research paper on happiness index

Conceptualization of Happiness Index Model

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• Abdul Kadir Othman 19 ,
• Fauziah Noordin 19 ,
• Anitawati Mohd Lokman 19 ,
• Norlida Jaafar 19 &
• Idaya Husna Mohd 19  

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 739))

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• International Conference on Kansei Engineering & Emotion Research

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Creating a happy environment among the employees of organizations is important including those working with the higher educational institutions. However, developing the right model and instrument to measure happiness is critical as they must be relevant to the context and setting of the study. Reviewing the existing work on the happiness index in higher educational institutions, most studies have adapted the PERMA model that was developed by Martin Seligman in 2011. The model comprises positive emotions that refer to hedonic feelings of happiness (e.g. feeling joyful, content, and cheerful), engagement that refers to psychological connection to activities or organizations (e.g. feeling absorbed, interested, and engaged in life), positive relationships that include feeling socially integrated, cared about and supported by others, and satisfied with one’s social connections, meaning that refers to believing that one’s life is valuable and feeling connected to something greater than oneself, and accomplishment that involves making progress toward goals, feeling capable to do daily activities, and having a sense of achievement. The results of the focus group study that was conducted indicate two additional dimensions of happiness emerged that include infrastructure that refers to the perception of staff on the maintenance and availability of the facilities in the organization and gratitude that refers to the levels of gratitude of staff on overall facilities and services offered of the organization.

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Abdul Kadir Othman, Fauziah Noordin, Anitawati Mohd Lokman, Norlida Jaafar & Idaya Husna Mohd

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Othman, A.K., Noordin, F., Lokman, A.M., Jaafar, N., Mohd, I.H. (2018). Conceptualization of Happiness Index Model. In: Lokman, A., Yamanaka, T., Lévy, P., Chen, K., Koyama, S. (eds) Proceedings of the 7th International Conference on Kansei Engineering and Emotion Research 2018. KEER 2018. Advances in Intelligent Systems and Computing, vol 739. Springer, Singapore. https://doi.org/10.1007/978-981-10-8612-0_86

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Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries

• Kai Ruggeri 1 , 2 ,
• Eduardo Garcia-Garzon 3 ,
• Áine Maguire 4 ,
• Sandra Matz 5 &
• Felicia A. Huppert 6 , 7  

Health and Quality of Life Outcomes volume  18 , Article number:  192 ( 2020 ) Cite this article

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Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP).

To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB).

The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions.

Conclusions

We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

What is well-being?

Well-being has been defined as the combination of feeling good and functioning well; the experience of positive emotions such as happiness and contentment as well as the development of one’s potential, having some control over one’s life, having a sense of purpose, and experiencing positive relationships [ 23 ]. It is a sustainable condition that allows the individual or population to develop and thrive. The term subjective well-being is synonymous with positive mental health. The World Health Organization [ 45 ] defines positive mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community”. This conceptualization of well-being goes beyond the absence of mental ill health, encompassing the perception that life is going well.

Well-being has been linked to success at professional, personal, and interpersonal levels, with those individuals high in well-being exhibiting greater productivity in the workplace, more effective learning, increased creativity, more prosocial behaviors, and positive relationships [ 10 , 27 , 37 ]. Further, longitudinal data indicates that well-being in childhood goes on to predict future well-being in adulthood [ 39 ]. Higher well-being is linked to a number of better outcomes regarding physical health and longevity [ 13 ] as well as better individual performance at work [ 30 ], and higher life satisfaction has been linked to better national economic performance [ 9 ].

Measurement of well-being

Governments and researchers have attempted to assess the well-being of populations for centuries [ 2 ]. Often in economic or political research, this has ended up being assessed using a single item about life satisfaction or happiness, or a limited set of items regarding quality of life [ 3 ]. Yet, well-being is a multidimensional construct, and cannot be adequately assessed in this manner [ 14 , 24 , 29 ]. Well-being goes beyond hedonism and the pursuit of happiness or pleasurable experience, and beyond a global evaluation (life satisfaction): it encompasses how well people are functioning, known as eudaimonic, or psychological well-being. Assessing well-being using a single subjective item approach fails to offer any insight into how people experience the aspects of their life that are fundamental to critical outcomes. An informative measure of well-being must encompass all the major components of well-being, both hedonic and eudaimonic aspects [ 2 ], and cannot be simplified to a unitary item of income, life satisfaction, or happiness.

Following acknowledgement that well-being measurement is inconsistent across studies, with myriad conceptual approaches applied [ 12 ], Huppert and So [ 27 ] attempted to take a systematic approach to comprehensively measure well-being. They proposed that positive mental health or well-being can be viewed as the complete opposite to mental ill health, and therefore attempted to define mental well-being in terms of the opposite of the symptoms of common mental disorders. Using the DSM-IV and ICD-10 symptom criteria for both anxiety and depression, ten features of psychological well-being were identified from defining the opposite of common symptoms. The features encompassed both hedonic and eudaimonic aspects of well-being: competence, emotional stability, engagement, meaning, optimism, positive emotion, positive relationships, resilience, self-esteem, and vitality. From these ten features an operational definition of flourishing, or high well-being, was developed using data from Round 3 of the European Social Survey (ESS), carried out in 2006. The items used in the Huppert and So [ 27 ] study were unique to that survey, which reflects a well-being framework based on 10 dimensions of good mental health. An extensive discussion on the development and validation of these measures for the framework is provided in this initial paper [ 27 ].

As this was part of a major, multinational social survey, each dimension was measured using a single item. As such, ‘multidimensional’ in this case refers to using available measures identified for well-being, but does not imply a fully robust measure of these individual dimensions, which would require substantially more items that may not be feasible for population-based work related to policy development. More detailed and nuanced approaches might help to better capture well-being as a multidimensional construct, and also may consider other dimensions. However, brief core measures such as the one implemented in the ESS are valuable as they provide a pragmatic way of generating pioneering empirical evidence on well-being across different populations, and help direct policies as well as the development of more nuanced instruments. While this naturally would benefit from complementary studies of robust measurement focused on a single topic, appropriate methods for using sprawling social surveys remain critical, particularly through better standardization [ 6 ]. While this paper will overview those findings, we strongly encourage more work to that end, particularly in more expansive measures to support policy considerations.

General approach and key questions

The aim of the present study was to develop a more robust measurement of well-being that allows researchers and policymakers to measure well-being both as a composite construct and at the level of its fundamental dimensions. Such a measure makes it possible to study overall well-being in a manner that goes beyond traditional single-item measures, which capture only a fraction of the dimensions of well-being, and because it allows analysts to unpack the measure into its core components to identify strengths and weaknesses. This would produce a similar approach as the most common reference for policy impacts: Gross Domestic Product (GDP), which is a composite measure of a large number of underlying dimensions.

The paper is structured as follows: in the first step, data from the ESS are used to develop a composite measure of well-being from the items suggested by Huppert & So [ 27 ] using factor analysis. In the second step, the value of the revised measure is demonstrated by generating insights into the well-being of 21 European countries, both at the level of overall well-being and at the level of individual dimensions.

The European social survey

The ESS is a biannual survey of European countries. Through comprehensive measurement and random sampling techniques, the ESS provides a representative sample of the European population for persons aged 15 and over [ 38 ]. Both Round 3 (2006–2007) and Round 6 (2012–2013) contained a supplementary well-being module. This module included over 50 items related to all aspects of well-being including psychological, social, and community well-being, as well as incorporating a brief measure of symptoms of psychological distress. As summarized by Huppert et al. [ 25 ], of the 50, only 30 items relate to personal well-being, of which only 22 are positive measures. Of those remaining, not all relate to the 10 constructs identified by Huppert and So [ 27 ], so only a single item could be used, or else the item that had the strongest face validity and distributional items were chosen.

Twenty-two countries participated in the well-being modules in both Round 3 and Round 6. As this it within a wider body of analyses, it was important to focus on those initially. Hungary did not have data for the vitality item in Round 3 and was excluded from the analysis, as appropriate models would not have been able to reliably resolve a missing item for an entire country. To be included in the analysis and remain consistent, participants therefore had to complete all 10 items used and have the age, gender, employment, and education variables completed. Employment was classified into four groups: students, employed, unemployed, retired; other groups were excluded. Education was classified into three groups: low (less than secondary school), middle (completed secondary school), and high (postsecondary study including any university and above). Using these criteria, the total sample for Round 6 was 41,825 people from 21 countries for analysis. The full sample was 52.6% female and ranged in age from 15 to 103 (M = 47.9; SD = 18.9). Other details about participation, response rates, and exclusion have been published elsewhere [ 38 ].

Huppert & So [ 27 ] defined well-being using 10 items extracted from the Round 3 items, which represent 10 dimensions of well-being. However, the items used in Round 3 to represent positive relationships and engagement exhibited ceiling effects and were removed from the questionnaire in Round 6. Four alternatives were available to replace each question. Based on their psychometric properties (i.e., absence of floor effects and wider response distributions), two new items were chosen for positive relationships and engagement (one item for each dimension). The new items and those they replaced can be seen in Table  1 (also see Supplement ).

Development of a composite measure of psychological well-being (MPWB)

A composite measure of well-being that yields an overall score for each individual was developed. From the ten indicators of well-being shown in Table 1 , a single factor score was calculated to represent MPWB. This overall MPWB score hence constitutes a summary of how an individual performs across the ten dimensions, which is akin to a summary score such as GDP, and will be of general value to policymakers. Statistical analysis was performed in R software, using lavaan [ 40 ] and lavaan.survey [ 35 ] packages. The former is a widely-used package for the R software designed for computing structural equation models and confirmatory factor analyses (CFA). The latter allows introducing complex survey design weights (combination of design and population size weights) when estimating confirmatory factor analysis models with lavaan, which ensures that MPWB scoring followed ESS guidelines regarding both country-level and survey specific weights [ 17 ]. Both packages have been previously tested and validated in various analyses using ESS data (as explained in detail in lavaan.survey documentation).

It should be noted that Round 6 was treated as the focal point of these efforts before repeating for Round 3, primarily due to the revised items that were problematic in Round 3, and considering that analyses of the 2006 data are already widely available.

Prior to analysis, all items were coded such that higher scores were more positive and lower scores more negative. Several confirmatory factor analysis models were performed in order to test several theoretical conceptualizations regarding MPWB. Finally, factor scores (expected a posteriori [ 15 ];) were calculated for the full European sample and used for descriptive purposes. The approach and final model are presented in supplemental material .

Factor scores are individual scores computed as weighted combinations of each person’s response on a given item and the factor scoring coefficients. This approach is to be preferred to using raw or sum scores: sum or raw scores fail to consider how well a given item serves as an indicator of the latent variable (i.e., all items are unrealistically assumed to be perfect and equivalent measures of MPWB). They also do not take into account that different items could present different variability, which is expected to occur if items present different scales (as in our case). Therefore, the use of such simple methods results in inaccurate individual rankings for MPWB. To resolve this, factor scores are both more informative and more accurate, as they avoid the propagation of measurement error in subsequent analyses [ 19 ].

Not without controversy (see Supplement ), factor scores are likely to be preferable to sum scores when ranking individuals on unobservable traits that are expected to be measured with noticeable measurement error (such as MPWB [ 32 ];). Similar approaches based on factor scoring have been successfully applied in large international assessment research [ 21 , 34 ]. With the aim of developing a composite well-being score, it was necessary to provide a meaningful representation of how the different well-being indicators are reflected in the single measure. A hierarchical model with one higher-order factor best approximated MPWB along with two first-order factors (see supplement Figure S 1 ). This model replicates the factor structure reported for Round 3 by Huppert & So [ 27 ]. The higher-order factor explained the relationship between two first-order factors (positive functioning and positive characteristics showed a correlation of ρ = .85). In addition, modelling standardized residuals showed that the items representing vitality and emotional stability and items representing optimism and self-esteem were highly correlated. The similarities in wording in both pairs of items (see Table 1 ) are suspected to be responsible for such high residual correlations. Thus, those correlations were included in the model. As presented in Table  2 , the hierarchical model was found to fit the data better than any other model but a bi-factor model including these correlated errors. The latter model resulted in collapsed factor structure with a weak, bi-polar positive functioning factor. However, this bi-factor model showed a problematic bi-polar group factor with weak loadings. Whether this group factor was removed (resulting in a S-1 bi-factor model, as in [ 16 ]), model fit deteriorated. Thus, neither bi-factor alternative was considered to be acceptable.

To calculate the single composite score representing MPWB, a factor scoring approach was used rather than a simplistic summing of raw scores on these items. Factor scores were computed and standardized for the sample population as a whole, which make them suitable for broad comparison [ 8 ]. This technique was selected for two reasons. First, it has the ability to take into account the different response scales used for measuring the items included in the multidimensional well-being model. The CFA model, from which MPWB scores were computed, was defined such that the metric of the MPWB was fixed, which results in a standardized scale. Alternative approaches, such as sum or raw scores, would result in ignoring the differential variability across items, and biased individual group scores. Our approach, using factor scoring, resolves this issue by means of standardization of the MPWB scores. The second reason for this technique is that it could take account of how strongly each item loaded onto the MPWB factor. It should be noted that by using only two sub-factors, the weight applied to the general factor is identical within the model for each round. This model was also checked to ensure it also was a good fit for different groups based on gender, age, education and employment.

Separate CFA analyses per each country indicate that the final model fit the data adequately in all countries (.971 < CFI < .995; .960 < TFI < .994; .020 < RMSEA < .05; 0,023 < SRMR < 0,042). All items presented substantive loadings on their respective factors, and structures consistently replicated across all tested countries. Largest variations were found when assessing the residual items’ correlations (e.g., for emotional stability and vitality correlation, values ranged from 0,076 to .394). However, for most cases, residuals correlations were of similar size and direction (for both cases, the standard deviation of estimated correlations was close of .10). Thus, strong evidence supporting our final model was systematically found across all analyzed countries. Full results are provided in the supplement (Tables S 2 -S 3 ).

Model invariance

In order to establish meaningful comparisons across groups within and between each country, a two-stage approach was followed, resulting in a structure that was successfully found to be similar across demographics. First, a descriptive comparison of the parameter estimates unveiled no major differences across groups. Second, factor scores were derived for the sample, employing univariate statistics to compare specific groups within country and round. In these analyses, neither traditional nor modern approaches to factor measurement invariance were appropriate given the large sample and number of comparisons at stake ([ 8 ]; further details in Supplement ).

From a descriptive standpoint, the hierarchical structure satisfactorily fit both Round 3 and Round 6 data. All indicators in both rounds had substantial factor loadings (i.e., λ > .35). A descriptive comparison of parameter estimates produced no major differences across the two rounds. The lack of meaningful differences in the parameter estimates confirms that this method for computing MPWB can be used in both rounds.

As MPWB scores from both rounds are obtained from different items that have different scales for responses, it is necessary to transform individual scores obtained from both rounds in order to be aligned. To do this between Round 3 and Round 6 items, a scaling approach was used. To produce common metrics, scores from Round 3 were rescaled using a mean and sigma transformation (Kolen & Brennan 2010) to align with Round 6 scales. This was used as Round 6 measures were deemed to have corrected some deficiencies found in Round 3 items. This does not change outcomes in either round but simply makes the scores match in terms of distributions relative to their scales, making them more suitable for comparison.

As extensive descriptive insights on the sample and general findings are already available (see [ 41 ]), we focus this section on the evidence derived directly from the proposed approach to MPWB scores. For the combined single score for MPWB, the overall mean (for all participants combined) is fixed to zero, and the scores represent deviation from the overall mean. In 2012 (Round 6), country scores on well-being ranged from − 0.41 in Bulgaria to 0.46 in Denmark (Fig.  1 ). There was a significant, positive relationship between national MPWB mean scores and national life satisfaction means ( r =  .56 (.55–.57), p  < .001). In addition, MPWB was negatively related with depression scores and positively associated with other well-being measurements (see Supplement ).

