA) score, and the domains of Physical Health and Happiness had positive and signficant relationships with satisfaction with life or the global cognitive aspects of subjective well-being (SWLS), dispositional optimism (LOT-R), positive affect (PANAS), and all six scales of psychological well-being (PWB), and a negative relationship with negative affect (PANAS). The Negative Emotion and Loneliness domains had an inverse relationship; that is, they had negative and significant relationships with satisfaction with life or the global cognitive aspects of subjective well-being (SWLS), dispositional optimism (LOT-R), positive affect (PANAS), and all six psychological well-being scales (PWB) and a positive relationship with negative affect (PANAS).

Regarding the construct of depression, the results showed that all five PERMA domains, the Overall Well-being (PERMA) score, and the Physical Health and Happiness domains had negative and significant relationships with the severity of depressive symptoms or total depression (BDI-II) and its two related factors, Cognitive-Affective and Somatic-Motivational. The Negative Emotion and Loneliness domains had a positive and significant relationship with the total depression score and its two factors.

Finally, the dimensions of the Health Survey (SF-36), the Physical Health Component and Mental Health Component, had positive and significant relationships with all five PERMA domains, the Overall Well-being (PERMA) score, and the Physical Health and Happiness domains. Its relationships with the Negative Emotion and Loneliness domains were negative and significant. Cronbach’s alphas, means, and standard deviations of the variables examined to analyze the validity of the PERMA-Profiler are shown in Table 9. The values of the correlations between the PERMA-Profiler domains and the other measures can be seen in Table 10.

Table 9 Cronbach´s alphas, means, standard deviations of the variables examined to analyze the validity of the PERMA-Profiler
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Table 10 Correlations of PERMA-Profiler domains with other constructs
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The results obtained from the comparison with other constructs showed high convergent and discriminant validity and a positive relationship with the physical and mental health status, indicating that the PERMA-Profiler domains are characterized by high levels of construct validity.

Exploratory Factor Analysis (EFA)

The results of the factor analysis show that the data set is adequate for EFA, values for the Kaiser-Meyer-Olkin Test (KMO = 0.951) and the Bartlett’s Test of Sphericity (χ2 = 35582.085, df = 153, p < 0.001), indicating that the sample in this study is suitable for conducting factor analysis.

The EFA employing Maximum Likelihood (ML) factor extraction methods with Oblique (direct oblimin) rotations identified three distinct factors. These factors explained 62.137% of the total variance.

The factor structure of the group of variables can be seen in Table 11. The factor structure matrix provides the correlations between all observed variables and all extracted factors. Following Courville and Thompson (2001) and Henson and Roberts (2006), who indicated that in Oblique (direct oblimin) rotations, both matrices should be taken into account, and the interpretation of results should first consider the structure coefficients, we observed that in the structure matrix in Factor 1, which could be called Well-Being, all the analyzed elements loaded adequately.

Table 11 Factor loadings for the exploratory factor analysis
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Considering the loadings of each element, we see that Factor 1 comprises the following domains of the PERMA-Profiler: Meaning (M), Accomplishment (A), Engagement (E), Positive Emotion (P), Happiness (Hap), and Physical Health (H); Satisfaction with Life Scale (SWLS), Positive Affect (PANAS) and the following Psychological Well-being Scales (PWB): Purpose in Life, Self-Acceptance, Environmental Mastery, Personal Growth, and Autonomy.

Factor 2, which could be called Relationships, is made up of—with a negative sign—the domain Relationships, (R) from the PERMA-Profiler, Positive Relations with Others from the Psychological Well-being Scales (PWB) and—with a positive sign—the domain Loneliness (Lon) from the PERMA-Profiler.

Factor 3 is made up of the domain Negative Emotion (N) from the PERMA-Profiler and Negative Affect (PANAS).

The result provides evidence of a general well-being factor, suggesting that psychological well-being and subjective well-being are strongly related at the general construct level, but their individual components are distinct. Besides assessing specific elements of both kinds of well-being, the PERMA-Profiler also assesses, with a single measurement, other indicators related to well-being, such as Physical Health, Negative Emotion, and Loneliness, a list where each element can be defined and measured independently from the others.

Discussion

The purpose of this work was to examine the psychometric properties of the PERMA-Profiler in a Spanish context and adult population. We translated and culturally adapted the test, and subsequently analyzed its reliability, validity based on internal structure with three confirmatory factor analyses, invariance across gender and age, and convergent and discriminant validity based on relationships with other constructs. Overall, the findings of this study indicate that the PERMA-Profiler is transferable to the Spanish context, and the Spanish version is a reliable and valid measure of well-being.

