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Journal of Well-Being Assessment

, Volume 2, Issue 1, pp 75–89 | Cite as

Validation of the Flourishing Scale for Married Employees in the Information Technology-Enabled Services Sector in India

  • Rajesh Premchandran
  • Pushpendra Priyadarshi
Original Research
  • 131 Downloads

Abstract

The Flourishing Scale (FS) created by Diener et al., Social Indicators Research, 97, 143–156 (2010) is a measurement that assesses eudaimonic well-being in terms of psychological functioning. In this study, the psychometric properties of the scale were explored by using three Indian samples (I: n = 262; II: n = 347; III: n = 508) each comprising of married individuals belonging to the Information Technology/Information Technology Enabled Services (IT/ITES) sector. Reliability analysis and a multigroup confirmatory factorial analysis (MGCFA) were carried out on the FS, and the validity was examined by analyzing their correlations with other measures of well-being. Results showed adequate psychometric properties for the scale, and convergent validity with subjective well-being measures. Results also demonstrated the unidimensional structure of the FS corroborating earlier findings. The MGCFA of the scale evidenced an invariant structure. In conclusion, the FS behaved consistently with the original study by Diener et al. and was found to be appropriate for use in assessing eudaimonic well-being among service sector employees in India. Further, due to its short length, the survey may be leveraged to evaluate the effectiveness of interventions of well-being programs by HR practitioners and may also be used in future studies on well-being.

Keywords

Psychological well-being Flourishing Eudaimonia Satisfaction India Validation 

1 Introduction

1.1 Well-Being

Positive psychology seeks to augment our understanding of well-being through extending the scope beyond studying mental disorders and exploring abilities to increase happiness levels (Seligman, Steen, Park & Peterson, 2005). It has been argued that the exploration of well-being should not only be limited to the absence of psychological problems, but also focus on positive functioning of the individual, accompanied by a corresponding analysis of thoughts, actions, and attitudes (Keyes 2002). This core tenet of positive psychology provides a foundation for both therapy and measurement. Simultaneous to the positive psychology movement, several countries over the last few years have devised national surveys designed to empirically measure well-being as a multi-dimensional construct (Hone et al. 2014a). Consequently, there is a need for systematic assessment using reliable and valid measurement instruments tested across different demographic samples (Diener et al. 2010). Research on positive psychology has also outlined details of happiness and well-being (Seligman and Csikszentmihalyi 2000), distinguishing between their two conceptualizations: hedonia and eudaimonia (Waterman et al. 2008). Eudaimonia is a conceptualization of well-being based on the ability of an individual to commit to develop the best in oneself, striving for excellence and acting in a virtuous manner (Huta 2013; Ryan and Deci 2001; Ryff 1989; Waterman 2007). Hedonia, on the other hand, refers to the happiness experienced from pursuing personal pleasure, gratification, and comfort, through both physical and emotional-cognitive methods (Huta 2013). Although these two traditions have philosophical origins (Waterman 1993), research has now provided theoretical grounding and empirical support (Huta and Waterman 2014; Waterman et al. 2010; Huta and Ryan 2010) for the two constructs. Hedonia has been measured primarily as subjective well-being (SWB, Diener and Lucas 1999) that includes a satisfaction component, measured through the Satisfaction with Life Scale (SWLS, Diener et al. 1985) and the affective component, analyzed through the Positive and Negative Affectivity Schedule (PANAS, Watson et al. 1988).

However, there is little consensus on the operationalization of eudaimonia as noted by Huta and Waterman (2014), arguing that the differences lie in the variety of lenses used to study the concept: subjective experiences, orientations in values, behaviors and positive functioning. Disabato et al. state that “To date, there is no single agreed-upon theory or methodological approach to studying eudaimonia” (p.1) (Disabato et al. 2016).