Distribution of national MPWB means and confidence intervals across Europe

Denmark having the highest well-being is consistent with many studies [ 4 , 18 ] and with previous work using ESS data [ 27 ]. While the pattern is typically that Nordic countries are doing the best and that eastern countries have the lowest well-being, exceptions exist. The most notable exception is Portugal, which has the third-lowest score and is not significantly higher than Ukraine, which is second lowest. Switzerland and Germany are second and third highest respectively, and show generally similar patterns to the Scandinavian countries (see Fig. 1 ). It should be noted that, for Figs.  1 , 2 , 3 , 4 , 5 , countries with the lowest well-being are at the top. This is done to highlight the greatest areas for potential impact, which are also the most of concern to policy.

Well-being by country and gender

Well-being by country and age

Well-being by country and employment

Well-being by country and education

General patterns across the key demographic variables – gender, age, education, employment – are visible across countries as seen in Figs.  1 , 2 , 3 , 4 , 5 (see also Supplement 2 ). These figures highlight patterns based on overall well-being as well as potential for inequalities. The visualizations presented here, though univariate, are for the purpose of understanding broad patterns while highlighting the need to disentangle groups and specific dimensions to generate effective policies.

For gender, women exhibited lower MPWB scores than men across Europe (β = −.09, t (36508) = − 10.37; p  < .001). However, these results must be interpreted with caution due to considerable overlap in confidence intervals for many of the countries, and greater exploration of related variables is required. This also applies for the five countries (Estonia, Finland, Ireland, Slovakia, Ukraine) where women have higher means than men. Only four countries have significant differences between genders, all of which involve men having higher scores than women: the Netherlands (β = −.12, t (1759) = − 3.24; p  < .001), Belgium (β = −.14, t (1783) = − 3.94; p  < .001), Cyprus (β = −.18, t (930) = − 2.87; p  < .001) and Portugal (β = −.19, t (1847) = − 2.50; p  < .001).

While older individuals typically exhibited lower MPWB scores compared to younger age groups across Europe (β 25–44  = −.05, t (36506) = − 3.686, p  < .001; β 45–65  = −.12, t (36506) = − 8.356, p  < .001; β 65–74  = −.16, t (36506) = − 8.807, p  < .001; β 75+  = −.28, t (36506) = − 13.568, p  < .001), the more compelling pattern shows more extreme differences within and between age groups for the six countries with the lowest well-being. This pattern is most pronounced in Bulgaria, which has the lowest overall well-being. For the three countries with the highest well-being (Denmark, Switzerland, Germany), even the mean of the oldest age group was well above the European average, while for the countries with the lowest well-being, it was only young people, particularly those under 25, who scored above the European average. With the exception of France and Denmark, countries with higher well-being typically had fewer age group differences and less variance within or between groups. Only countries with the lowest well-being showed age differences that were significant with those 75 and over showing the lowest well-being.

MPWB is consistently higher for employed individuals and students than for retired (β = −.31, t (36506) = − 21.785; p  < .00) or unemployed individuals (β = −.52, t (36556) = − 28.972; p  < .001). Unemployed groups were lowest in nearly all of the 21 countries, though the size of the distance from other groups did not consistently correlate with national MPWB mean. Unemployed individuals in the six countries with the lowest well-being were significantly below the mean, though there is little consistency across groups and countries by employment beyond that. In countries with high well-being, unemployed, and, in some cases, retired individuals, had means below the European average. In countries with the lowest well-being, it was almost exclusively students who scored above the European average. Means for retired groups appear to correlate most strongly with overall well-being. There is minimal variability for employed groups in MPWB means within and between countries.

There is a clear pattern of MPWB scores increasing with education level, though the differences were most pronounced between low and middle education groups (β = .12, t (36508) = 9.538; p  < .001). Individuals with high education were significantly higher on MPWB than those in the middle education group (β = .10, t (36508) =11.06; p  < .001). Differences between groups were noticeably larger for countries with lower overall well-being, and the difference was particularly striking in Bulgaria. In Portugal, medium and high education well-being means were above the European average (though 95% confidence intervals crossed 0), but educational attainment is significantly lower in the country, meaning the low education group represents a greater proportion of the population than the other 21 countries. In the six countries with the highest well-being, mean scores for all levels of education were above the European mean.

Utilizing ten dimensions for superior understanding of well-being

It is common to find rankings of national happiness and well-being in popular literature. Similarly, life satisfaction is routinely the only measure reported in many policy documents related to population well-being. To demonstrate why such limited descriptive approaches can be problematic, and better understood using multiple dimensions, all 21 countries were ranked individually on each of the 10 indicators of well-being and MPWB in Round 6 based on their means. Figure  6 demonstrates the variations in ranking across the 10 dimensions of well-being for each country.

Country rankings in 2012 on multidimensional psychological well-being and each of its 10 dimensions

The general pattern shows typically higher rankings for well-being dimensions in countries with higher overall well-being (and vice-versa). Yet countries can have very similar scores on the composite measure but very different underlying profiles in terms of individual dimensions. Figure  7 a presents this for two countries with similar life satisfaction and composite well-being, Belgium and the United Kingdom. Figure 7 b then demonstrates this even more vividly for two countries, Finland and Norway, which have similar composite well-being scores and identical mean life satisfaction scores (8.1), as well as have the highest two values for happiness of all 21 countries. In both pairings, the broad outcomes are similar, yet countries consistently have very different underlying profiles in individual dimensions. The results indicate that while overall scores can be useful for general assessment, specific dimensions may vary substantially, which is a relevant first step for developing interventions. Whereas the ten items are individual measures of 10 areas of well-being, had these been limited to a single domain only, the richness of the underlying patterns would have been lost, and the limitation of single item approaches amplified.

a Comparison of ranks for dimensions of well-being between two different countries with similar life satisfaction in 2012: Belgium and United Kingdom. b Comparison of ranks for dimensions of well-being between two similar countries with identical life satisfaction and composite well-being scores in 2012: Finland and Norway

The ten-item multidimensional measure provided clear patterns for well-being across 21 countries and various groups within. Whether used individually or combined into a composite score, this approach produces more insight into well-being and its components than a single item measure such as happiness or life satisfaction. Fundamentally, single items are impossible to unpack in reverse to gain insights, whereas the composite score can be used as a macro-indicator for more efficient overviews as well as deconstructed to look for strengths and weaknesses within a population, as depicted in Figs.  6 and 7 . Such deconstruction makes it possible to more appropriately target interventions. This brings measurement of well-being in policy contexts in line with approaches like GDP or national ageing indexes [ 7 ], which are composite indicators of many critical dimensions. The comparison with GDP is discussed at length in the following sections.

Patterns within and between populations

Overall, the patterns and profiles presented indicate a number of general and more nuanced insights. The most consistent among these is that the general trend in national well-being is usually matched within each of the primary indicators assessed, such as lower well-being within unemployed groups in countries with lower overall scores than in those with higher overall scores. While there are certainly exceptions, this general pattern is visible across most indicators.

The other general trend is that groups with lower MPWB scores consistently demonstrate greater variability and wider confidence intervals than groups with higher scores. This is a particularly relevant message for policymakers given that it is an indication of the complexity of inequalities: improvements for those doing well may be more similar in nature than for those doing poorly. This is particularly true for employment versus unemployment, yet reversed for educational attainment. Within each dimension, the most critical pattern is the lack of consistency for how each country ranks, as discussed further in other sections.

Examining individual dimensions of well-being makes it possible to develop a more nuanced understanding of how well-being is impacted by societal indicators, such as inequality or education. For example, it is possible that spending more money on education improves well-being on some dimensions but not others. Such an understanding is crucial for the implementation of targeted policy interventions that aim at weaker dimensions of well-being and may help avoid the development of ineffective policy programs. It is also important to note that the patterns across sociodemographic variables may differ when all groups are combined, compared to results within countries. Some effects may be larger when all are combined, whereas others may have cancelling effects.

Using these insights, one group that may be particularly important to consider is unemployed adults, who consistently have lower well-being than employed individuals. Previous research on unemployment and well-being has often focused on mental health problems among the unemployed [ 46 ] but there are also numerous studies of differences in positive aspects of well-being, mainly life satisfaction and happiness [ 22 ]. A large population-based study has demonstrated that unemployment is more strongly associated with the absence of positive well-being than with the presence of symptoms of psychological distress [ 28 ], suggesting that programs that aim to increase well-being among unemployed people may be more effective than programs that seek to reduce psychological distress.

Certainly, it is well known that higher income is related to higher subjective well-being and better health and life expectancy [ 1 , 42 ], so reduced income following unemployment is likely to lead to increased inequalities. Further work would be particularly insightful if it included links to specific dimensions of well-being, not only the comprehensive scores or overall life satisfaction for unemployed populations. As such, effective responses would involve implementation of interventions known to increase well-being in these groups in times of (or in spite of) low access to work, targeting dimensions most responsible for low overall well-being. Further work on this subject will be presented in forthcoming papers with extended use of these data.

This thinking also applies to older and retired populations in highly deprived regions where access to social services and pensions are limited. A key example of this is the absence in our data of a U-shaped curve for age, which is commonly found in studies using life satisfaction or happiness [ 5 ]. In our results, older individuals are typically lower than what would be expected in a U distribution, and in some cases, the oldest populations have the lowest MPWB scores. While previous studies have shown some decline in well-being beyond the age of 75 [ 20 ], our analysis demonstrates quite a severe fall in MPWB in most countries. What makes this insight useful – as opposed to merely unexpected – is the inclusion of the individual dimensions such as vitality and positive relationships. These dimensions are clearly much more likely to elicit lower scores than for younger age groups. For example, ageing beyond 75 is often associated with increased loneliness and isolation [ 33 , 43 ], and reduction in safe, independent mobility [ 31 ], which may therefore correspond with lower scores on positive relationships, engagement, and vitality, and ultimately lower scores on MPWB than younger populations. Unpacking the dimensions associated with the age-related decline in well-being should be the subject of future research. The moderate positive relationship of MPWB scores with life satisfaction is clear but also not absolute, indicating greater insights through multidimensional approaches without any obvious loss of information. Based on the findings presented here, it is clearly important to consider ensuring the well-being of such groups, the most vulnerable in society, during periods of major social spending limitations.

Policy implications

Critically, Fig.  6 represents the diversity of how countries reach an overall MPWB score. While countries with overall high well-being have typically higher ranks on individual items, there are clearly weak dimensions for individual countries. Conversely, even countries with overall low well-being have positive scores on some dimensions. As such, the lower items can be seen as potential policy levers in terms of targeting areas of concern through evidence-based interventions that should improve them. Similarly, stronger areas can be seen as learning opportunities to understand what may be driving results, and thus used to both sustain those levels as well as potentially to translate for individuals or groups not performing as well in that dimension. Collectively, we can view this insight as a message about specific areas to target for improvement, even in countries doing well, and that even countries doing poorly may offer strengths that can be enhanced or maintained, and could be further studied for potential applications to address deficits. We sound a note of caution however, in that these patterns are based on ranks rather than actual values, and that those ranks are based on single measures.

Figure 7 complements those insights more specifically by showing how Finland and Norway, with a number of social, demographic, and economic similarities, plus identical life satisfaction scores (8.1) arrive at similar single MPWB scores with very different profiles for individual dimensions. By understanding the levers that are specific to each country (i.e. dimensions with the lowest well-being scores), policymakers can respond with appropriate interventions, thereby maximizing the potential for impact on entire populations. Had we restricted well-being measurement to a single question about happiness, as is commonly done, we would have seen both countries had similar and extremely high means for happiness. This might have led to the conclusion that there was minimal need for interventions for improving well-being. Thus, in isolation, using happiness as the single indicator would have masked the considerable variability on several other dimensions, especially those dimensions where one or both had means among the lowest of the 21 countries. This would have resulted in similar policy recommendations, when in fact, Norway may have been best served by, for example, targeting lower dimensions such as Engagement and Self-Esteem, and Finland best served by targeting Vitality and Emotional Stability.

Targeting specific groups and relevant dimensions as opposed to comparing overall national outcomes between countries is perhaps best exemplified by Portugal, which has one of the lowest educational attainment rates in OECD countries, exceeded only by Mexico and Turkey [ 36 ]. This group thus skews the national MPWB score, which is above average for middle and high education groups, but much lower for those with low education. Though this pattern is not atypical for the 21 countries presented here, the size of the low education group proportional to Portugal’s population clearly reduces the national MPWB score. This implies that the greatest potential for improvement is likely to be through addressing the well-being of those with low education as a near-term strategy, and improving access to education as a longer-term strategy. It will be important to analyze this in the near future, given recent reports that educational attainment in Portugal has increased considerably in recent years (though remains one of the lowest in OECD countries) [ 36 ].

One topic that could not be addressed directly is whether these measures offer value as indicators of well-being beyond the 21 countries included here, or even beyond the countries included in ESS generally. In other words, are these measures relevant only to a European population or is our approach to well-being measurement translatable to other regions and purposes? Broadly speaking, the development of these measures being based on DSM and ICD criteria should make them relevant beyond just the 21 countries, as those systems are generally intended to be global. However, it can certainly be argued that these methods for designing measures are heavily influenced by North American and European medical frameworks, which may limit their appropriateness if applied in other regions. Further research on these measures should consider this by adding potential further measures deemed culturally appropriate and seeing if comparable models appear as a result.

A single well-being score

One potential weakness remains the inconsistency of scaling between ESS well-being items used for calculating MPWB. However, this also presents an opportunity to consider the relative weighting of each item within the current scales, and allow for the development of a more consistent and reliable measure. These scales could be modified to align in separate studies with new weights generated – either generically for all populations or stratified to account for various cultural or other influences. Using these insights, scales could alternatively be produced to allow for simple scoring for a more universally accessible structure (e.g. 1–100) but with appropriate values for each item that represents the dimensions, if this results in more effective communication with a general public than a standardized score with weights. Additionally, common scales would improve on attempts to use rankings for presenting national variability within and between dimensions. Researchers should be aware that factor scores are sample-dependent (as based on specific factor model parameters such as factor loadings). Nevertheless, future research focused on investigating specific item differential functioning (by means of multidimensional item response functioning or akin techniques) of these items across situations (i.e., rounds) and samples (i.e., rounds and countries) should be conducted in order to have a more nuanced understanding of this scale functioning.

What makes this discussion highly relevant is the value of a more informed measure to replace traditional indicators of well-being, predominantly life satisfaction. While life satisfaction may have an extensive history and present a useful metric for comparisons between major populations of interest, it is at best a corollary, or natural consequence, of other indicators. It is not in itself useful for informing interventions, in the same way limiting to a single item for any specific dimension of well-being should not alone inform interventions.

By contrast, a validated and standardized multidimensional measure is exceptionally useful in its suitability to identify those at risk, as well as its potential for identifying areas of strengths and weaknesses within the at-risk population. This can considerably improve the efficiency and appropriateness of interventions. It identifies well-understood dimensions (e.g. vitality, positive emotion) for direct application of evidence-based approaches that would improve areas of concern and thus overall well-being. Given these points, we strongly argue for the use of multidimensional approaches to measurement of well-being for setting local and national policy agenda.

There are other existing single-score approaches for well-being addressing its multidimensional nature. These include the Warwick-Edinburgh Mental Well-Being Scale [ 44 ] and the Flourishing Scale [ 11 ]. In these measures, although the single score is derived from items that clearly tap a number of dimensions, the dimensions have not been systematically derived and no attempt is made to measure the underlying dimensions individually. In contrast, the development approach used here – taking established dimensions from DSM and ICD – is based on years of international expertise in the field of mental illness. In other words, there have long been adequate measures for identifying and understanding illness, but there is room for improvement to better identify and understand health. With increasing support for the idea of these being a more central focus of primary outcomes within economic policies, such approaches are exceptionally useful [ 13 ].

Better measures, better insights

Naturally, it is not a compelling argument to simply state that more measures present greater information than fewer or single measures, and this is not the primary argument of this manuscript. In many instances, national measures of well-being are mandated to be restricted to a limited set of items. What is instead being argued is that well-being itself is a multidimensional construct, and if it is deemed a critical insight for establishing policy agenda or evaluating outcomes, measurements must follow suit and not treat happiness and life satisfaction values as universally indicative. The items included in ESS present a very useful step to that end, even in a context where the number of items is limited.