The results of the analyses to confirm the internal consistency are very acceptable in all domains and the Overall Well-being (PERMA) score, except for the Engagement domain, which presents the lowest values. These results are similar to those obtained by the authors (Butler & Kern, 2016) of the questionnaire, who found good reliability in all domains except for Engagement, which was the weakest in the samples studied. The results obtained in the adaptations and analysis of the properties of the questionnaire in other languages and cultures, with very good reliability of the domains and low reliability of the Engagement domain (Iasiello et al., 2017; Ayşe, 2018; Wammerl et al., 2019; Cobo-Rendón et al., 2020; Giangrasso, 2021; Pezirkianidis et al., 2021; de Carvalho et al., 2023; Chaves et al., 2023). Pezirkianidis et al. (2021), suggest that the weak reliability of the Engagement domain may indicate that its three elements refer to commitment and are not homogeneous but different; also indicate that the psychometric problems could also be due to the nature of the construct itself because it is used in various contexts, such as work, school, and society, the items included in the domain do not measure commitment in any specific context but in general in people’s lives. Wammerl et al. (2019) indicate that this result may be due to the heterogeneity of the domain’s content combined with its short structure.

The relationships established between the five domains that make up PERMA are all positive and significant, as are their relationships with the Overall Well-being (PERMA) score, showing the interdependent nature of the domains and that a multidimensional model is the best option to study these different domains (de Carvalho et al., 2023).

The three confirmatory factor analyses showed that the model of five independent interrelated factors (domains) obtained the best fit compared to the other two models. This result also confirms the high construct validity of the Spanish version of the questionnaire, using a model consisting of the five PERMA domains, supporting the multidimensional structure of PERMA. In the studies by Butler and Kern (2016), the data supported the five-factor structure of the PERMA model, and the confirmatory factor analysis revealed that the five-factor interrelated model fit the data adequately. Similar results have been obtained in several studies of adaptations and analyses of the properties of the questionnaire in other languages and cultures when several models have been compared in Indonesia (Hidayat et al., 2018), Greece (Pezirkianidis et al., 2021), China (Yang & Mohd, 2021), Brazilian (de Carvalho et al., 2023) and Mexico (Chaves et al., 2023), in which the model of five interrelated factors showed the best fit. The only study that compared several models and found no clear difference of a better fit between the five-factor model and other models was the study of Wammerl et al. (2019) with German-speaking population. These authors found that the bi-factor model (which defends both the existence of a general well-being factor, with the responses to the items directly influencing this factor, and the existence of the five PERMA factors) had a better model fit than the five-factor model, although both models showed good global model fit.

Although the PERMA-Profiler also provides a total well-being score, the Overall Well-being (PERMA) score, Butler and Kern (2016) suggest that the multidimensional structure of the measure should be maintained instead of condensing the answers into a single score. These authors indicate that “A single score assumes that the underlying measure is unidimensional, but the PERMA-Profiler was specifically designed to be multidimensional in nature” (p. 22).

The analyses of the factorial invariance of the PERMA model separately across gender and age groups, performed with the first-order model of five factors (domains), show gender and age invariance; that is, the scores of men and women can be compared and interpreted meaningfully. This result is in line with those obtained in studies that examined the factorial invariance across gender (Wammerl et al., 2019; Pezirkianidis et al., 2021; Chaves et al., 2023) and age (Pezirkianidis et al., 2021; Chaves et al., 2023).

As expected, the findings of the present study regarding the convergent and discriminant validity of the PERMA-Profiler show that both the five PERMA domains and the Overall Well-being (PERMA) score are related to the measures of satisfaction with life or subjective well-being (SWLS), psychological well-being (PWB), positive and negative affect (PANAS), dispositional optimism (LOT-R), depression (BDI-II), and general physical and mental health status (SF-36).

Regarding convergent validity between well-being variables, the five PERMA domains and the Overall Well-being (PERMA) score are positively and signficantly related to satisfaction with life or subjetive well-being (SWLS), the six dimensions of psychological well-being (PWB), positive affect (PANAS), and dispositional optimism (LOT-R). These results are similar to those obtained by other authors; for example, Giangrasso (2021) in Italian population, found positive and significant relationships with the six dimensions of psychological well-being (PWB); Pezirkianidis et al. (2021) in Greek population, found the same relationships with satisfaction with life (SWLS), and subjectivity of an individual’s global happiness (Subjective Happiness Scale, SHS; Lyubomirsky & Lepper, 1999). Positive and significant relationships were also found by Wammerl et al. (2019) in German-speaking population, with psychological well-being (short-form of the PWB; Ryff & Keyes, 1995), by de Carvalho et al. (2023) in Brazilian population, with satisfaction with life (SWLS), psychological well-being (PWB), positive affect (PANAS) and dispositional optimism (LOT-R), and by Chaves et al. (2023) in Mexican population, with satisfaction with life (SWLS) and positive affect (PANAS).