Research on contemporary perspectives of well-being shows that though eudaimonic models vary, they focus fundamentally on two aspects: some form of personal meaning and growth, and the clear omission of affect (Ryan and Deci 2001). A widely used theory of eudaimonia is psychological well-being, which is commensurate with positive functioning (Ryff and Singer 1998). Based on this, Diener and colleagues created the Flourishing Scale (2010) as a summary measure of psychological functioning, with a view to complement other measures of SWB. The Flourishing Scale encompasses core aspects of human functioning and needs like competence, relatedness, and self-acceptance. These constructs have their roots in Aristotle’s concept of eudaimonia, which is the Greek expression for happiness or welfare. Hence, we believe that this scale offers a novel and accurate operationalization of eudaimonia, distinct from existing instruments, meriting further testing.

1.2 Flourishing Scale

The Flourishing Scale (FS) is sought to address the gaps in existing well-being measures by amalgamating constructs from several psychological well-being theories. The central tenet of these theories focuses on the concept of eudaimonia. The origins of eudaimonia can be traced back to Aristotle’s (fourth-century BC) era, where the definition of well-being along with pleasure-seeking happiness meant being true to oneself and working toward personal growth. A century later, Aristippus of Cyrene (third century BCE), the Greek philosopher, conceptualized eudaimonia as human flourishing and self-actualization. Building upon this, flourishing as defined by Corey Keyes, implied the pursuit of a meaningful and virtuous life with healthy relationships (Keyes 2002). Based upon earlier humanistic psychology theories, the FS assesses several identified universal human psychological needs. It covers multiple dimensions of well-being such as competence, self-acceptance, meaning, and relatedness, borrowed from Ryff (1989) and Ryan and Deci (2001). Ryff and Singer (2008) divided human fulfilment, or flourishing, into six categories: Autonomy, personal growth, self-acceptance, purpose in life, environmental mastery, and positive relations with others. The Flourishing Scale by Diener et al. (2010) covers the constructs posited above and includes essential parts of human functioning, especially highlighting the social aspect of human mental prosperity. This is in accordance with the human relations school, which suggests that social relationships or social capital improve individual performance, as they contribute to employee well-being (Agneessens and Wittek 2008; Perrow, 1986). This concept also makes this scale unique compared to other flourishing scales (Hone et al. 2014a, b) and more relevant to collectivist cultures such as India, where individuals place greater emphasis on conforming and cooperating with others in their in-groups as opposed to fulfilling their own goals and ambitions (Mortenson 2002). Another aspect subsumed within the FS is flow, a construct discussed by Csikszentmihalyi (1990), suggesting engagement with interest as a key component of human psychological capital, an argument further supported by Seligman (2002). Finally, FS also encompasses optimism, based on previous research (Peterson et al. 1988; Scheier et al. 2001) demonstrating that optimism is important to successful functioning and well-being. These concepts of human flourishing are closely related to the ancient and philosophical concept of eudaimonia, described above. Consequently, in this article, we use the terms eudaimonic well-being and flourishing interchangeably. The FS was first introduced as the Psychological Flourishing Scale in a 12-item format and subsequently shortened to 8 items (Diener et al. 2010). The short length of the instrument is advantageous to psychometric testing especially since it is challenging to secure time from respondents for longer surveys.

Several studies have confirmed the validity, reliability, and the invariant one-factor structure of the 8-item FS across different nations such as the USA (Diener et al. 2010), Germany (Esch et al. 2013), Iran (Khodarahimi 2013), Portugal (Silva and Caetano 2013), Japan (Sumi 2014), and New Zealand (Hone et al. 2014a, b). However, research to investigate the psychometric properties of this scale is still scant in India. To date, there has been only one study to the authors’ knowledge that has tested this scale. That study by Singh et al. (2016) found adequate internal consistency, with Cronbach’s α ranging between .80 and .95 for the three samples tested. The absolute and relative fit indices were also satisfactory with the CFA (Confirmatory Factor Analysis) showing a single factor structure and adequate convergent validity. However, because of the limited sample which focused on non-working adolescents and a generic population set, the study cannot be extended to a professional environment.