As has been argued by many, greater consistency in measurement of well-being is also needed [ 26 ]. This may come in the form of more consistency regarding dimensions included, the way items are scored, the number of items representing each dimension, and changes in items over time. While inconsistency may be prevalent in the literature to date for definitions and measurement, the significant number of converging findings indicates increasingly robust insights for well-being relevant to scientists and policymakers. Improvements to this end would support more systematic study of (and interventions for) population well-being, even in cases where data collection may be limited to a small number of items.

The added value of MPWB as a composite measure

While there are many published arguments (which we echo) that measures of well-being must go beyond objective features, particularly related to economic indicators such as GDP, this is not to say one replaces the other. More practically, subjective and objective approaches will covary to some degree but remain largely distinct. For example, GDP presents a useful composite of a substantial number of dimensions, such as consumption, imports, exports, specific market outcomes, and incomes. If measurement is restricted to a macro-level indicator such as GDP, we cannot be confident in selecting appropriate policies to implement. Policies are most effective when they target a specific component (of GDP, in this instance), and then are directly evaluated in terms of changes in that component. The composite can then be useful for comprehensive understanding of change over time and variation in circumstances. Specific dimensions are necessary for identifying strengths and weaknesses to guide policy, and examining direct impacts on those dimensions. In this way, a composite measure in the form of MPWB for aggregate well-being is also useful, so long as the individual dimensions are used in the development and evaluation of policies. Similar arguments for other multidimensional constructs have been made recently, such as national indexes of ageing [ 7 ].

In the specific instance of MPWB in relation to existing measures of well-being, there are several critical reasons to ensure a robust approach to measurement through systematic validation of psychometric properties. The first is that these measures are already part of the ESS, meaning they are being used to study a very large sample across a number of social challenges and not specifically a new measure for well-being. The ESS has a significant influence on policy discussions, which means the best approaches to utilizing the data are critical to present systematically, as we have attempted to do here. This approach goes beyond existing measures such as Gallup or the World Happiness Index to broadly cover psychological well-being, not individual features such as happiness or life satisfaction (though we reiterate: as we demonstrate in Fig.  7 a and b, these individual measures can and should still covary broadly with any multidimensional measure of well-being, even if not useful for predicting all dimensions). While often referred to as ‘comprehensive’ measurement, this merely describes a broad range of dimensions, though more items for each dimension – and potentially more dimensions – would certainly be preferable in an ideal scenario.

These dimensions were identified following extensive study for flourishing measures by Huppert & So [ 27 ], meaning they are not simply a mix of dimensions, but established systematically as the key features of well-being (the opposite of ill-being). Furthermore, the development of the items is in line with widely validated and practiced measures for the identification of illness. The primary adjustment has simply been the emphasis on health, but otherwise maintains the same principles of assessment. Therefore, the overall approach offers greater value than assessing only negative features and inferring absence equates to opposite (positives), or that individual measures such as happiness can sufficiently represent a multidimensional construct like well-being. Collectively, we feel the approach presented in this work is therefore a preferable method for assessing well-being, particularly on a population level, and similar approaches should replace single items used in isolation.

While the focus of this paper is on the utilization of a widely tested measure (in terms of geographic spread) that provides for assessing population well-being, it is important to provide a specific application for why this is relevant in a policy context. Additionally, because the ESS itself is a widely-recognized source of meaningful information for policymakers, providing a robust and comprehensive exploration of the data is necessary. As the well-being module was not collected in recent rounds, these insights provide clear reasoning and applications for bringing them back in the near future.

More specifically, it is critical that this approach be seen as advantageous both in using the composite measure for identifying major patterns within and between populations, and for systematically unpacking individual dimensions. Using those dimensions produces nuanced insights as well as the possibility of illuminating policy priorities for intervention.

In line with this, we argue that no composite measure can be useful for developing, implementing, or evaluating policy if individual dimensions are not disaggregated. We are not arguing that MPWB as a single composite score, nor the additional measures used in ESS, is better than other existing single composite scoring measures of well-being. Our primary argument is instead that MPWB is constructed and analyzed specifically for the purpose of having a robust measure suitable for disaggregating critical dimensions of well-being. Without such disaggregation, single composite measures are of limited use. In other words, construct a composite and target the components.

Well-being is perhaps the most critical outcome measure of policies. Each individual dimension of well-being as measured in this study represents a component linked to important areas of life, such as physical health, financial choice, and academic performance [ 26 ]. For such significant datasets as the European Social Survey, the use of the single score based on the ten dimensions included in multidimensional psychological well-being gives the ability to present national patterns and major demographic categories as well as to explore specific dimensions within specific groups. This offers a robust approach for policy purposes, on both macro and micro levels. This facilitates the implementation and evaluation of interventions aimed at directly improving outcomes in terms of population well-being.

Availability of data and materials

The datasets analysed during the current study are available in the European Social Survey repository, http://www.europeansocialsurvey.org/data/country_index.html

Abbreviations

Diagnostic and Statistical Manual of Mental Disorders

European Social Survey

Gross Domestic Product

International Classification of Disease

Multidimensional psychological well-being

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Acknowledgements

The authors would like to thank Ms. Sara Plakolm, Ms. Amel Benzerga, and Ms. Jill Hurson for assistance in proofing the final draft. We would also like to acknowledge the general involvement of the Centre for Comparative Social Surveys at City University, London, and the Centre for Wellbeing at the New Economics Foundation.

This work was supported by a grant from the UK Economic and Social Research Council (ES/LO14629/1). Additional support was also provided by the Isaac Newton Trust, Trinity College, University of Cambridge, and the UK Economic and Social Research Council (ES/P010962/1).

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KR is the lead author and researcher on the study, responsible for all materials start to finish. FH was responsible for the original grant award and the general theory involved in the measurement approaches. ÁM was responsible for broad analysis and writing. EGG was responsible for psychometric models and the original factor scoring approach, plus writing the supplementary explanations. SM provided input on later drafts of the manuscript as well as the auxiliary analyses. The authors read and approved the final manuscript.

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Additional file 1: figure s1.

. Hierarchical approach to modelling comprehensive psychological well-being. Table S1 . Confirmatory Factor Structure for Round 6 and 3. Figure S2 . Well-being by country and gender. Figure S3 . Well-being by country and age. Figure S4 . Well-being by country and employment. Figure S5 . Well-being by country and education. Table S2 . Item loadings for Belgium to Great Britain. Table S3 . Item loadings for Ireland to Ukraine.

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Ruggeri, K., Garcia-Garzon, E., Maguire, Á. et al. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries. Health Qual Life Outcomes 18 , 192 (2020). https://doi.org/10.1186/s12955-020-01423-y

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Exploring constructs of well-being, happiness and quality of life

Oleg n. medvedev.

1 School of Medicine, University of Auckland, Auckland, New Zealand

C. Erik Landhuis

2 School of Social Sciences and Public Policy, Auckland University of Technology, Auckland, New Zealand

Associated Data

The following information was supplied regarding data availability:

The raw data are provided in the Supplemental File .

Existing definitions of happiness, subjective well-being, and quality of life suggest conceptual overlap between these constructs. This study explored the relationship between these well-being constructs by applying widely used measures with satisfactory psychometric properties.

Materials and Methods

University students ( n = 180) completed widely used well-being measures including the Oxford Happiness Questionnaire (OHQ), the World Health Organization Quality of Life Questionnaire, the Satisfaction with Life Scale, and the Positive and Negative Affect Scale. We analyzed the data using correlation, regression, and exploratory factor analysis.

All included well-being measures demonstrated high loadings on the global well-being construct that explains about 80% of the variance in the OHQ, the psychological domain of Quality of Life and subjective well-being. The results show high positive correlations between happiness, psychological and health domains of quality of life, life satisfaction, and positive affect. Social and environmental domains of quality of life were poor predictors of happiness and subjective well-being after controlling for psychological quality of life.

Together, these data provide support for a global well-being dimension and interchangeable use of terms happiness, subjective well-being, and psychological quality of life with the current sample and measures. Further investigation with larger heterogeneous samples and other well-being measures is warranted.

Introduction

The existing definitions of happiness, subjective well-being, and health related quality of life and the main components assigned to these constructs in the research literature (see Table 1 ) suggest conceptual overlap between these dimensions ( Camfield & Skevington, 2008 ). Quality of life was defined in the cross-cultural project of the World Health Organization (WHO) as:

An individual’s perception of their position in life, in the context of the culture and value systems in which they live, and in relation to their goals, expectations, standards, and concerns. It is a broad ranging concept, affected in a complex way by the person’s physical health, psychological state, level of independence, social relationships and their relationships to salient features of their environment. ( WHOQOL Group, 1995 , p. 1404)

The new reconceptualization of subjective well-being assumed to be synonymous of happiness by Diener (2006 , p. 400) as: “An umbrella term for different valuations that people make regarding their lives, the events happening to them, their bodies and minds, and the circumstances in which they live” resulted in greater theoretical convergence between these constructs. This raises an issue as to the point in which conceptual overlap invites redundancy, and whether one or the other of the terms is now surplus to requirements.

Historically, humans strived to achieve happiness and considered it the most important goal in life ( Compton, 2005 ). Cross-cultural research provide supporting evidence for primacy of happiness compared to other individual values such as physical health, wealth or love ( Kim-Prieto et al., 2005 ; Skevington, MacArthur & Somerset, 1997 ). Essentially, other human goals are valued because they are believed to give rise to happiness ( Csikszentmihaliy, 1992 ). Initially psychology was dealing with mental health issues affecting physical and social functioning of an individual ( Andrews & McKennell, 1980 ; Beck, 1991 , 1993 ). Happiness, well-being, and quality of life have only attracted increased interest of psychologists by the end of the 20th century resulting in growing research in this area ( Diener, 1984 ; WHOQOL Group, 1998a , 1998b ). Happiness and well-being research became increasingly important in the economics’ context ( Kristoffersen, 2010 ), and well-being data are widely used along with economic indicators by economists ( Kahneman & Krueger, 2006 ).

Currently, there is no agreement between researchers in defining happiness and its related constructs ( Diener, 2006 ; Diener et al., 2010 ; Rojas & Veenhoven, 2013 ; Kern et al., 2014 ; Shin & Johnson, 1978 ). In the literature happiness is often called subjective well-being ( Diener, 2006 ; Hills & Argyle, 2002 ), emotional well-being, positive affect ( Brandburn, 1969 ; Fordyce, 1988 ), and quality of life ( Diener, 2000 ; Ratzlaff et al., 2000 ; Shin & Johnson, 1978 ), which suggests that the meanings of happiness may depend on the context ( Diener, 2006 ; Carlquist et al., 2016 ). Elsewhere, subjective happiness was defined as “a global evaluation of life satisfaction” ( Diener, 2006 , p. 400). In the same way, subjective well-being was defined as “evaluations of life quality” ( Andrews & McKennell, 1980 , p. 131). These definitions indicate close relationship between the constructs of happiness, subjective well-being, quality of life, and life satisfaction. More recently subjective well-being was proposed as more appropriate “Big One” including the relevant aspects of global well-being ( Diener, 2006 ; Kashdan, Biswas-Diener & King, 2008 ).

Happiness can be described by bottom-up and top-down processes ( Andrews & McKennell, 1980 ; Diener, 1984 ). The bottom-up approach implies that happiness depends on aggregated positive and negative feelings ( Diener, 1984 ). However, evidence suggests that positive affect is not a counterpart of negative affect and the correlation between them is merely moderate ( Argyle, 2001 ; Tellegen et al., 1988 ). Alternatively, top-down approaches explain happiness is a result of subjective evaluations of individual’s life experiences or satisfaction with life (SL) ( Andrews & McKennell, 1980 ; Diener, Lucas & Oishi, 2005 ). The top-down approach has both theoretical foundation ( Beck, 1993 ; Diener, 1984 ) and empirical support ( Andrews & McKennell, 1980 ; Butler et al., 2006 ). The approaches appear to complement each other because research on happiness assessment consistently indicates that positive and negative affect and a cognitive component or SL map on unidimensional happiness construct ( Argyle, 2001 ; Hills & Argyle, 1998 , 2002 ; Joseph & Lewis, 1998 ). The cognitive component of happiness may involve personality traits such as optimism, extraversion, and internal locus of control ( Fordyce, 1988 ; Mayers, 1992 ).

Shin & Johnson (1978) noted, “happiness has been mistakenly identified with feelings of pleasure” in the research literature and defined as emotional well-being ( Fordyce, 1986 ; Layard, 2005 ). Shin & Johnson (1978) proposed that this definition refers to hedonic happiness associated with feeling happy also called euphoria or elation. They argued that feeling happy and being happy are not the same— being happy refers to enduring condition rather than to momentary pleasures or happy feelings. Accordingly, happiness should be understood as global evaluation of individual’s life quality according to their own criteria, which include both cognitions and emotions ( Shin & Johnson, 1978 ). According to this model feeling happy refers to state happiness and being happy incorporates both state and trait happiness.

The hedonic concept of happiness does not consider that cognitive appraisal plays the important role in emotional functioning ( Frijda, 1998 , 2007 ). According to the dual route model of emotional processing proposed by LeDoux (2000) , triggering information is simultaneously sent to the amygdala, resulting in immediate physiological responses like “fight or flight” ( Cannon, 1929 ), and to prefrontal cortex for further cognitive appraisal. Evidence shows that activation of the amygdala could be inhibited by prefrontal brain structures involved in conscious cognition ( Thayer et al., 2009 ; Thayer & Lane, 2000 ). Also, the impact of cognition on emotional states is well supported by evidence-based cognitive therapy ( Butler et al., 2006 ; Ellis, 2002 ). Therefore, the definition of happiness as merely emotional well-being is limited, because it does not account for the cognitive component of happiness supported by both theories and empirical evidence ( Diener et al., 1999 ; Eid & Larsen, 2008 ; Frijda, 2007 ).

Rather than constructing happiness as merely emotional well-being, Ryff (1989) and Ryff & Keyes (1995) proposed a eudemonic model of happiness, which they also called psychological well-being or positive functioning, comprising six dimensions: purpose in life; personal growth; environmental mastery; autonomy; positive self-regard; and social connections. These dimensions do not include basic components of subjective well-being and happiness such as emotions and life satisfaction consistently supported by the literature ( Helliwell, Huang & Wang, 2014 ; Diener, Sapyta & Suh, 1998 ; Rojas & Veenhoven, 2013 ). The construct validity of the assessment instrument based on these six factors ( Ryff, 1989 ) was challenged by later investigation indicating substantial overlap between dimensions ( Springer & Hauser, 2006 ; Springer, Hauser & Freese, 2006 ).

Ryff’s (1989) model of eudemonic happiness was also scrutinized by Diener et al. (2010) , resulting in development of an alternative construct defined as “psychological flourishing” or an individual’s self-perceived success, which is an aspect of life satisfaction. The proposed construct emphasizes positive functioning and covers dimensions such as social relationships; purposeful life; engagement in activities; self-esteem; and optimism, which overlap with components of widely used quality of life and happiness measures ( WHOQOL Group, 1998a ; Hills & Argyle, 2002 ). For instance, social relationships is a domain of the quality of life measure ( WHOQOL Group, 1998a ) and self-esteem and optimism are components of the widely used happiness measure ( Hills & Argyle, 2002 ). The component “purposeful life” implies that one cannot be happy without having a purpose making happiness an exclusive attribute of a group of adults who managed to develop such a purpose. Including this component in a psychometric measure may violate fundamental measurement principle of invariance across population groups ( Thurstone, 1931 ) because the sense of purpose in life varies substantially across cultural and age groups ( Oishi & Diener, 2014 ). Notwithstanding the importance of eudemonic well-being associated with individual’s fulfilment, it is implicitly included in subjective well-being and reflected by the overall SL ( Diener et al., 1999 ; Eid & Larsen, 2008 ; Kashdan, Biswas-Diener & King, 2008 ).

Different measures were developed to assess well-being associated constructs, however, definitions used in these instruments appear inconsistent ( Diener et al., 1999 ; Diener et al., 2010 ; Fordyce, 1986 ; Joseph & Lewis, 1998 ). Also, one or two items often used to measure well-being or happiness in national and cross-cultural surveys appeared unreliable compared to measures with more items covering various well-being components ( Andrews & McKennell, 1980 ; Hills & Argyle, 2002 ; Joseph & Lewis, 1998 ).