In addition to examining the relationships with the constructs of positive psychology, we analyzed the relationship with the general physical and mental health status through the Health Survey SF-36 Questionnaire (SF-36), finding a positive and significant relationship between the five PERMA domains and the Overall Well-being (PERMA) score and all the health dimensions evaluated with this questionnaire. This positive relationship was also found by Chaves et al. (2023) in Mexican population, although with a shorter version of this health measure, the Short-Form Health Survey (SF-12, Ware et al., 1996). In our work, for the first time, a general and broad measure of health (SF-36) is related to PERMA-Profiler, indicating a positive relationship between the levels of the well-being domains and the total well-being score with the levels of physical and mental health.

Considering the discriminant validity, the results show that the five PERMA domains and the Overall Well-being (PERMA) score are negatively and significantly related to negative affect (PANAS). This relationship was also obtained by de Carvalho et al. (2023) in Brazilian population and by Chaves et al. (2023) in Mexican population. Besides the relationship with negative affect, we also found that the five PERMA domains and the Overall Well-being (PERMA) score are negatively and significantly related to the total score of depression evaluated with the Beck Depression Inventory-II (BDI-II) and also with the two factors of this inventory, Cognitive-Affective and Somatic-Motivational. This relationship was also obtained by Wammerl et al. (2019) in German-speaking population and by Pezirkianidis et al. (2021) in Greek population, using the Depression Anxiety Stress Scales (DASS; Lovibond & Lovibond, 1995).

The results of the analyses of convergent and discriminant validity confirm that the PERMA-Profiler domains have high levels of construct validity.

The result of the EFA performed to obtain information about the nature of the underlying constructs used to assess well-being and to confirm which specific variables effectively capture each factor revealed three factors: the first one is the main factor of well-being—psychological well-being and subjective well-being; the second factor comprises relations with others; and the third factor is made up of negative emotions. These factors represent the domains of the PERMA-Profiler, mainly the first one, together with the components of the other measures of well-being. Seligman (2018) indicated that PERMA constitutes the elements of well-being, not that it presents a new kind of well-being, as confirmed by the EFA. Moreover, through the following factors, the PERMA-Profiler also assesses other elements related to well-being—Physical Health, Negative Emotion, and Loneliness—which complement the measure of global well-being.

Thus, the domains of the PERMA-Profiler assess psychological well-being and subjective well-being, and other elements related to well-being, each independently. This is particularly useful for the development of specific interventions.

Limitations

Several limitations of this study are worth mentioning. We applied self-report measures, so social desirability may have influenced the responses to the tests. However, as suggested by Diener (1994), social desirability is considered a substantive individual difference and not necessarily an artifact and the scores in social desirability may correlate with conformity and avoidance of thinking about unpleasant affect, both of which correlate with well-being. Individual differences in social desirability are related to personality content concerning subjective well-being, so a high score in social desirability does not threaten the validity of the subjective well-being scores.

A point of concern about the results of the present study concerns the fact that the Engagement domain had low reliability, so the construct may need a revision of the items. Another limitation is that, despite the very large sample size, there was gender imbalance.

Conclusion

We can conclude that the development and evaluation of the psychometric characteristics of PERMA-Profiler in the Spanish context show that its structure and qualities are preserved even in populations other than those used for its creation. The multidimensional structure of the PERMA theory in the Spanish population is confirmed.

The fact that the questionnaire has a structure that can measure five different dimensions related to well-being— subjective and psychological—in a short time facilitates its use. Moreover, it can be applied in different contexts, such as education, organization, health and scientific research. For example, applying the PERMA model to health behaviors can increase understanding their contributions to well-being and flourishing.

According to the authors of PERMA-Profiler, an ideal profile of well-being cannot be recommended because the measure is descriptive in nature, not prescriptive. “Different profiles may be more or less adaptive for different people at different times, depending on their personality, history, and social context” (Butler & Kern, 2016, p. 22).

Currently, there are many theories about well-being that recognize its different pillars. It would be appropriate for future research to agree on the main components of well-being and to study the relationships between different personal profiles of well-being and mental and physical health, as well as the moderating role of other variables.