1.3 The Indian Context

India saw considerable economic growth, in the late 1990s and early 2000s, post-liberalization, uplifting several millions from the lower middleclass into a life where basic human needs were no longer the only motivators to work. This newfound sentiment of surplus motivates the current research into positive functioning, as employees begin to value jobs that give them a sense of purpose and meaning.

The economic growth has been accompanied by more women entering the workforce and consequently the rise of dual-earner couples which makes managing work and family challenging for urban households (Alagaraja et al. 2016). For a large majority of Indians, especially Hindus, family centricity is the norm (Sinha and Sinha 1990). Ancient Indian texts, such as the Atharva Vedas, suggest that the institution of family, parenting and bringing up children are services to society that ensure continuity of humanity. The values and nourishment that is provided to one’s children is seen as the most exalted form of sacrifice. The concept of Gruhastha (Radhakrishnan 1992), according to Hindu scriptures, literally means “being in and occupied with home, family” or “householder”, and is one of the four stages of a person’s life. Each of these four stages is called an Ashrama. The first is the Brahmacharya, or a bachelor student, marked by devotion and obedience to one’s teacher. This is followed by Gruhastha. After Gruhastha comes Vanaprastha (forest dweller, retired) where one must withdraw from worldly concern and material pursuits. Finally, the last Ashrama, is the Sannyasa (renunciation) where the individual is expected to renounce one’s home and possessions and wander in search of alms and beg for food.

Among these Ashramas, the second Ashrama of the married householder (Gruhastha) is central because it sustains the other three. Hindu scriptures also indicate that happiness is a function of how well a person performs in each of the Ashramas, fulfilling the duties and obligations that are specific to each of them. In the Gruhastha, the person must perform his obligatory duties (dharma) and earn wealth (artha) to ensure the welfare of his family, society and the environment. Hence the question of what brings happiness has also found mention in the ancient holy text, Taittarya Upanishads (Gambhirananda 1986) and highlights why wellbeing has been a topic of philosophical enquiry for millennia. Hence, examining the Gruhastha Ashram dweller (married employee with children), while conducting a psychological enquiry into human flourishing, would add considerable value to the context of eudaimonia or flourishing, and hence extend generalizability of the scale.

The Indian IT/ITES sector is a significant contributor to the Indian economy and has a huge impact on jobs and growth of middle-class families. IT refers to Information Technology while ITES, or Information Technology Enabled Services, refers to outsourced services reliant on technology in industries, such as banking and finance, telecommunications, insurance, and healthcare. The sector employs about 3.7 million people, is worth USD 160 billion, and contributes nearly 10% to India’s Gross Domestic Product (GDP, IBEF Report 2017). However, this sector also witnesses greater time pressures, high stress (Dhar and Dhar 2010; Vaid 2009), attrition (Bhatnagar 2007), lack of work-life balance (Singh 2010), exhaustion (Ahuja et al. 2007; Budhwar et al. 2006), organizational deviance (Krishnan and Singh 2010), and higher levels of gender disparity (Upadhya and Vasavi 2006). Therefore, this demographic cohort is ideal to explore flourishing or eudaimonic well-being given the fact that the Flourishing Scale looks at competence, self-acceptance, meaning, relatedness, optimism, altruism, and engagement.

In summary, the aim of the present study is to validate the Flourishing Scale, recently developed by Diener et al. (2010) which expands existing research on eudaimonia, a relatively newer branch of well-being research. This study will help extend the generalizability of the Flourishing Scale by validating this new instrument in the Indian context. Further, we choose to validate this scale through examining married employees from the IT/ITES industry in India.