Hills & Argyle (2002) considered limitations of earlier happiness measurements when developing their Oxford Happiness Questionnaire (OHQ). The authors used the terms “well-being” and “subjective well-being” as synonyms for “happiness” when describing the OHQ. This instrument is a new version of the Oxford Happiness Inventory ( Argyle, 2001 ) and both scales were widely used in Oxford University for assessment of personal happiness and are shown to have satisfactory psychometric properties ( Hills & Argyle, 2002 ). The OHQ is a unidimensional scale that contains items tapping into positive and negative affect, life satisfaction and happy traits such as sense of control, physical fitness, positive cognition, mental alertness, self-esteem, cheerfulness, optimism, and empathy ( Diener, 1984 ; Hills & Argyle, 2002 ).

Quality of Life was widely recognized as a health related issue associated with the WHO’s definition of health been not only the absence of disease but a complete mental, social, and physical well-being ( WHOQOL Group, 1995 , p. 1404). The short-form version of the World Health Organization’s Quality of Life measurement tools (WHOQOL-BREF) is a 26-item questionnaire that assesses quality of life on physical, psychological, social, and environmental domains ( WHOQOL Group, 1998a ).

The WHO definition above supports an emerging consensus that QOL is a multidimensional construct conceptualized as separate domains and sub-domains relating to all areas of life ( Skevington, 2002 ; WHOQOL Group, 1995 ).

In psychology many variables of interest cannot be measured directly, and as latent constructs the establishment of their properties remains an ongoing challenge. By using accurate operational definitions a construct’s properties can be evaluated, but reliable and valid measurements can be obtained only when the operational definitions themselves have been rigorously developed ( Aiken & Groth-Marnat, 2006 ). Happiness, subjective well-being, and quality of life are concepts that share common components ( Table 1 ) and arguably, lack standardized operational definitions or criteria. This lack is evident in the interchangeable use of these terms in the research literature ( Andrews & McKennell, 1980 ; Diener et al., 1999 ; Fordyce, 1986 ; Shin & Johnson, 1978 ). The aim of the current study is to clarify relationships between these constructs empirically by applying widely used and well-validated measures of well-being including the OHQ, the World Health Organization Quality of Life Questionnaire (WHOQOL-BREF), the SL Scale, and the Positive and Negative Affect Scale (PANAS).

Participants

The Auckland University of Technology Ethics Committee granted ethical approved for this study (Ethics Application Number 11/209). New Zealand university students ( n = 180) recruited in class completed the study questionnaire; from them 35 were males (19.9%), 141 were females (80.1%) and four participants did not provide gender information. We have conducted power analysis to estimate a minimum sample size required for the correlational study with α (two tailed) = 0.05, β = 0.20, and r ≥ 0.25, which is n = 123 and our sample size is greater. The sample size also satisfied 20 participants per item criteria for principle component analysis with eight study variables ( Hair et al., 1995 ). The participants age ranges from 18 to 55 years, mean age is 24.6 years and standard deviation (SD) is 7.28. About 66 participants (36.7%) identified themselves as New Zealand European, 34 (18.9%) as Asian, 24 (13.7%) as Pasifika, 14 (7.8%) as other European, 7 (3.9%) as Maori, 30 (16.7%) as other ethnicities, and five participants did not indicate their ethnicity. These data has been previously used as a part of psychometric investigation that applied Rasch analysis to evaluate psychometric properties of the OHQ ( Medvedev et al., 2016 ), which is unrelated to the purpose of the current study.

Instruments

Oxford Happiness Questionnaire ( Hills & Argyle, 2002 ) includes 29 items using six-point Likert scale response format. WHOQOL-BREF quality of life questionnaire ( WHOQOL Group, 1998a ) includes 26 items with five-point Likert scale response format representing four different domains. The SL scale contains five items presented in seven-point Likert scale format ( Diener et al., 1985 ). The PANAS ( Watson, Clark & Tellegen, 1988 ) includes two subscales measuring positive and negative affect independently. Each scale is composed of 10 adjectives expressing different feelings and emotions like “excited,” “interested” or “distressed” and participants indicate the correspondence of their average feeling to each provided adjective on a five-point Likert scale from “not at all or very slightly” = 1 to “extremely” = 5. The composite subjective well-being scale (SWS) was calculated as a mean of z -scores for the SL scale ( Diener et al., 1985 ), the PANAS positive affect subscale and the reversed coded PANAS negative affect subscale ( Watson, Clark & Tellegen, 1988 ). Therefore, the SWS combines positive and negative affect and life satisfaction, which are the main components of subjective well-being suggested by the literature ( Table 1 ).

The study questionnaires were completed by the participants in the lecture theaters of the Auckland University of Technology before lecture. The study complied with local ethical guidelines.

Data analyses

The data analysis was performed using IBM SPSS program, version 24. The data was screened for normality of distribution and for meeting assumptions for correlation, regression, and principle component analysis. We computed descriptive statistics and examined internal consistency (Cronbach’s alpha and item-to-total correlations) for all included measures with the current dataset. Correlation and regression analyses were conducted to explore the relationships between study variables and the extent to which quality of life domains predict happiness and subjective well-being. Principle component analysis was used to examine communalities and loadings on the first principle component for all study variables.

Psychometric properties of the measures

Psychometric properties of the applied scales were tested with our data set. The inter-item total correlation for all the scales were in the permissible range from 0.3 to 0.75 with an exception of the item 2 in OHQ, which correlates with other items at about 0.12. Means, SD, and reliability coefficients for each scale including the OHQ, the WHOQOL-BREF domain scales, the SL and the PANAS positive and negative affect are summarized in Table 2 . The majority of the scales have reliability coefficients over 0.8 with the exception of social and environmental domain scales of WHOQOL falling below this number.

Correlational analysis

Correlations between the outcome variables, gender, and age are represented in Table 3 . The results show that neither gender nor age correlates significantly with any of the scales. The correlations between all well-being related measures are significant and range from moderate to strong.

Oxford happiness is the Oxford Happiness Questionnaire ( Hills & Argyle, 2002 ); QOL is quality of life, QOL general is the general question about quality of life and QOL social, QOL psychological, QOL environment, QOL health are the four domain scales of WHOQOL ( WHOQOL Group, 1998a ); Life satisfaction is the Satisfaction with Life scale ( Diener et al., 1985 ); Positive affect and Negative affect are PANAS subscales measuring positive and negative affect respectively ( Watson, Clark & Tellegen, 1988 ); Subjective well-being is the composite scale of subjective well-being combining the Satisfaction with Life and the PANAS Positive and reversed Negative affect scales.

Multiple regression analysis

The data satisfied assumptions of multiple regression analysis with skewness and kurtosis values within ±1, no significant outliers, no signs of multicolinearity and variance inflating factor below 5. Multiple linear regression analysis was performed to test regression weights of WHOQOL domains and their significance in predicting happiness as measured by OHQ and subjective well-being measured by the SWS composite measure. Table 4 shows that the WHOQOL domains together explain 73% of happiness on the OHQ. It also shows that the strongest predictor is psychological domain of WHOQOL and environmental factors appear not significant in predicting happiness. All the WHOQOL domains appear significant and together explain about 66% of subjective well-being with psychological domain as the strongest predictor ( Table 4 ).

Happiness is measured by the Oxford Happiness Questionnaire.

R , multiple regression coefficient.

Principle component analysis

Principle component analysis was first conducted for all applied scales aiming to extract communality of each scale. Extracted communalities and loadings on the single factor for all scales together with total variance explained by scales and total eigenvalue are represented in Table 5 . The extracted communalities of the scales range from 0.38 (social relationships) to 0.83 (the OHQ and the psychological domain of WHOQOL).

Scree plot ( Fig. 1 ) shows the sharp drop and clear Cattell’s cut off point (elbow) after the first principal component and the rest of the plot representing other extracted components is shallow and almost flat.

Alternatively, the SL and the PANAS scales were replaced by the SWS, which followed the same analysis illustrated in Table 6 . The extracted communalities range between 0.42 (social relationships) and 0.81 (the OHQ and the psychological domain of WHOQOL).

The aim of this study was to investigate the relationship between happiness, subjective well-being, quality of life, and related components by applying widely used scales with satisfactory psychometric properties. Our data offer preliminary clarification of the relationship between happiness, subjective well-being, and quality of life. The results show that all applied well-being measures have high loadings on the global well-being domain that explains about 80% of the variance in the OHQ, the psychological domain of Quality of Life and subjective well-being ( Tables 5 and ​ and6). 6 ). These findings support the proposed global dimension of well-being that transcends relative distinctions between specific components contributing to the overall wellness ( Kashdan, Biswas-Diener & King, 2008 ; Hills & Argyle, 1998 , 2002 ; Joseph & Lewis, 1998 ). These results also provide support for interchangeable use of happiness and subjective well-being, and suggest that these constructs and quality of life domains may be considered as facets of the global well-being construct.

Widely used well-being measures capture subjective evaluation of individual’s condition and top-down approach suggests that people are happier if they evaluate their life including its eudemonic aspects in a positive way ( Diener et al., 1999 ; Diener, 1984 ). In contrast, negative evaluations diminish well-being, may discount eudemonic components and lead to psychological conditions such as depression or anxiety ( Diener et al., 1999 ; Beck, 1991 , 1993 ). Therefore, moderate to strong correlations found between subjective well-being measures in this study were expected according to this approach. The strongest relationship is evident between the OHQ, psychological domain of the WHOQOL, the SL, the PANAS Positive affect, and the SWS ( Table 3 ). It is likely that correlation values were suppressed due to unrepresentative student sample with substantial proportion of international students for whom English is a second language, which might have produced a response bias. Also, the correlation values could suffer from inconsistent item wording in different scales. For example, the WHOQOL items ask the participants to evaluate their affective experiences for the last two weeks ( WHOQOL Group, 1998a ), whereas the PANAS measures how the participants feel on average ( Watson, Clark & Tellegen, 1988 ). Thus, the correlations could be higher if uniform wording was used for all scales and applied to a larger sample more representative of the general population.

Furthermore, the results show that the psychological, physical health, social, and environmental domains of WHOQOL together explain 73% of happiness measured by the OHQ and 66% of subjective well-being. In both cases psychological domain was the strongest predictor, but environmental factors explain only 14% of the variance in subjective well-being and were found not significant in predicting happiness. These data suggest that environment does not appear as relevant determinant of individual happiness. However, psychological domain appeared as strongest predictor of both happiness and subjective well-being in contrast to both environment and social relationships. The social relationships explain the least amount of variance in the global well-being construct indicating that they may play important but not the major role in individual’s well-being of the current sample. These results are consistent with earlier studies supporting top-down approach and emphasizing the role of individual’s cognition in subjective happiness ( Andrews & McKennell, 1980 ; Andrews & Withey, 1976 ; Butler et al., 2006 ).

The main problem to address redundancy issue is the proposed multidimensional structure of WHOQOL in which the four domains are typically assessed independently without providing a combined quality of life score ( WHOQOL Group, 1998b ). However, our results suggest that the psychological domain of WHOQOL can be used as an alternative brief measure of happiness or subjective well-being, which is an advantage, because it is a six-item scale with good reliability compared to the 29-item OHQ.

Tested with our data set, satisfactory psychometric properties of all scales used in the study appeared consistent with earlier research ( Hills & Argyle, 2002 ; Diener et al., 1985 ; Watson, Clark & Tellegen, 1988 ; WHOQOL Group, 1998a ) with the exception of item 2 in the OHQ, which correlates with other items at 0.12, below commonly acceptable level of 0.3. Thus, discarding this item would slightly increase reliability of the OHQ, which is recommended for future application of this scale. However, it is unlikely that this item could have strong influence on overall sufficiently high reliability of the scale (α = 0.90).

Limitations

The common limitations of subjective well-being research refer to participants’ transient mood states and other contextual influences, which might affect participants’ responses ( Eid & Larsen, 2008 ). However, these effects were minimized because the data were collected in different classes. The other limitation of this study refers to the modest sample size and disproportionally larger number of female participants (80.1%) comparing to male (19.9%), which limits generalization of the findings to the male population. In this study we used all measures in their original form without enhancement of their psychometric properties to maintain consistency with studies conducted earlier. Recently proposed modification of happiness and quality of life assessment tools ( Medvedev et al., 2016 ; Krägeloh et al., 2016 ) may contribute to more accurate estimations of relationships between these happiness and well-been measures. However, this would require similar psychometric enhancements of all other measures (e.g., PANAS) involved in the analyses, which are not available to date.

Future Directions

Further research should investigate the relationship between happiness, subjective well-being, and quality of life among more diverse populations, including people differing in socio-economic status and health conditions, as these dimensions have proved to be crucial in the assessment of well-being under its different formalizations. Finally, the research should focus on development of more accurate instruments for assessment of happiness and subjective well-being by considering the WHOQOL domains and other relevant measures.

Conclusions

Taken together, the findings of this study provide support for a global well-being dimension and interchangeable use of terms happiness, subjective well-being, and psychological quality of life with the current sample and measures. The WHOQOL measures happiness or subjective well-being by its psychological domain but in addition includes subscales focused on measurement of perceived physical health and more externally oriented domains such as social relationships and environmental factors. These differences should be considered in measurement definitions to refine reliability and validity. The findings of this study contribute to better understanding of the relationships between happiness, subjective well-being, and quality of life, which is necessary for more accurate assessment of these constructs. Also, these findings have implications for the enhancement of people’s well-being, happiness, and quality of life through development of contentment and emotional stability. Further investigation with larger heterogeneous samples and other well-being measures is warranted.

Supplemental Information

Supplemental information 1, funding statement.

The authors received no funding for this work.

Additional Information and Declarations

The authors declare that they have no competing interests.

Oleg N. Medvedev conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft, obtained ethics approval.

C. Erik Landhuis conceived and designed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.

The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

The Auckland University of Technology Ethics Committee granted ethical approval for this study (Ethics Application Number 11/209).

ORIGINAL RESEARCH article

The art of happiness: an explorative study of a contemplative program for subjective well-being.

• 1 Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
• 2 Department of Psychology, Sapienza University of Rome, Rome, Italy
• 3 Institute Lama Tzong Khapa, Pisa, Italy

In recent decades, psychological research on the effects of mindfulness-based interventions has greatly developed and demonstrated a range of beneficial outcomes in a variety of populations and contexts. Yet, the question of how to foster subjective well-being and happiness remains open. Here, we assessed the effectiveness of an integrated mental training program The Art of Happiness on psychological well-being in a general population. The mental training program was designed to help practitioners develop new ways to nurture their own happiness. This was achieved by seven modules aimed at cultivating positive cognition strategies and behaviors using both formal (i.e., lectures, meditations) and informal practices (i.e., open discussions). The program was conducted over a period of 9 months, also comprising two retreats, one in the middle and one at the end of the course. By using a set of established psychometric tools, we assessed the effects of such a mental training program on several psychological well-being dimensions, taking into account both the longitudinal effects of the course and the short-term effects arising from the intensive retreat experiences. The results showed that several psychological well-being measures gradually increased within participants from the beginning to the end of the course. This was especially true for life satisfaction, self-awareness, and emotional regulation, highlighting both short-term and longitudinal effects of the program. In conclusion, these findings suggest the potential of the mental training program, such as The Art of Happiness , for psychological well-being.