2 Method

2.1 Data Analysis Strategy

The study comprises of three samples, each with a set of married IT/ITES professionals in India. The first step was to assess the unidimensional nature of the FS construct by testing the factorial validity of the scale, using confirmatory factor analysis (CFA). Results from this is expected to show a single-factor solution of the FS, in line with previous studies (Diener et al. 2010; Hone et al. 2014a, b; Silva and Caetano 2013; Sumi 2014; Tang et al. 2016). Internal consistencies were computed next, along with generating descriptive analysis for each of the constructs measured. A multi-group confirmatory factor analysis (MGCFA) was carried out to look at measurement invariance across the samples (Jöreskog 1971). Next, we tested for convergent validity. Convergent validity would be supported if the eight items strongly correlate with its proposed construct and the construct itself is strongly related to other measures of wellbeing (Cunningham et al. 2001). We used measures widely used in wellbeing literature such as subjective wellbeing, family satisfaction, affect and job satisfaction. The choice of these scales was driven by the need to support and add to existing studies that have used other measures of well-being while establishing convergent validity with happiness constructs. Most studies validating the Flourishing scale have relied on the Satisfaction with Life Scale and some other measure of subjective well-being (see Silva and Caetano 2013; Howell and Buro, 2015), in addition to what Diener et al. (2010) used in the original study. Last, we tested for discriminant validity of the scale by examining whether the scale correlates weakly with all other constructs except for the one to which it is conceptually correlated (Gefen and Straub 2005). We expect the Average Variance Extracted for the construct to be greater than the square of the correlations with other constructs measured. Data were analyzed using SPSS 21.0 and Amos 22.0 (IBM Corporation, New York).

2.2 Participants

The data for the scale were obtained through three samples, collected at different time periods and locations. Participants in each of the surveys were married IT/ITES professionals. The population in Sample 3 comprised respondents having at least 1 child each and was purposive to include 35% women which is representative of the employees in the IT/ITES sector between the age group of 30 and 40 years. All respondents were informed about the aim of the study and ensured anonymity. The surveys were all conducted in English as it is the official language of communication in corporate India, and the respondents were proficient in communicating in English. All participants completed a demographic questionnaire and the FS, along with other constructs measured: job satisfaction, family satisfaction, and affect.

2.3 Sample 1

The respondents were full time employees enrolled in a part-time management program at a premier university in Northern India. They were approached directly and were given paper copies of the survey to fill out and return to the author the following day. Only married students working in the IT/ITES sector were given the survey. Out of the 310 surveys that were administered, 262 complete responses were received, and the rest were rejected as they were incomplete. There were 223 (85%) males and 39 (15%) females which is a lower proportion of females than desired, if this were to be a representative sample. In total, 118(56%) were graduates and 114 (44%) had postgraduate degrees. The average age was 31 years (Standard Deviation, SD = 6.5) and the average tenure with the firm they worked at the time of the survey, was 5.5 years (SD = 3.0).

2.4 Sample 2

For the second wave, the respondents were managers who participated in training programs at a premier management institute in Northern India. They were approached directly after the training and were given paper copies of the survey to fill out and return to the trainer the following day. Only married trainees working in the IT/ITES sector were given the survey. Out of 402 surveys, 55 were rejected due to incomplete responses and therefore we had 347 complete responses. There were 267 (77%) males and 80 (23%) females, which is a slightly lower proportion of females than desired but higher than Sample 1. In total, 187 (54%) were graduates and 160 (46%) had postgraduate degrees. The average age of the sample was 39.4 years (SD = 7.7).

2.5 Sample 3

For the final sample, data were collected through direct interviews with married employees working in two tier 1 cities of India. Tier 1 cities are the largest cities classified based on development, population and infrastructure by the Government of India.These respondents were approached in and around major IT hubs and offices located in the cities of Hyderabad and Bangalore. These cities were chosen primarily because of the population of IT/ITES firms and number of people working here, comprising 50% of the total individuals employed in this sector in India, making this a representative sample. In total, out of the 508 respondents, 248 (49%) of the respondents were from Bangalore and 260 from Hyderabad. We did not collect the education levels of these respondents as the norm for employment in IT/ITES is a graduate degree, and education levels beyond were not found to influence results in the previous survey. The average age was 35 years (SD = 3.2) with 325 males (64%) and 183 female participants (36%). The male to female ratio is approximately representative of women in the IT/ITES sector at the mid-manager level. The age of the respondents was restricted to a range of 30–45 years, to include married individuals with younger kids, which is typically the time when individuals face greater responsibilities at home, while moving up their career. Of these, 55.5% (282) had a single child, 42% (212) had two children, and 14 individuals (2.5%) had three children. In total, 50% (255) were from dual-earner families.

2.6 Measures

Flourishing Scale

This scale is an eight-item scale of positive human functioning (Diener et al. 2010). Items evaluate features that helps in human flourishing, such as positive relationships, feelings of competence, meaning and purpose in life, and engagement with daily activities. The responses were made on a five-point scale, ranging from 1 (strongly disagree) to 5 (strongly agree). A high score on the scale would convey that respondents have a positive self-image in critical areas of functioning. A sample item is “I lead a purposeful and meaningful life.” Internal consistencies of the scale for the three samples are reported in Table 1.
Table 1

Descriptive statistics, reliabilities, and average variance extracted

 

Mean

S.D.

α

AVE

Sample 1 (N = 262)

 Flourishing scale

5.59

0.80

.89

.51

 Positive affect

3.78

0.85

.94

.64

 Negative affect

1.99

0.76

.87

.54

 Family satisfaction

4.53

1.30

.87

.58

 Job satisfaction

5.29

1.22

.76

.52

 Subjective well-being

0.00

1.00

.64

.52

Sample 2 (N = 347)

 Flourishing scale

5.80

0.70

.87

.50

 Positive affect

3.95

0.72

.90

.54

 Negative affect

2.05

0.74

.89

.52

 Family satisfaction

5.80

1.03

.89

.67

 Job satisfaction

5.69

1.06

.84

.64

 Subjective well-being

0.00

1.00

.78

.55

Sample 3 (N = 508)

 Flourishing scale

4.12

0.47

.90

.52

 Positive affect

4.33

0.68

.91

.84

 Negative affect

1.75

0.60

.86

.75

 Family satisfaction

4.15

0.46

.86

.76

 Job satisfaction

4.21

0.48

.81

.58

 Subjective well-being

0.00

0.76

.82

.62

FS: α - Cronbach’s Alpha, AVE Average variance extracted

Satisfaction with Family Scale

We used a modified version of the five-item SWLS (Diener et al. 1985) to assess the satisfaction levels with family lives. This modification has been used by other researchers studying family satisfaction (e.g. Dunn and O’Brien, 2013; Keeney et al. 2013) or other life domains. For example, the item “In most ways, my life is close to ideal” was changed to “In most ways, my family life is close to ideal.”. The responses are made on a five-point scale, ranging from 1 (strongly disagree) to 5 (strongly agree). A high score on the scale would convey that respondents have higher family satisfaction levels. Internal consistencies of the scale for the three samples are reported in Table 1.

Job Satisfaction Scale

The three-item job satisfaction scale is a subscale of the Michigan Organizational Assessment Questionnaire (MOAQ-JSS; Cammann et al. 1983). The responses are made on a 5-point scale, ranging from 1 (strongly disagree) to 5 (strongly agree). A high score on the scale would convey that respondents have higher job satisfaction levels. A sample item is “All in all I am satisfied with my job.” Internal consistencies of the scale for the three samples are reported in Table 1.

Positive and Negative Affect

Affect was assessed using the Positive and Negative Affect Schedule (PANAS, Watson et al. 1988). The positive affect (PA) scale of PANAS consisted of ten items, namely, active, alert, attentive, determined, enthusiastic, excited, inspired, interested, proud, and strong. The negative affect (NA) scale comprised of afraid, ashamed, distressed, guilty, hostile, irritable, jittery, nervous, scared, and upset. Participants rated the extent to which they had felt each of the affects “during the past few days.” We used “during the past few days” to ensure that we capture the affect component over a period aiding accurate recall and helping respondents average their experiences. Wang et al. 2012) while commenting about measuring wellbeing remarked “events occurring some time ago may be poorly recalled and contrasting experiences within an extended period can be difficult to aggregate into an overall judgment (pp. 2) and urged a compromise period of a few days or a week to measure trait. This is also the practice on other studies using the PANAS (e.g. Chen et al. 2013; Sears et al. 2011). The responses are made on a five-point scale, ranging from 1 (slightly) to 5 (very much). Internal consistencies of the scale for the three samples are reported in Table 1.