Introduction

People desire many valuable things in their life, but—more than anything else—they want happiness ( Diener, 2000 ). The sense of happiness has been conceptualized as people's experienced well-being in both thoughts and feelings ( Diener, 2000 ; Kahneman and Krueger, 2006 ). Indeed, research on well-being suggests that the resources valued by society, such as mental health ( Koivumaa-Honkanen et al., 2004 ) and a long life ( Danner et al., 2001 ), associate with high happiness levels. Since the earliest studies, subjective well-being has been defined as the way in which individuals experience the quality of their life in three different but interrelated mental aspects: infrequent negative affect, frequent positive affect, and cognitive evaluations of life satisfaction in various domains (physical health, relationships, and work) ( Diener, 1984 , 1994 , 2000 ; Argyle et al., 1999 ; Diener et al., 1999 ; Lyubomksky et al., 2005 ; Pressman and Cohen, 2005 ). A growing body of research has been carried out aimed at identifying the factors that affect happiness, operationalized as subjective well-being. In particular, the construct of happiness is mainly studied within the research fields of positive psychology or contemplative practices, which are grounded in ancient wisdom traditions. Positive psychology has been defined as the “the scientific study of human strengths and virtues” ( Sheldon and King, 2001 ), and it can be traced back to the reflections of Aristotle about different perspectives on well-being ( Ryan and Deci, 2001 ). On the other end, contemplative practices include a great variety of mental exercises, such as mindfulness, which has been conceived as a form of awareness that emerges from experiencing the present moment without judging those experiences ( Kabat-Zinn, 2003 ; Bishop et al., 2004 ). Most of these exercises stem from different Buddhist contemplative traditions such as Vipassana and Mahayana ( Kornfield, 2012 ). Notably, both perspective share the idea of overcoming suffering and achieving happiness ( Seligman, 2002 ). Particularly, Buddhism supports “the cultivation of happiness, genuine inner transformation, deliberately selecting and focusing on positive mental states” ( Lama and Cutler, 2008 ). In addition, mindfulness has been shown to be positively related to happiness ( Shultz and Ryan, 2015 ), contributing to eudemonic and hedonic well-being ( Howell et al., 2011 ).

In fact, although the definition of happiness has a long history and goes back to philosophical arguments and the search for practical wisdom, in modern times, happiness has been equated with hedonism. It relies on the achievement of immediate pleasure, on the absence of negative affect, and on a high degree of satisfaction with one's life ( Argyle et al., 1999 ). Nonetheless, scholars now argue that authentic subjective well-being goes beyond this limited view and support an interpretation of happiness as a eudemonic endeavor ( Ryff, 1989 ; Keyes, 2006 ; Seligman, 2011 ; Hone et al., 2014 ). Within this view, individuals seem to focus more on optimal psychological functioning, living a deeply satisfying life and actualizing their own potential, personal growth, and a sense of autonomy ( Deci and Ryan, 2008 ; Ryff, 2013 ; Vazquez and Hervas, 2013 ; Ivtzan et al., 2016 ). In psychology, such a view finds one of its primary supports in Maslow's (1981) theory of human motivation. Maslow argued that experience of a higher degree of satisfaction derives from a more wholesome life conduct. In Maslow's hierarchy of needs theory, once lower and more localized needs are satisfied, the unlimited gratification of needs at the highest level brings people to a full and deep experience of happiness ( Inglehart et al., 2008 ). Consequently, today, several scholars argue that high levels of subjective well-being depend on a multi-dimensional perspective, which encompasses both hedonic and eudemonic components ( Huta and Ryan, 2010 ; Ryff and Boylan, 2016 ). Under a wider perspective, the process of developing well-being reflects the notion that mental health and good functioning are more than a lack of illness ( Keyes, 2005 ). This approach is especially evident if we consider that even the definition of mental health has been re-defined by the World Health Organization (1948) , which conceives health not merely as the absence of illness, but as a whole state of biological, psychological, and social well-being.

To date, evidence exists suggesting that happiness is, in some extent, modulable and trainable. Thus, simple cognitive and behavioral strategies that individuals choose in their lives could enhance happiness ( Lyubomirsky et al., 2005 ; Sin and Lyubomirsky, 2009 ). In the history of psychology, a multitude of clinical treatments have been applied to minimize the symptoms of a variety of conditions that might hamper people from being happy, such as anger, anxiety, and depression (for instance, see Forman et al., 2007 ; Spinhoven et al., 2017 ). In parallel with this view, an alternative—and less developed—perspective found in psychology focuses on the scientific study of individual experiences and positive traits, not for clinical ends, but instead for personal well-being and flourishing (e.g., Fredrickson and Losada, 2005 ; Sin and Lyubomirsky, 2009 ). Yet, the question of exactly how to foster subjective well-being and happiness, given its complexity and importance, remains open to research. Answering this question is of course of pivotal importance, both individually and at the societal level. Positive Psychology Interventions encompass simple, self-administered cognitive behavioral strategies intended to reflect the beliefs and behaviors of individuals and, in response to that, to increase the happiness of the people practicing them ( Sin and Lyubomirsky, 2009 ; Hone et al., 2015 ). Specifically, a series of comprehensive psychological programs to boost happiness exist, such as Fordyce's program ( Fordyce, 1977 ), Well-Being Therapy ( Fava, 1999 ), and Quality of Life Therapy ( Frisch, 2006 ). Similarly, a variety of meditation-based programs aim to develop mindfulness and emotional regulatory skills ( Carmody and Baer, 2008 ; Fredrickson et al., 2008 ; Weytens et al., 2014 ), such as Mindfulness-Based Stress Reduction (MBSR; Kabat-Zinn, 1990 ) and Mindfulness-Based Cognitive Therapy (MBCT; Teasdale et al., 2000 ). Far from being a mere trend ( De Pisapia and Grecucci, 2017 ), those mindfulness-based interventions have been shown to lead to increased well-being ( Baer et al., 2006 ; Keng et al., 2011 ; Choi et al., 2012 ; Coo and Salanova, 2018 ; Lambert et al., 2019 ) in several domains, such as cognition, consciousness, self, and affective processing ( Raffone and Srinivasan, 2017 ). Typically, mindfulness programs consist of informal and formal practice that educate attention and develop one's capacity to respond to unpredicted and/or negative thoughts and experiences ( Segal and Teasdale, 2002 ). In this context, individuals are gradually introduced to meditation practices, focusing first on the body and their own breath, and later on thoughts and mental states. The effects of these programs encompass positive emotions and reappraisal ( Fredrickson et al., 2008 ; Grecucci et al., 2015 ; Calabrese and Raffone, 2017 ) and satisfaction in life ( Fredrickson et al., 2008 ; Kong et al., 2014 ) and are related to a reduction of emotional reactivity to negative affect, stress ( Arch and Craske, 2006 ; Jha et al., 2017 ), and aggressive behavior ( Fix and Fix, 2013 ). All these effects mediate the relationship between meditation frequency and happiness ( Campos et al., 2016 ). This allows positive psychology interventions to improve subjective well-being and happiness and also reduce depressive symptoms and negative affect along with other psychopathologies ( Seligman, 2002 ; Quoidbach et al., 2015 ). Engaging in mindfulness might enhance in participants the awareness of what is valuable to them ( Shultz and Ryan, 2015 ). This aspect has been related to the growth of self-efficacy and autonomous functioning and is attributable to an enhancement in eudemonic well-being ( Deci and Ryan, 1980 ). Moreover, being aware of the present moment provides a clearer vision of the existing experience, which in turn has been associated with increases in hedonic well-being ( Coo and Salanova, 2018 ). Following these approaches, recent research provides evidence that trainings that encompass both hedonic and eudemonic well-being are correlated with tangible improved health outcomes ( Sin and Lyubomirsky, 2009 ).

Although there is a consistent interest in scientific research on the general topic of happiness, such studies present several limitations. Firstly, most of the research has focused on clinical studies to assess the effectiveness of happiness-based interventions—in line with more traditional psychological research, which is primarily concerned with the study of mental disorders ( Garland et al., 2015 , 2017 ; Groves, 2016 ). Secondly, most of the existing interventions are narrowly focused on the observation of single dimensions (i.e., expressing gratitude or developing emotional regulation skills) ( Boehm et al., 2011 ; Weytens et al., 2014 ). Moreover, typically studies involve brief 1- to 2-week interventions ( Gander et al., 2016 ), in contrast with the view that eudemonia is related to deep and long-lasting aspects of one's personal lifestyle. Furthermore, while the effectiveness of mindfulness-based therapies is well-documented, research that investigates the effects of mindfulness retreats has been lacking, which are characterized by the involvement of more intense practice from days to even years [for meta-analysis and review, see Khoury et al. (2017) , McClintock et al. (2019) , Howarth et al. (2019) ].

In this article, we report the effects on subjective well-being of an integrated mental training program called The Art of Happiness , which was developed and taught by two of the authors (CM for the core course subject matter and NDP for the scientific presentations). The course lasted 9 months and included three different modules (see Methods and Supplementary Material for all details), namely, seven weekends (from Friday evening to Sunday afternoon) dedicated to a wide range of specific topics, two 5-day long retreats, and several free activities at home during the entire period. The course was designed to help practitioners develop new ways to nurture their own happiness, cultivating both self-awareness and their openness to others, thereby fostering their own emotional and social well-being. The basic idea was to let students discover how the union of ancient wisdom and spiritual practices with scientific discoveries from current neuropsychological research can be applied beneficially to their daily lives. This approach and mental training program was inspired by a book of the Fourteenth Dalai Lama Tenzin Gyatso and the psychiatrist Lama and Cutler (2008) . The program rests on the principle that happiness is inextricably linked to the development of inner equilibrium, a kinder and more open perspective of self, others, and the world, with a key role given to several types of meditation practices. Additionally, happiness is viewed as linked to a conceptual understanding of the human mind and brain, as well as their limitations and potentiality, in the light of the most recent scientific discoveries. To this end, several scientific topics and discoveries from neuropsychology were addressed in the program, with a particular focus on cognitive, affective, and social neuroscience. Topics were taught and discussed with language suitable for the general public, in line with several recent books (e.g., Hanson and Mendius, 2011 ; Dorjee, 2013 ; Goleman and Davidson, 2017 ). The aim of this study was to examine how several psychological measures, related to psychological well-being, changed among participants in parallel with course attendance and meditation practices. Given the abovementioned results of the positive effects on well-being ( Baer et al., 2006 ; Fredrickson et al., 2008 ; Keng et al., 2011 ; Choi et al., 2012 ; Kong et al., 2014 ; Coo and Salanova, 2018 ; Lambert et al., 2019 ), we predicted to find a significant increase in the dimensions of life satisfaction, control of anger, and mindfulness abilities. Conversely, we expected to observe a reduction of negative emotions and mental states ( Arch and Craske, 2006 ; Fix and Fix, 2013 ; Jha et al., 2017 )—i.e., stress, anxiety and anger. Moreover, our aim was to explore how those measures changed during the course of the mental training program, considering not only the general effects of the course (longitudinal effects) but also specific effects within each retreat (short-term effects). Our expectation for this study was therefore that the retreats would have had an effect on the psychological dimensions of well-being linked to the emotional states of our participants, while the whole course would have had a greater effect on the traits related to well-being. The conceptual distinction between states and traits was initially introduced in regard to anxiety by Cattell and Scheier (1961) , and then subsequently further elaborated by Spielberger et al. (1983) . When considering a mental construct (e.g., anxiety or anger), we refer to trait as a relatively stable feature, a general behavioral attitude, which reflects the way in which a person tends to perceive stimuli and environmental situations in the long term ( Spielberger et al., 1983 ; Spielberger, 2010 ). For example, subjects with high trait anxiety have indeed anxiety as a habitual way of responding to stimuli and situations. The state, on the other hand, can be defined as a temporary phase within the emotional continuum, which, for example, in anxiety is expressed through a subjective sensation of tension, apprehension, and nervousness, and is associated with activation of the autonomic nervous system in the short term ( Spielberger et al., 1983 ; Saviola et al., 2020 ). Here, in the adopted tests and analyses, we keep the two time scales separated, and we investigate the results with the aim of understanding the effects of the program on states and traits of different emotional and well-being measures. As a first effect of the course, we expect that the retreats affect mostly psychological states (as measured in the comparison of psychological variables between start and end of each retreat), whereas the full course is predicted to affect mainly psychological traits (as measured in the comparison of the psychological variables between start, middle, and end of the entire 9-month period).

Materials and Methods

Participants.

The participants in the mental training program and in the related research were recruited from the Institute Lama Tzong Khapa (Pomaia, Italy) in a 9-month longitudinal study (seven modules and two retreats) on the effects of a program called The Art of Happiness (see Supplementary Material for full details of the program). Twenty-nine participants followed the entire program (there were nine dropouts after the first module). Their mean age was 52.86 years (range = 39–66; SD = 7.61); 72% were female. Participants described themselves as Caucasian, reaching a medium-high scholarly level with 59% of the participants holding an academic degree and 41% holding a high school degree. The participants were not randomly selected, as they were volunteers in the program. Most of them had no serious prior experience of meditation, only basic experience consisting of personal readings or watching video courses on the web, which overall we considered of no impact to the study. The only exclusion criteria were absence of a history of psychiatric or neurological disease, and not being currently on psychoactive medications. The study was approved by the Ethics Committee of the Sapienza University of Rome, and all participants gave written informed consent. The participants did not receive any compensation for participation in the study.

The overall effectiveness of the 9-month training was examined using a within-subjects design, with perceived stress, mindfulness abilities, etc. (Time: pre–mid–end) as the dependent variable. The effectiveness of the retreats was examined using a 2 × 2 factor within-subjects design (condition: pre vs. post; retreat: 1 vs. 2), with the same dependent variables. The specific contemplative techniques that were applied in the program are described in the Supplementary Material , the procedure is described in the Procedure section, and the measurements are described in the Materials section.

Mental Training Program

The program was developed and offered at the Institute Lama Tzong Khapa (Pomaia, Italy). It was one of several courses that are part of the Institute's ongoing programs under the umbrella of “Secular Ethics and Universal Values.” These various programs provide participants with opportunities to discover how the interaction of ancient wisdom and spiritual practices with contemporary knowledge from current scientific research in neuropsychology can be applied extensively and beneficially to improve the quality of their daily lives.

Specifically, The Art of Happiness was a 9-month program, with one program activity each month, either a weekend module or a retreat; there were two retreats—a mid-course retreat and a concluding retreat (for full details on the program, see Supplementary Material ). Each thematic module provided an opportunity to sequentially explore the topics presented in the core course text, The Art of Happiness by the Lama and Cutler (2008) .

In terms of the content of this program, as mentioned above, the material presented and explored has been drawn on the one hand from the teachings of Mahayana Buddhism and Western contemplative traditions, and current scientific research found in neuropsychology on the other hand. On the scientific side, topics included the effects of mental training and meditation, the psychology and neuroscience of well-being and happiness, neuroplasticity, mind–brain–body interactions, different areas of contemplative sciences, the placebo effects, the brain circuits of attention and mind wandering, stress and anxiety, pain and pleasure, positive and negative emotions, desire and addiction, the sense of self, empathy, and compassion (for a full list of the scientific topics, see Supplementary Material ).

The overall approach of the course was one of non-dogmatic exploration. Topics were presented not as undisputed truths, but instead as information to be shared, explored, examined, and possibly verified by one's own experience. Participants were heartily invited to doubt, explore, and test everything that was shared with them, to examine and experience firsthand whether what was being offered has validity or not.

The course was, essentially, an informed and gentle training of the mind, and in particular of emotions, based on the principle that individual well-being is inextricably linked to the development of inner human virtues and strengths, such as emotional balance, inner self-awareness, an open and caring attitude toward self and others, and clarity of mind that can foster a deeper understanding of one's own and others' reality.

The program provided lectures and discussions, readings, and expert videos introducing the material pertinent to each module's topic. Participants engaged with the material through listening, reading, discussing, and questioning. Participants were provided with additional learning opportunities to investigate each topic more deeply, critically, and personally, through the media of meditation, journaling, application to daily life, exercises at home, and contemplative group work with other participants in dyads and triads. Participants were then encouraged to reflect repeatedly on their insights and on their experiences, both successful and not, to apply their newly acquired understandings to their lives, by incorporating a daily reflection practice into their life schedule. The two program retreats also provided intensive contemplative experiences and activities, both individual and in dialogue with others.

On this basis, month after month in different dedicated modules, participants learned new ways to nurture their own happiness, to cultivate their openness to others, to develop their own emotional and social well-being, and to understand some of the scientific discoveries on these topics.

The specific topics addressed in corresponding modules and retreats, each in a different and consecutive month, were as follows: (1) The Purpose of Life: Authentic Happiness; (2) Empathy and Compassion; (3) Transforming Life's Suffering; (4) Working with Disturbing Emotions I: Hate and Anger; first retreat (intermediate); (5) Working with Disturbing Emotions II: The Self Image; (6) Life and Death; (7) Cultivating the Spiritual Dimension of Life: A Meaningful Life; second retreat (final). Full details of the entire program are reported in the Supplementary Material .