Subjective Well-Being

SWB was operationalized with Diener’s conceptualization covering affect and evaluation (Diener et al. 1985). SWB, hence, covers how people evaluate their lives, or specific aspects of their lives, positively or negatively. This includes judgments and feelings as well as affective responses such as joy or sorrow. Since this study focuses on the work and family domains, we consider it appropriate to use the job and family satisfaction aspects of the evaluative component, as opposed to a generic measure of life satisfaction, per Diener’s operationalization. This is in-line with assertions for several authors like Page and Vella-Brodrick (2013) who argue for context-specific assessments. This is supported by the abstract-specific hypothesis, which suggests that individuals responding to surveys about the quality of life will base their answers on the specificity of the mode of measurement (Cummins et al. 2002; Davern et al. 2007) and will mostly rely on heuristics or cognitive shortcuts (Tversky and Kahneman 1974) while answering questions.

The affect portion was measured using positive and negative affect using the PANAS scale. The calculation of SWB was done by taking an average of the standardized scores for job satisfaction, family satisfaction, and PA and of reverse scores of negative affect based on earlier studies using this tri-partite concept (e.g., Busseri 2015; Sheldon and Lyubomirsky 2012). Standardization is warranted as the individual variables have different variances so that the association between the SWB (the composite) and other variables being tested will not be unduly affected by any one original variable with a large variance within the composite. Higher scores indicated higher levels of SWB, and this was used for the correlational analysis. Internal consistencies of the scale for the three samples are reported in Table 1.

3 Results

3.1 Descriptive Analysis and Reliabilities

Table 1 displays the descriptive analysis, average variance extracted (AVE), and the internal consistencies for all the examined variables across the three samples. Cronbach’s α of the FS across the three samples were all above .70, ranging from .85 to .92. The reliabilities for the other study variables also exceeded .70.

3.2 Factorial Validity

Structural Equation Modeling was applied to conduct CFAs to examine the convergent and discriminant validity of the multiple-item variables. The two validities are elements that make up construct validity along with factorial validity.

Results from original study (Diener et al. 2010) indicated a unidimensional model for the FS scale. Four CFAs were performed. First a CFA was conducted for the three samples independently followed by a multigroup CFA (MGCFA). Table 2 reports chi-square (χ2) and other fit indices of all CFAs. MGCFA is a widely used technique, providing a robust statistical test for the measurement invariance of factors over different groups. Issues of configural and metric invariance were also examined (Ployhart and Vandenberg 2010). Configural invariance examines whether measures taken across samples represent the same underlying construct, and metric invariance is the degree to which indicator items for the same construct function similarly across measurements (Vandenberg and Lance 2000). Configural invariance requires that the same pattern of factor loadings is specified for each sample, that is, the CFA confirms that the items in the measuring instrument exhibit the same configuration of loadings in all three samples. Configural invariance is supported if a multiple-group model, specifying which items measure each factor, fits the data well and all item loadings are significant. The multi-group analysis tested a model of configural invariance by simultaneously evaluating the fit of the models across three samples. As is shown in Table 1, the one-factor solution fits the data adequately (Comparative Fit Index, CFI = .95; Incremental Fit Index, IFI = .95; Normed Fit Index, NFI = .94; Root Mean Square Error of Approximation, RMSEA = .06; Standardized Root Mean Square Residual, SRMR = .02) suggesting an invariant one-factor solution for the FS scale. Though the NFI was below the cut-off of .95, we accept the overall fit, given the sensitivity of the index to sample size.
Table 2

Goodness of fit statistics for tests of factorial validity of the Flourishing Scale

FS

χ2

df

χ2/df

CFI

IFI

NFI

RMSEA

SRMR

Sample 1

58.80

19

3.11

.964

.964

.940

.090

.041

Sample 2

6.00

19

3.21

.973

.973

.951

.081

.031

Sample 3

78.00

19

4.12

.973

.972

.964

.081

.040

Multi-group (Configural)

240.00

53

4.50

.964

.962

.944

.062

.030

Multi-group (Metric)

297.00

67

4.41

.953

.951

.934

.062

.040

CFI - Comparative Fit Index, IFI - Incremental Fit Index, NFI - Normed Fit Index, RMSEA - Rroot Mean Ssquare Error of Approximation, SRMR - Standardized Root Mean Square Residual

It was important to ensure that the scaling of latent variables, in the model, was the same across the three groups. A test of metric invariance was conducted to confirm that the items were measuring the same construct. The test confirmed that the factor loadings of individual items on the FS latent factor could be held equal across the three groups. While constraining factor loadings to be equal across samples, to test metric invariance, there was neither significant decrease in χ2, [Δ χ2 (14) = 38, p > .001], nor in CFI greater than .01 (Δ < .01). These results suggest that item loadings can be reasonably assumed to be invariant across samples, and it can be concluded that loadings were equivalent across groups. Overall, the unidimensional factor structure of the scale was supported.

3.3 Convergent and Discriminant Validity

For measurement validity, as shown in Table 1, AVE values for all constructs exceed .50, which is in line with the cutoff (Hair et al. 2006), suggesting that more than half of the variance of the factor is explained by the adopted items (Pavlou et al. 2007). Reliability values as reflected in Composite Reliability were all above .70, further supporting good convergent reliability (Bagozzi and Yi 1988). To assess Convergent Validity, the FS was correlated with different measures of happiness and well-being: Family Satisfaction; Job Satisfaction; PANAS and a composite SWB. Table 3 presents the findings, revealing substantial correlations ranging from .48 to .58, between the FS and other happiness measures resulting in maintaining consistency with the original study (Diener et al. 2010). The FS showed a higher correlation with the SWB scale.
Table 3

Correlations between the Flourishing Scale and other well-being measures

Flourishing scale

Positive affect

Negative affect

Family satisfaction

Job satisfaction

SWBa

Sample 1

.59**

−.30**

.49**

.48**

.80**

Sample 2

.64**

−.40**

.48**

.42**

.67**

Sample 3

.38**

−.34**

.58**

.57**

.54**

aSubjective Well-being - averaging standardized scores for job satisfaction, reverse scored negative affect and positive affect; **p < .01

According to one of the most stringent tests of discriminant validity (i.e., Fornell and Larcker 1981), discriminant validity is demonstrated when the average variance extracted (AVE) for each variable exceeds the shared variance between each pair of variables. AVE measures the explained variance of the construct. When comparing AVE with the correlation coefficient, we check whether the items of the construct explain more variance than the items of the other constructs. This is done by comparing AVE with the square of the cross-correlation and checking whether that value is less than the AVE for the construct (Fornell and Larcker 1981; Pavlou et al. 2007). Table 4 shows squared correlations and when compared to Table 1, we see conditions are satisfied except for Sample 1 where the squared correlation of SWB and FS was higher than the AVE for FS (.64 vs .51, see Table 4). Since this was only found in one sample and only for one construct which is a composite of PA, NA, and the satisfaction scales, we proceed acknowledging this deviation. Thus, discriminant and convergent validity for each construct was established.
Table 4

Square of correlations across samples

Flourishing scale

Positive affect

Negative affect

Family satisfaction

Job satisfaction

SWBa

Sample 1

.35

.09

.24

.23

.64

Sample 2

.41

.16

.23

.18

.45

Sample 3

.14

.12

.33

.23

.27

aSubjective Well-Being - averaging standardized scores for job satisfaction, reverse scored negative affect and positive affect