Participants were guided in the theory and practice of various contemplative exercises throughout the course pertaining to all the different themes. Recorded versions of all the various meditation exercises were made available to participants, enabling them to repeat these practices at home at their own pace.

Participants were encouraged to enter the program already having gained some basic experience of meditation, but this was not a strict requirement. In fact, not all participants in this experiment actually fulfilled this (only five), although each of the other participants had previous basic experiences of meditation (through personal readings, other video courses, etc.). In spite of this variety, by the end of the 9-month program, all participants were comfortable with contemplative practices in general and more specifically with the idea of maintaining a meditation practice in their daily lives.

During the various Art of Happiness modules, a variety of basic attentional and mindful awareness meditations were practiced in order to enhance attentional skills and cultivate various levels of cognitive, emotional, social, and environmental awareness.

Analytical and reflective contemplations are a form of deconstructive meditation ( Dahl et al., 2015 ), which were applied during the course in different contexts. On the one hand, these types of meditations were applied in the context of heart-opening practices—for example, in the cultivation of gratitude, forgiveness, loving-kindness toward self and others, self-compassion, and compassion for others. Analytical and reflective meditations were also practiced as a learning tool for further familiarization with some of the more philosophical subject matter of the course—engaging in a contemplative analysis of impermanence (for example, contemplating more deeply and personally the transitory nature of one's own body, of one's own emotions and thoughts, as well as of the material phenomena that surround us). These analytical meditations were also accompanied by moments of concentration (sustained attention) at the conclusion of each meditation focusing on what the meditator has learned or understood in the meditative process, in order to stabilize and reinforce those insights more deeply within the individual.

Additional contemplative activities were also included in the program: contemplative art activities, mindful listening, mindful dialogue, and the practice of keeping silence during the retreat. Participants were, in addition, encouraged to keep a journal of their experiences during their Art of Happiness journey, especially in relation to their meditations and the insights and questions that emerged within themselves, in order to enhance their self-awareness and cultivate a deeper understanding of themselves, their inner life and well-being, and their own inner development during the course and afterward.

During the two retreats, the previous topics were explored again (modules 1–4 for the intermediary retreat and modules 5–7 for the final retreat), but without discussing the theoretical aspects (i.e., the neuroscientific and psychological theories), instead only focusing on the contemplative practices, which were practiced extensively for the whole day, both individually and in group activities (for a full list of the contemplative practices and retreat activities, see Supplementary Material ).

We collected data at five-time points, always during the first day (either of the module or the retreat): at baseline (month 1 - T0), at pre (T1) and post (P1) of the mid-course retreat (month 5–Retreat 1), and at pre (T2) and post (R2) of the final retreat (month 9–Retreat 2), as shown in Figure 1 . Participants filled out the questionnaires on paper all together within the rooms of the Institute Lama Tzong Khapa at the beginning of each module or retreat, and at the end of the retreats, with the presence of two researchers. The order of the questionnaires was randomized, per person and each questionnaire session lasted less than an hour.

Figure 1 . The timing of the course and the experimental procedure, including the modules, the retreats, and the 5 data collections (from T0 to P2).

The adopted questionnaires were those commonly used in the literature to measure a variety of traits and states linked to well-being. An exhaustive description of the self-reported measures follows below.

Satisfaction With Life Scale (SWLS)

The SWLS ( Diener et al., 1985 ) was developed to represent cognitive judgments of life satisfaction. Participants indicated their agreement in five items with a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Scores range from 5 to 35, with higher scores representing higher levels of satisfaction. Internal consistency is very good with Cronbach's α = 0.85 [Italian version of the normative data in Di Fabio and Palazzeschi (2012) ].

Short Version of the Perceived Stress Scale (PSS-10)

The PSS ( Cohen et al., 1983 ) was designed to assess individual perception and reaction to stressful daily-life situations. The questionnaire consists of 10 questions related to the feelings and thoughts of the last month, with a value ranging from 0 (never) to 4 (very often) depending on the severity of the disturbance caused. Scores range from 0 to 40. Higher scores represent higher levels of perceived stress, reflecting the degree to which respondents find their lives unpredictable or overloaded. Cronbach's α ranges from 0.78 to 0.93 [Italian version of the normative data by Mondo et al. (2019) ].

State-Trait Anxiety Inventory (STAI)

The STAI ( Spielberger et al., 1983 ) was developed to assess anxiety. It has 40 items, on which respondents evaluate themselves in terms of frequency with a four-point Likert scale ranging from 1 (almost never) to 4 (almost always). The items are grouped in two independent subscales of 20 items each that assess state anxiety, with questions regarding the respondents' feelings at the time of administration, and trait anxiety, with questions that explore how the participant feels habitually. The scores range from 20 to 80. Higher scores reflect higher levels of anxiety. Internal consistency coefficients for the scale ranged from 0.86 to 0.95 [Italian version of the normative data by Spielberger et al. (2012) ].

Positive and Negative Affect Schedule (PANAS)

PANAS ( Watson et al., 1988 ) measures two distinct and independent dimensions: positive and negative affect. The questionnaire consists of 20 adjectives, 10 for the positive affect subscale and 10 for the negative affect scale. The positive affect subscale reflects the degree to which a person feels enthusiastic, active, and determined while the negative affect subscale refers to some unpleasant general states such as anger, guilt, and fear. The test presents a five-point Likert scale (1 = very slightly or not at all; 5 = extremely). The alpha reliabilities are acceptably high, ranging from 0.86 to 0.90 for positive affect and from 0.84 to 0.87 for negative affect [Italian version of the normative data by Terracciano et al. (2003) ].

Five Facet Mindfulness Questionnaire (FFMQ)

The FFMQ ( Baer et al., 2008 ) was developed to assess mindfulness facets through 39 items rated on a five-point Likert scale, ranging from 1 (never or very rarely true) to 5 (very often or always true). A total of five subscales are included: attention and observation of one's own thoughts, feelings, perceptions, and emotions ( Observe ); the ability to describe thoughts in words, feelings, perceptions, and emotions ( Describe ); act with awareness, with attention focused and sustained on a task or situation, without mind wandering ( Act-aware ); non-judgmental attitude toward the inner experience ( Non-Judge ); and the tendency to not react and not to reject inner experience ( Non-React ). Normative data of the FFMQ have demonstrated good internal consistency, with Cronbach's α ranging from 0.79 to 0.87 [Italian version by Giovannini et al. (2014) ].

State-Trait Anger Expression Inventory-2 (STAXI-2)

The STAXI-2 ( Spielberger, 1999 ) provides measures to assess the experience, expression, and control of anger. It comprises 57 items rated on a four-point Likert scale, ranging from 0 (not at all) to 3 (very much indeed). Items are grouped by four scales: the first, State Anger scale, refers to the emotional state characterized by subjective feelings and relies on three more subscales: Angry Feelings, Physical Expression of Anger, and Verbal Expression of Anger. The second scale is the Trait Anger and indicates a disposition to perceive various situations as annoying or frustrating with two subscales—Angry Temperament and Angry Reaction. The third and last scales are Anger Expression and Anger Control. These assess anger toward the environment and oneself according to four relatively independent subscales: Anger Expression-OUT, Anger Expression-IN, Anger Control-OUT, and Anger Control-IN. Alpha coefficients STAXI-2 were above 0.84 for all scales and subscales, except for Trait Anger Reaction, which had an alpha coefficient of 0.76 [Italian version by Spielberger (2004) ].

Statistical Analysis

The responses on each questionnaire were scored according to their protocols, which resulted in one score per participant and a time point for each of the 22 scale/subscale questionnaires examined. Missing values (<2%) were imputed using the median. Descriptive statistics for all variables were analyzed and are summarized in Table 1 and in the first panel (column) of Figures 2 – 5 . Prior to conducting primary analyses, the distribution of scores on all the dependent variables was evaluated. Because the data were not normally distributed, we used non-parametric tests. Permutation tests are non-parametric tests as they do not rely on assumptions about the distribution of the data and can be used with different types of scales and with a small sample size.

Table 1 . Descriptive statistics of the depended variables among time points.

Figure 2 . Results of the Satisfaction with Life Scale (SWLS), Perceived Stress Scale (PSS), State and Trait Anxiety Index (STAI), and Positive and Negative Affect Scales (PANAS). The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from the one-way ANOVA. Note that scores are on the y -axis and time is on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—retreat 1), pre (T2), and post (R2) of the final retreat (month 9—retreat 2). Statistical significance, * p < 0.05.

Figure 3 . Results for the Five Facet Mindfulness Questionnaire FFMQ (Observe, Describe, Act with Awareness, Non-judge, and Non-react). The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from one-way ANOVA. Note that scores are on the y -axis and time id on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—retreat 1), pre (T2), and post (P2) of the final retreat (month 9—retreat 2). Statistical significance, * p < 0.05.

Figure 4 . Results of the first part of the State Trait Anger Expression Inventory (STAXI-2): State Anger, State Anger Feelings, State Anger Physical, State Anger Verbal, Trait Anger, and Trait Anger Temperament. The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from one-way ANOVA. Note that scores are on the y -axis and time is on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—retreat 1), pre (T2), and post (R2) of the final retreat (month 9—retreat 2). Statistical significance, ** p < 0.01 and * p < 0.05.

Figure 5 . Results from the second part of the State Trait Anger Expression Inventory (STAXI-2): Trait Anger Reaction, Anger Expression-IN, Anger Expression-OUT, Anger Control-IN, and Anger Control OUT. The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from one-way ANOVA. Note that scores are on the y -axis and time is on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—Retreat 1), pre (T2), and post (R2) of the final retreat (month 9—Retreat 2). Statistical significance, * p < 0.05.

The longitudinal effects of the program were analyzed to determine whether scores changed between the start, mid-point (5 months), and the end (9 months) of the course. To achieve this, we compared the main effect of the program on the score , considering Time as a unique factor with three levels: at the baseline (T0), at the pre of the mid-retreat (T1), and at the pre of the final retreat (T2). Here, we used a one-way permutation Repeated Measures Analysis of Variance (RM ANOVA) with the aovperm() function from the Permuco package v. 1.0.2 in R ( Frossard and Renaud, 2018 ), which implements a method from Kherad-Pajouh and Renaud (2014) . The difference between the traditional and the permutation ANOVA is that, while the traditional ANOVA tests the equality of the group mean, the permutation version tests the exchangeability of the group observations. In this study, the number of permutations was set to 100,000 and the alpha level was set to 0.05; therefore, the p -value was computed as the ratio between the number of permutation tests that have an F value higher than the critical F value and the number of permutations performed. Effect size estimates were calculated using partial eta squared. Post hoc testing used pairwise permutational t -tests with the “pairwise.perm.t.test” function from the “RVAideMemoire” package in R ( Hervé and Hervé, 2020 ). To account for Type I errors introduced by multiple pairwise tests and Type II errors introduced by small sample size, we applied the false discovery rate (FDR) correction method of Benjamini and Hochberg (1995) and set statistical significance at p = 0.05. Results are summarized in Table 2 and in the third panel (column) of Figures 2 – 5 .

Table 2 . One-way ANOVA and pairwise comparison results with 100,000 permutations.

The short-term effects of the contemplative program on each retreat were analyzed to determine whether scores changed post-retreats and whether these changes occurred in both retreats. Thus, we used a two-way permutation RM ANOVA, with the score of each scale/subscale as the dependent variable and the within-subject factors Retreat (1, 2) and Condition (Pre T1/T2, Post P1/P2) as independent variables. Results are summarized in Table 3 and in the second panel (column) of Figures 2 – 5 .

Table 3 . Results of the two-way permutation RM ANOVAs.

In addition, we explored differences attributed to the course and to the retreats using a paired permutation t test with the “perm.t.test()” function in R. We compare those psychological measures at the beginning of the course (T0) with its very end (P2), which coincided with the end of the second retreat. In this way, we illustrate a summary of changes due both to the second retreat and to the whole course. The results are summarized in Table 4 and depicted in a radar plot in Figure 6 .

Table 4 . Overall changes between the start (T0) and the end of the course (P2).

Figure 6 . Results of the permutation t -test between the start and the end of the course. All values ranged from 0 to 1. Variables: SWLS, Satisfaction with Life Scale; S-Ang/F, Feeling Angry; S-Ang/V, Feel like Expressing Anger Verbally; S-Ang/P, Feel like Expressing Anger Physically; T-Ang/T, Angry Temperament; T-Ang/R, Angry reaction; AX-O, Anger Expression-OUT; AX-I, Anger Expression-IN; AC-O, Anger Control-OUT; AC-I, Anger Control-IN; PSS, Perceived Stress Scale; STAI-Y1, State-Trait Anxiety Inventory—State; STAI-Y2, State-Trait Anxiety Inventory—Trait; PA and NA, Positive and Negative Affect Scales, respectively; OBS, Observe; DES, Describe; AWA, Act with awareness, Njudge, Non-judge; NReact, Non-react. To make consistent that an increase of the specific scale corresponds to an improvement in well-being, negative scales were reversed, namely: PSS, STAI-Y1, STAI-Y2, PANAS-NA, S-Ang, S-Ang/F, S-Ang/P, S-Ang/V, T-Ang, T-Ang/T, S-Ang/R, AX-O, AX-I. Concerning the statistical significance, *** p < 0.001, ** p < 0.01, and * p < 0.05.

Effects of the Program

Results from one-way permutation RM ANOVA showed a statistically significant effect of the program on SWLS at the p = 0.008 level over the Time course factor with a large effect size (ηp 2 = 0.16). Post hoc analysis revealed that the SWLS score was significantly higher at T2 with respect to T2 (mean difference = 2.48; p = 0.016). Similarly, SWLS was higher T2 as compared to T1 (mean difference = 1.38; p = 0.032).

Results also provided statistically significant evidence of changes in the PSS over the Time course ( p = 0.009), showing a large effect size (ηp 2 = 0.16). Post-hoc results showed a difference between T0 and T1, revealing that the PSS was significantly lower at T1 (mean difference = −2, p = 0.02).

Results revealed a significant effect of the Time course for Trait Anxiety ( p = 0.009, ηp 2 = 0.16). Post-hoc tests revealed a reduction in Trait Anxiety from the start of the course (T0) to the first day of the second retreat (T2) (M diff. = −3.21, p = 0.25).

Results also showed a significant effect of the Time course for negative affect ( p = 0.004, ηp 2 = 0.19). Post hoc analysis revealed that contemplative practice led to a reduction in negative affect from the baseline (T0) to the first day of the first retreat (T1) (mean difference = −2.42) and between T0 and first day of the second retreat (T2) (mean difference = −2.92), which differed significantly with p = 0.021 and p = 0.012, respectively.

Moreover, a significant effect of the Time course was found for several subscales of the FFMQ. First, the observe scale was found at the p = 0.023 level showing a large effect size (ηp 2 = 0.13). Post-hoc comparisons revealed an increasing capacity to observe one's own thoughts, from the middle of the course (T1) to the first day of the second retreat (T2) (mean difference = 1.58, p = 0.038). Likewise, there was a significant difference for the capacity to Act with Awareness ( p = 0.036, ηp 2 = 0.12). Post hoc comparisons revealed an increased level at T2 as compared to T1 (mean difference = 2.07, p = 0.043). The Time course had a significant effect on the Non-Judge subscale with a large effect size ( p = 0.002, ηp 2 = 0.20). Post hoc analysis indicated a significant increase from T0 to T1 (mean difference = 2.07, p = 0.013), as well as from T0 to T2 (mean difference = 3.31, p = 0.013).