4 Discussion

The FS was evaluated with the help of data acquired from three samples of married individuals working in the IT/ITES industry in India. The CFA revealed a one-factor structure for the scale, like the original study from the USA (Diener et al. 2010) and studies from other regions. This one-factor structure was invariant across the three samples, that is, the factor loadings of the items on the underlying factor did not differ systematically across samples. In accordance with what was found in the original study of the FS, the scale was verified to show an excellent internal consistency reliability across the samples (α: .87, .89, and .90). The convergent validity of the scale was analyzed by exploring the relation of the FS to the other well-being measures – PANAS, measures of job and family satisfaction, and SWB. The FS scale showed high correlations with the measures, proving the convergent validity of the scale. Steenkamp and van Trijp (1991) showed that the presence of a single latent factor in a scale is the most valued sign of construct validity. Hattie (1985) stated “One of the most critical and basic assumptions of measurement theory is that a set of items forming an instrument all measure just one thing in common.” (p. 149). In comparison with the other Indian study using FS (Singh et al. 2016), the results are similar with respect to reliability of the scale, factor structure, and correlations with subjective well-being constructs. In summary, for the study across the three samples, the FS emerged as a valid instrument to assess eudaimonic well-being with the help of one single score.

Overall, the FS is adequate to assess eudaimonic well-being, and particularly if a shorter version of a eudaimonic well-being is required. Other scales like Ryff’s scales of Psychological Well-being (Ryff and Keyes 1995), the Questionnaire for Eudaimonic Well-Being (Waterman et al. 2010), or the Mental Health Continuum (Keyes 2002) are longer and this could be a credible alternative. In short, the study achieved the goals of the research, and the results obtained demonstrated that the scale had satisfactory psychometric properties similar to those shown in the original study (Diener et al. 2010) and showed satisfactory reliability, convergent validity and discriminant validity.

There are some limitations to the present research. First, all data were collected cross-sectionally using self-reports, which could lead to concerns about common method variance. Future studies should try collecting data for variables at different times. Introducing a time lag into the data collection helps minimize consistency themes and demand characteristics. Future studies would enhance the scale’s test–retest reliability by exploring stability over differing time periods for the same respondent pool. Next, the samples in this study had restrictions. The entire sample consisted of married IT/ITES professionals, who, although ethnically diverse, were homogeneous in terms of socio-economic and educational background. The psychometric properties of FS should be examined further in different populations, for example, with millennials who may have other values and priorities that reflect their idea of a life well lived or pursuits that are eudaimonic. This study should also be replicable in other samples comprising of married individuals and perhaps across other industries (manufacturing, hospitality, or retail) to ensure that there is further invariance across demographic cohorts. Third, this study only uses a limited set of wellbeing measures to examine convergent and discriminant validity. Future studies could also look at more traditional well-being measures (e.g., Satisfaction with Life Scale) and psychological symptoms (e.g., General Health Questionnaire, Goldberg and Williams, 2000; Scales of Psychological Well-being, Ryff and Keyes 1995) to evaluate convergent and discriminant validity more comprehensively.

With rising levels of dual earner couples in India, it would be interesting to take a dyadic approach using the actor partner interdependence model (APIM; Kenny et al. 2006) to examine partner effects in analyzing how spousal eudaimonia for dual earners influence individual levels. Another aspect while looking at the concept of eudaimonia is correlation with other similar constructs such as spiritual well-being (Fisher et al. 2000).

In summary, it can be stated that the FS has been validated for the present married IT/ITES employees from India. Despite the sector-specific nature of the study, the application of the scale in the service sector would be of high value because this target group is most likely to experience stress (Dhar and Dhar 2010; Vaid 2009), lack of work–life balance (Singh 2010), and work exhaustion (Ahuja et al. 2007; Budhwar et al. 2006). As practitioners within these sectors plan employee self-assessment-based interventions, the FS can be used as a summary measure of self-reported psychological functioning. This is also useful for policy makers who can administer well-being surveys using the FS in addition to employing either theoretically or practically important well-being correlates. Easterlin (1974) remarked, that in societies with rising incomes, it is not necessary that economic growth of nations produces rising happiness. Hence testing eudaimonia in developing countries like India becomes imperative. Other researchers have also begun to empirically test these claims (e.g. Diener et al. 2013), and scales such as the FS will be immensely beneficial to assess psychological or eudaimonic well-being in developing economies such as India.

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indian Institute of ManagementLucknowIndia

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