In regard to the STAXI-2, we found Time course significant effects on Trait Anger ( p = 0.001, ηp 2 = 0.23) and its subscales, Trait Anger Temperament ( p = 0.001, ηp 2 = 0.22) and Trait Anger Reaction ( p = 0.016, ηp 2 = 0.14). Post-hoc comparisons revealed a significance difference on the Trait Anger Scale, which decreased from the beginning of the course (T0) to 5 months later (T1) (mean difference = −1.83, p = 0.041) and also from T0 to the end of the course (T2) (mean difference = −3.24, p = 0.002). Similarly, State Anger Temperament significantly decreased from T0 to T1 (mean difference = −0.79, p = 0.016) and from T0 to T2 (mean difference = −1.38, p = 0.008). Additionally, Trait Anger Reaction decreased from T0 to T2 (mean difference = −1.24, p = 0.023). Finally, the longitudinal effect of the course on the STAXI-2 led to significant results in the Anger Control-IN subscale over the Time course ( p = 0.03, ηp 2 = 0.12). Here, post-hoc comparisons showed a statistically significant difference between T0 and T2, which increased (mean difference = 1.76, p =.044). For more details, see Table 2 and the third panel (column) of Figures 2 – 5 .

Effects of the Retreats

Two-way permutation RM ANOVAs showed a significant main effect for Retreat on SWLS ( p = 0.002, ηp 2 = 0.16), Trait Anxiety ( p = 0.001, ηp 2 = 0.19), positive affect ( p = 0.044, ηp 2 = 0.07), Observe ( p = 0.008, ηp 2 = 0.12), Act with awareness ( p ≤ 0.001, ηp 2 = 0.22), Non-Judge ( p = 0.045, ηp 2 =.07), Non-React ( p = 0.02, ηp 2 = 0.10), Trait Anger ( p = 0.008, ηp 2 = 0.12), Trait Anger Temperament ( p = 0.022, ηp 2 = 0.09), Trait Anger Reaction ( p = 0.019, ηp 2 = 0.10), and Anger Control-IN ( p = 0.029, ηp 2 = 0.08). A main effect of the Condition (Pre vs. Post) was found only for the State Anxiety scale with p = 0.004 and a large effect size (ηp 2 = 0.14). Analysis results including F statistics are summarized in Table 3 ; a visual representation of the data is presented in the second panel (column) of Figures 2 – 5 .

Overall Effects of the Course and Retreats

As predicted, permutation t -test analysis revealed that participants increased their reported level of SWLS from the start (T0) to the end (P2) of the course (mean difference = 2.83, p = 0.008). Two subscales from the FFMQ, namely, the capacity to observe one's own thoughts (mean difference = 1.86, p = 0.039) and non-judgmental attitude toward the inner experience (mean difference = 3.24, p = 0.006), also significantly increased from the start to the end of the course. On the other hand, the affect linked to the progression from the start (T0) to the very end of the course (P2) was related to a significant decrease in the negative affect (mean difference = −3.62, p = 0.001). In the same way, the average level of stress of the sample decreased significantly (mean difference = −1.9, p = 0.033) along with a significant decrease of Trait Anxiety (M diff = −3.97, p ≤ 0.001). Participants also decreased on almost all STAXI-2 subscales. Here, the results from permutation paired t -test reveal a significant difference in scores, which decreased from T0 to P2 on all the subscales of Trait Anger (mean difference = −3.55, p ≤ 0.001; Trait Anger Temperament: mean difference = −1.34, p ≤ 0.001; Trait Anger Reaction: mean difference = −1.52, p ≤ 0.001), with an increased value for the subscales Anger Control-OUT (mean difference = 1.93, p ≤ 0.009) and Anger Control-IN (mean difference = 1.93, p = 0.017). For more details, see Table 4 and Figure 6 .

The aim of this study was to examine the effectiveness of an integrated 9-month mental training program called The Art of Happiness , which was developed to increase well-being in a general population. By a range of well-established psychometric assessment tools, we quantified how several psychological well-being variables changed with course attendance. We took into account both the trait effects of the course acting at a long timescale (over the 9-month duration of the full course) and the state effects of intensive retreat experiences acting at a short time scale (over the course of each of the two retreats). Several psychological well-being measures related to states and—more importantly—traits gradually improved as participants progressed from the beginning to the end of the course.

On the one hand, the program produced a significant longitudinal effect (9 months) revealing a progressive increase in the volunteer's levels of life satisfaction and of the capacities to reach non-judgmental mental states, to act with awareness, to non-react to inner experience, and to exercise control over attention to the internal state of anger, in line with other contemplative interventions ( Fredrickson et al., 2008 ; Keng et al., 2011 ; Baer et al., 2012 ; Kong et al., 2014 ). Conversely, after the completion of the program, there were decreases in levels of trait anxiety, trait anger (including both the anger temperament and reaction subscales), and negative affect, showing a progressive reduction during the intervention. These results support prior research that demonstrated the longitudinal positive effects of a multitude of contemplative practices on well-being measures linked to—among others—decreased trait anxiety, trait anger, and negative affect ( Fix and Fix, 2013 ; Khoury et al., 2015 ; Gotink et al., 2016 ). Such findings highlight the gradual development of mental states related to subjective well-being in parallel with ongoing contemplative practices over a time scale of months, with a gradual increase of wholesome mental states, and a gradual decrease of unwholesome mental states. Notably, as in other mindfulness interventions ( Khoury et al., 2015 ; Gotink et al., 2016 ), there was a significant reduction in the level of perceived stress already in the first few months of the program (T0–T1).

Additionally, these results show the specific effects between retreat experiences within the program as an intervention for fostering happiness. Specifically, the retreats had a positive effect on the participants' perceived well-being, which improved between the two retreats (with a 4-month interval). Among other assessed dimensions, between the retreats, there were significantly increased levels of life satisfaction, positive affect, and mindful abilities to act with awareness, to observe, non-react, and non-judge inner experience and the capacity to control anger toward oneself. Conversely, there were significantly lower levels of trait anxiety and trait anger (including both the anger temperament and reaction subscales) between the retreats (over a period of 4 months).

Regarding the very short effects of the course, we highlight significant changes within the first part of the training and prior to the first retreat (T0–T1). Here, some variables related to happiness changed most, suggesting their independence from retreat. Particularly, PSS notably decreased along with negative affect and Trait Anger (the subscale of Angry Temperament), while the capacity of non-judgmental attitude toward the inner experience significantly increased, providing useful information for future interventions.

Moreover, participants' state anxiety significantly decreased in a very short time (5 days), between pre and post of both retreats. These findings are consistent with previous studies, which demonstrated the positive effects of contemplative training and practices on these measures in retreats ( Khoury et al., 2017 ; Howarth et al., 2019 ; McClintock et al., 2019 ). In Figure 6 , we make a general and integrated comparison between the various psychological measures, comparing the very beginning of the course with its very end, which also coincided with the end of the second retreat. In this way, we illustrate both state changes (due to the second retreat) and trait changes (due to the whole course). This representation allows an integrated view of all the changes that took place at different time scales. This graph might suggest that the only measures that did not change significantly from the beginning to the end of the course are those in which the participants already had a score strongly oriented toward well-being, and therefore with little room for a change. Thus, future studies could take into account individual differences when evaluating happiness programs.

Although the present findings are promising, this study presents several limitations that need to be taken into consideration. The two main limitations rely on the absence of a randomized control group and in the fact that participants were self-selected. This lack of verification makes it difficult to determine whether the results are attributable to the program or to other factors, for example, simply arising due to spending time in a happiness-oriented activity. It is also important to note that despite examining several assessments within persons, the sample size was restricted to 29. Furthermore, responses to the questionnaires may have been biased toward the socially desirable response as the course's staff administered them, and another active group could have controlled for these effects. Consequently, it is recommended to conduct future studies with larger samples and a well-designed and controlled trial, in order to achieve more conclusive findings. Another limitation is that, while all the participants attended the whole course with a comparable (coherent) level of commitment to the practices (including the retreats), we did not verify their course-related activity and practices at home, and therefore, we have no way to check whether they actually did the practice activities at home as suggested during the modules.

Possible new directions of exploration of this study concern the age range of the participants, which, in our case, was limited to middle-aged individuals (39–66), and therefore, the effects on younger or older individuals remain currently unexplored. Another interesting direction would be to conduct follow-up measurements to assess the stability of the longitudinal effects months or years after the end of the program. Finally, while well-being and happiness are individual and subjective narratives of one's life as good and happy ( Bauer et al., 2008 ), and therefore self-assessments through questionnaires are a valid and common tool of investigation, in interventions such as The Art of Happiness , it would be appropriate to also explore individual differences, more objective psychophysiological effects, as well as cultural and social aspects influencing the inner model of happiness.

Despite these methodological limitations and still unexplored directions of research, the results described here suggest that The Art of Happiness may be a promising program for fostering well-being in individuals, improving mental health and psychological functioning. Longitudinal integrated contemplative programs with retreats offer a unique opportunity for the intensive development of the inner attitudes related to the capacity to be happy, reducing mental health symptoms and improving a more stable eudemonic well-being in healthy adults.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Nicola De Pisapia, upon reasonable request.

Ethics Statement

The studies involving human participants were reviewed and approved by Ethics Committee of the Sapienza University of Rome. The participants provided their written informed consent to participate in this study.

Author Contributions

ND, CM, and AR designed the study. ND, CM, LC, and AR collected the data. CR analyzed the data. CR and ND wrote the original draft. All authors edited and reviewed the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank the Institute Lama Tzong Khapa (Pomaia, Italy) for the support in various phases of this experiment. We also wish to express our gratitude to the reviewers for their thoughtful comments and efforts toward improving the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.600982/full#supplementary-material

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Keywords: meditation, wisdom, happiness, well–being, mindfulness

Citation: Rastelli C, Calabrese L, Miller C, Raffone A and De Pisapia N (2021) The Art of Happiness: An Explorative Study of a Contemplative Program for Subjective Well-Being. Front. Psychol. 12:600982. doi: 10.3389/fpsyg.2021.600982

Received: 31 August 2020; Accepted: 11 January 2021; Published: 11 February 2021.

Reviewed by:

Copyright © 2021 Rastelli, Calabrese, Miller, Raffone and De Pisapia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nicola De Pisapia, nicola.depisapia@unitn.it

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Happiness among higher education academicians: a demographic analysis

Rajagiri Management Journal

ISSN : 0972-9968

Article publication date: 20 March 2020

Issue publication date: 29 June 2020

To deal with highly energetic younger generation patiently, need academicians who can spread happiness while teaching/mentoring are needed. This is possible when an academician himself is a happy person. This paper aims to explore the factors that generate happiness among academicians, studies the impact of demographic variables on academicians’ happiness and examines the relationship between academicians’ happiness and their performance.

Design/methodology/approach

Convenience purposive sampling method was used to obtain data through self-administered survey questionnaire based on a five-point Likert scale, delineating the research purpose and assurance of confidentiality. For data analysis, statistical techniques like mean, percentage method, Levene’s test, t -test and analysis of variance were used. To study the relationship between performance and happiness, the attitude, motivation and outcome theory was applied and happiness index was developed.

After analyzing the various factors impacting academicians’ happiness, this study found that except for work–life balance, research activities and working environment, all other factors are available to academicians according to their ranked importance assigned to them. This study also obtained a happiness index using matrix and has developed an equation which can be applied to find out the relationship between happiness and performance in future.

Research limitations/implications

This study has certain limitations, first, this study has been conducted on academicians working in higher education institutes situated in Delhi/NCR and thus entails a specific socio-cultural environment that may limit the potential level of generalization.

Practical implications

The results of this research might help institutes/higher education bodies to make rules and policies which may further augment academicians’ happiness to accomplish their desired goals.

Social implications

An academician who is happy, satisfied and motivated can easily deal with today's enthusiastic younger generation and can spread happiness amongst them. so it is very much necessary for an academician to be happy and energetic all the time.

Originality/value

This study found the factors impacting higher education academicians’ happiness and its impact on their teaching performance.

• Higher education
• Negative emotions
• Academicians' performance
• Happiness index
• Happiness quotient
• Workplace happiness

Arora, R.G. (2020), "Happiness among higher education academicians: a demographic analysis", Rajagiri Management Journal , Vol. 14 No. 1, pp. 3-17. https://doi.org/10.1108/RAMJ-11-2019-0024

Emerald Publishing Limited

Copyright © 2020, Ritu Gandhi Arora.

Published in Rajagiri Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Longman’s dictionary (2005, p. 634) defines happiness as “state of being happy”, means a feeling of gratification, i.e. something is fine or correct, as being satisfied with something, not apprehensive or about being fortunate and doing well. Happiness is generally confused with a form of mood or emotion or satisfaction; also, both these terms are used interchangeably by many authors. Happiness has been termed as positive emotions by various psychologists. Workplace happiness is the result of strategies, principles, rules and regulations made by the top management. It is a general notion amongst employees that if they are successful at their job and completing all their targets well in time, they are happy. But today, the scenario has been reversed. It is important to be happy, which will then help people become a success. There are enormous changes coming in the work environment. Long-established systems, policies, rules and strategies might not be apt for today’s generation. For this generation, the meaning of work and work style has also changed. Old customs need revalidation, and new approaches require fast adaptation. It is apparent of one becoming irritating and annoyed after a stretched and chaotic schedule, but this may not even happen if one finds his/her work interesting enough. Getting engaged in work results in high productivity and will automatically generate interest only when employees are feeling happy at work place. Being happy is the key to productivity ( Djoen and Hewagamage, 2016 ), and it has considerable relationship with performance ( Michael, 1989 ). Employers also look forward to a high-performing employee who in turn gives high productivity, to attain organizational goals. To enhance employee productivity, management adopts various strategies like rewards/incentives, direction communication with staff members, top management supports, employee involvement in decision making and so on.

Conceptualization

Happiness is subjective, i.e. a feeling of well-being experienced by an individual, specially featured by the presence of affirmative emotions and the nonappearance of negative emotions. It may be distinct as the experience of recurrent positive effect, infrequent negative effect and, on the whole, a sense of satisfaction with life ( McBride, 2010 ). Happiness at work is closely correlated with greater performance and productivity as well as greater energy, better reviews, faster promotion, higher income, better health and long life. If taken as a whole, the idea of happiness is how much you like what you have or do. Even if two persons have everything equal, they may differ in their happiness, as it depends on how much you actually require, i.e. your expectations may differ.

In an academician’s career, his/her happiness not only depends upon job satisfaction, students' results and feedback. Government systems, its pay policies and organizational hierarchies also plays a major role. Academicians work in an altogether different environment, i.e. they deal with the younger generation in classrooms, matured individuals and learned faculty outside the classrooms and knowledgeable entrepreneurs to understand industry requirements.

Even though many studies available on the relationship between happiness and productivity, performance, stress among employees, etc. that concentrate on many industries, e.g. construction, Information Technology (IT), Information Technology Enabled Services (ITeS), manufacturing, textile, telecom, etc. but very few studies are available as far as academicians’ happiness is concerned. Among academicians also, the higher education faculty plays the crucial role in shaping the personality of students from unrefined human product to refined saleable product to be further consumed by industry and later by the economy. Their low happiness level influences their knowledge sharing in the classrooms and ultimate sufferers are none other than students (Ministry of Human Resource Development (MHRD) Survey, 2015-16). So, to enhance their performance, keeping them happy is exceptionally important across the education sector.

This study mainly focuses on finding out the various factors which impact their happiness at workplace. The results of this research might help institutes/higher education bodies to make rules and policies which may further augment academicians’ happiness to accomplish their desired goals.

Literature review

Ford et al. (2003) argued that happiness involves activities that convey a sense of pleasantness, happiness and positive well-being, that not only make working satisfied but also fun. In psychology, happiness is a relatively positive perception about self, but definitely not total absence of negative emotions ( Diener and Satvik, 1991 ). Happiness at workplace has positive effects on performance. To make employees happy, companies must decide the factors that contribute to their happiness and pleasure at workplace. Workplace happiness and relationship between employees (individual or group) are, therefore, positively related to each other. Frey and Stulzer (2000) examined three factors of happiness, i.e. personality and demographic factors (work, income, community, value, religion, family, experience, education, gender and age), micro- and macro-economic factors (per capita income, employment, inflation) and third is institutional factors like democracy and federalism. Whereas, Graham et al. (2004) mentioned that happiness is subject to various changes and fluctuations; it is a part of our nature, inherent in us by our parents through genes.

Factors affecting happiness at workplace among academicians

Hill (1986) has reported empirical support for extrinsic factors such as salary, administrative work and fringe benefits as far as happiness among faculty is concerned, but he also supported research and teaching as intrinsic happiness factors. Lacy and Sheehan (1997) , contended work environment, organization’s atmosphere, relationship with colleagues as predictors of happiness among academicians. Leung et al. (2000) observed further that acknowledgment, management policies and monetary sufficiency are the predictors of job happiness among academicians. Mushtaq and Sajid (2013) in their study found that classroom environment makes academicians happy. If their students are happy, they do not even feel the work load stress. Jennifer (1996) discussed the impact of financial rewards, classroom teaching culture, role diversity, autonomy and organizational structure on the academician’s happiness at work. Further in this, Farren and Nelson (1999) underlined that the employees’ feel connected with those organizations which carry out mixture of staff development program compared with those who do not. Since long, researchers have also maintained that variety of facilities (monetary/non-monetary) have positive effects on employees’ attitudes ( Simons et al. , 2007 , Butter, Lowe, 2010). Empirical research done in Lithuania depicts that employee-oriented practices always have a significant and positive relation with employee motivation as well as their happiness also affects employee turnover intentions.

Academic institutions transmit knowledge and develop students; their poor performance or low morale can influence the knowledge sharing, and the ultimate sufferers are the future generations. At apex level, the Indian higher education industry has number of central, state, deemed and private universities ( All India Survey on Higher Education 2016‐17, 2017 ). This industry is either short of manpower or the quality of faculty is very poor in terms of communication skills, subject expertise, industry academia interface, etc. This requires the severe need for enhancing the attractiveness of teaching as a profession as well as motivator to select this profession by choice not by compulsion amongst the young generation.

Objectives of the study

find out factors influencing happiness of academicians at various institutional levels;

explore the difference in happiness level of academicians working at different hierarchical levels in terms of demographic variables like age, gender and designation; and

use the differences for framing a mathematical model to study the relationship between academicians’ happiness and their performance using the attitude, motivation and outcome (AMO) theory.

Workplace happiness factors significantly differ among demographic variables like age, education and designation.

Workplace happiness factors do not significantly differ among demographic variables like age, education and designation.

Research methodology

The research study was conducted on academicians working in various universities (government, private and deemed) and colleges (self-financing or aided) located in and around Delhi/NCR. Convenience purposive sampling method was used to obtain data through self-administered survey questionnaire based on a five-point Likert scale, delineating the research purpose and assurance of confidentiality. Respondents were given the liberty of not to give their identifiable information to maintain the anonymity of the responses. The questionnaire included the instruments related to top management support, job satisfaction and work culture. Of 350, 336 duly filled questionnaires were received back via mail or in person. A total of 21 of 336 returned questionnaires were found to be invalid, so, in total, 315 responses were used for further analysis. The study was conducted from January 2018 to February 2019.

For data analysis, statistical techniques like factor analysis, mean, rank/percentage method, Levene’s test, t -test and analysis of variance (ANOVA) were used. Levene’s test was used to test the equality of variances for a variable calculated for two or more groups (Levene, 1960).

Reliability analysis

Table I represents the reliability coefficient of all scales used in this study. The reliability of the questionnaire was checked through Cronbach’s alpha which is used to estimate the reliability of a psychometric test. Closer the Cronbach’s alpha coefficient is to 1.0, the greater the internal consistency of the items in the scale (Gliem and Gliem, 2003). The results of the test show that the items are reliable, i.e. 0.882. The Kaiser–Meyer–Oklin (KMO) value for these variables was 0.859, indicating that the sample size was adequate for applying factor analysis (Field, 2005).

Results and discussion

The sample comprises all categories of academicians including assistant professors, associate professors and professors having minimum qualification required for the appointment on the concerned post. The sample was selected keeping in mind the faculty/student (1:2:3) ratio decided by UGC/AICTE also to provide due and adequate representation to various other variables like age, sex, gender, nature of organization, job nature and department. The various classifications of samples are duly represented in Table II .

Exploratory factor analysis

The variables with loadings of at least 0.5 (Hair et al. , 2006) were included in the analysis. For factor extraction, principal component method was used. Eight factors were obtained and named according to the variables included in them. These factors with their names and respective loadings are shown in Table III .

To find out the factors affecting academician’s happiness level in an organization, factor analysis was applied and eight factors were obtained as a result of the exploratory factor analysis, namely, research activities (F1), working environment (F2), fringe benefits (F3), personal growth (F4), job security (F5), salary (F6), work–life balance (F7) and involvement in social endeavors (F8). Mean and standard deviation (SD) of the various happiness factors thus obtained affecting happiness at workplace and their rankings are shown in Table IV .

Table IV shows that academicians want F4 ( x ̄ = 4.32) through a well-structured organization chart/defined hierarchy; they expect an institute to define their career path clearly at the time of joining or through a well-defined individual career plan. Also, because of government emphasis and increasing awareness among public for social causes, academicians have given importance to institutional F8 to serve societies and their involvement in same ( x ̄ = 4.21).

To establish the difference between the happiness factors and various demographic variables, ANOVA and t -test have been applied. Further, the significant relationship between the groups within a demographic characteristic has been tested by applying the post hoc test.

Gender-wise comparison of factors affecting academicians’ happiness at workplace

Academicians may have different views regarding happiness factors. To find out whether there is any significant difference between the mean score of male and female academicians, t -test has been applied ( Table V ). Highest mean value for F7 for both females ( x ̄ = 4.35) and males ( x ̄ = 4.27) depicts that both men and women want to maintain equity in their professional and personal life. They give equal priority to enjoyment and work. For both, F6 is the second important factor which makes them happy. Whereas, in case of female academicians, their involvement in social awareness programs gives them happiness, and male academicians feel happy when they are more involved in what and why questions related to various issues at social and professional front, i.e. their involvement in F1.

Further, the results show that there is a significant difference between male and female academicians in the influence of F3, F5 and F8 on their happiness.

Null hypothesis is hence rejected, as there is a significant difference between male and female respondents regarding various factors affecting their happiness while working and performing in an institution.

Age-wise comparison of factors affecting happiness

Age of an academician also came out as an important factor, which determines happiness quotient of academicians. Academicians under 35 years of age rate F7 and F2 at work place as more important than their F6 and growth prospects in the college/institute as one of the important reasons to be happy. Whereas, academicians above 35 years of age feel happy when they are involved in F1, F6 and are able to maintain F7. They feel happy when an institute offers them competitive pay package and also provides them sufficient time and facilities to balance their work and life ( Table VI ).

The comparison of factors between different age groups of respondents regarding factors impacting their happiness at workplace differs significantly except on two factors, i.e. F7 and F8. Study clearly stated that because of the difference in age, employee priorities also change; at one point of time, he/she gives more preference to F6 and at another point of time he/she is more in favor of research and CSR activities. To be happy at workplace, academicians need regular feedback and appropriate appraisals. Hence, the null hypothesis is rejected, and alternate hypothesis accepted for these factors.

The post hoc test results ( Table VI ) reveal that the difference is significant among the different age group for six factors (except F7 and F8).

Designation-wise comparison of factors affecting happiness

Table VII shows that assistant professors feel happy when they have been provided cordial Work Environment (F2) in an institute ( x ̄ = 4.27) through which they can maintain coordination between their family and job F2 ( x ̄ = 4.22). Teaching is known to be a profession which needs dedication and hard work not only for self but also for society. So, faculty needs to be calm and cool while dealing with young generation of 20-25 years of age.

Associate professors gives importance to factors which ensures their F5 ( x ̄ = 4.47) along with F1 ( x ̄ = 4.40) and F7 ( x ̄ = 4.40), and same is in the case of professors. They also want to be involved in more research projects ( x ̄ = 4.45) sponsored/funded by UGC or companies, respectively. But simultaneously, they are also of a viewpoint that maintaining work–life and good F6 package is equally important because of family responsibilities and presence of growing/teenage kids at home.

As per the results shown in Table VII , hypothesis H0 that designation of faculty member significantly influences workplace happiness among academicians is accepted in case of five major factors, namely, F1, F2, F3, F4 and F5. The post hoc results also state that this difference is significant in case of these five factors only.

Mathematical model and equation to draw the relationship between academicians’ performance and happiness using the AMO theory

After exploring the factors influencing higher education academicians’ happiness level, the interaction of extracted factors has been used to draw a matrix.

In this study, three matrices are used to represent the relationship among the factors affecting happiness at the three designations: assistant professor, associate professor and professors, because of difference in factors influencing happiness at the three hierarchical levels; so, to determine the numerical happiness index, the permanence of the matrices is evaluated. The permanent is similar to determinant of matrix but with all signs positive, e.g.: perm ( a b c d e f g h i ) = a e i + b f g + c d h + c e g + b d i + a f h .

The permanent of assistant professor matrix: perm ( M A P ) = ( ( ( D 4 F 6 G 2 + B 2 D 4 F 6 )  H 7 + ( D 4 F 7 G 2 +   B 2 D 4 F 7 ) H 6 +   B 2 D 4 F 6 G 7 )   I 9 + ( ( D 4 F 9 G 2 + B 2 D 4 )   H 6 + B 2 D 4 F 6 )   I 7 + ( B 11 D 4 F 6 G 9 H 7 +   ( D 4 F 7 G 9 +   D 4 G 7 )  H 6 )   I 2 )   J 10 K 8 + ( ( B 11 D 4 F 6 G 2 + B 2 D 4 F 6 G 11 )   H 8 I 9 +   B 11 D 4 F 6 G 9 H 8 I 2 )   J 10 K 7 + ( B 11 D 4 F 6 G 7 H 8 I 9 + B 11 D 4 F 6 G 9 H 8 I 7 ) J 10 K 2 +   ( B 2 D 4 F 6 G 7 H 8 I 9 + B 2 D 4 F 6 G 9 H 8 I 7 )   J 10 K 11 +   ( ( ( B 11 D 4 F 6 G 2 +   B 2 D 4 F 6 G 11 )   H 8 I 7 + B 11 D 4 F 6 G 7 H 8 I 2 )   J 9 + ( ( ( B 11 D 4 F 6 G 2 +   B 2 D 4 F 6 G 11 )   H 7 + ( B 11 D 4 F 7 G 2 + B 2 D 4 F 7 G 11 )   H 6 +   B 2 D 4 F 6 G 7 H 11 )   I 9 +   ( ( B 11 D 4 F 9 G 2 +   B 2 D 4 F 9 G 11 )   H 6 +   B 2 D 4 F 6 G 9 H 11 )   I 7 + ( B 11 D 4 F 6 G 9 H 7 +   ( B 11 D 4 F 7 G 9 +   B 11 D 4 F 9 G 7 )   H 6 )   I 2 )   J 8 +   ( ( B 11 D 4 F 7 G 2 +   B 2 D 4 F 7 G 11 )   H 8 I 9 +   ( B 11 D 4 F 9 G 2 + B 2 D 4 F 9 G 11 )   H 8 I 7 +   ( B 11 D 4 F 7 G 9 +   B 11 D 4 F 9 G 7 )   H 8 I 2 )   J 6 +   ( B 11 D 4 F 6 G 7 H 8 I 9 +   B 11 D 4 F 6 G 9 H 8 I 7 )   J 2 )   K 10

The permanent of associate professor matrix: perm   ( M ASOP ) =   ( ( ( A 1 D 4 E 5 F 12 +   ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 1 )   G 11 H 6 +   ( A 1 D 5 E 12 F 6 +   A 6 D 5 E 12 F 1 )   G 11 H 4 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 11 H 12 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 12 H 11 +   ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 ) F 6 +   A 6 D 4 E 5 F 12 )   G 11 H 1 )   K 7 +   ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 12 H 7 +   ( ( A 1 D 4 E 5 F 12 +   ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 1 )   G 7 +   A 1 D 4 E 5 F 7 G 12 )   H 6 +   ( A 1 D 5 E 12 F 6 +   A 6 D 5 E 12 F 1 )   G 7 H 4 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 7 H 12 +   ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 7 +   A 6 D 4 E 5 F 7 G 12 )   H 1 )   K 11 )   L 8 +   ( ( ( A 1 D 4 E 5 F 12 + ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 1 )   G 11 H 6 +   ( A 1 D 5 E 12 F 6 +   A 6 D 5 E 12 F 1 )   G 11 H 4 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 11 H 12 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 12 H 11 +   ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 11 H 1 )   K 8 +   ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 12 H 8 +   A 5 D 4 E 8 F 1 G 12 H 6 +   ( A 1 D 5 E 8 F 6 +   A 6 D 5 E 8 F 1 )   G 12 H 4 +   A 5 D 4 E 8 F 6 G 12 H 1 )   K 11 )   L 7 +   ( ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 11 H 7 +   A 1 D 4 E 5 F 7 G 11 H 6 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 7 H 11 +   A 6 D 4 E 5 F 7 G 11 H 1 )   K 8 +   ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 11 H 8 +   A 5 D 4 E 8 F 1 G 11 H 6 +   ( A 1 D 5 E 8 F 6 + A 6 D 5 E 8 F 1 )   G 11 H 4 +   A 5 D 4 E 8 F 6 G 11 H 1 )   K 7 +   ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 7 H 8 +   A 5 D 4 E 8 F 1 G 7 H 6 + ( A 1 D 5 E 8 F 6 +   A 6 D 5 E 8 F 1 )   G 7 H 4 +   A 5 D 4 E 8 F 6 G 7 H 1 )   K 11 )   L 12 +   ( ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 11 H 7 +   ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 7 G 11 H 6 +   A 6 D 5 E 12 F 7 G 11 H 4 +   A 6 D 4 E 5 F 7 G 11 H 12 +   ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 7 +   A 6 D 4 E 5 F 7 G 12 )   H 11 )   K 8 +   ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 ) G 11 H 8 +   A 5 D 4 E 8 F 12 G 11 H 6 +   ( A 12 D 5 E 8 F 6 +   A 6 D 5 E 8 F 12 )   G 11 H 4 +   A 5 D 4 E 8 F 6 G 11 H 12 +   A 5 D 4 E 8 F 6 G 12 H 11 )   K 7 +   ( ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 7 +   A 6 D 4 E 5 F 7 G 12 )   H 8 +   A 5 D 4 E 8 F 6 G 12 H 7 + ( A 5 D 4 E 8 F 12 G 7 +   A 5 D 4 E 8 F 7 G 12 )   H 6 +   ( ( A 12 D 5 E 8 F 6 +   A 6 D 5 E 8 F 12 )   G 7 +   A 6 D 5 E 8 F 7 G 12 )   H 4 +   A 5 D 4 E 8 F 6 G 7 H 12 )   K 11 )   L 1

The permanent of professors matrix is: p e r m a (   M P ) =   ( ( A 1 F 6 +   A 6 F 1 )   G 12 H 7 +   ( ( A 1 F 12 +   A 12 F 1 )   G 7 +   A 1 F 7 G 12 ]   H 6   +   ( A 1 F 6 +   A 6 F 1 )   G 7 H 12 +   ( ( A 12 F 6 +   A 6 F 12 )   G 7 +   A 6 F 7 G 12 ) ) H 1 ) L 8 +   ( A 1 F 6 +   A 6 F 1 )   G 12 H 8 L 7 +   ( A 1 F 6 +   A 6 F 1 )   G 7 H 8 L 12 +   ( ( A 12 F 6 +   A 6 F 12 )   G 7 +   A 6 F 7 G 12 )   H 8 L 1

The permanence of this matrix has been used to quantify the qualitative happiness factors. The happiness index thus obtained through the matrix has been related to the performance of the academicians. Thus, the factors of happiness are converted to a numerical value through which the degree of performance can be ascertained. So, this matrix helped to quantify the qualitative factors of happiness. According to Davidoff (1987), individual performance is generally determined by three factors, namely, ability – the capability to do the job; work environment – the tools, materials and information needed to do the job; and motivation – the desire to do the job happily and readily.

In this paper, matrix is used to show the relationship between various happiness factors affecting three different levels taken for study, i.e. assistant professor, associate professor and professors. The factors affecting different academicians working at different levels are related to each other. Through GTA, i.e. through digraph, matrix and permanent function, the happiness index of assistant professor (perma H AS ), associate professor (perma H ASOP ) and of professors (perma H P ) is obtained. Through this, the happiness index of academicians (HI A ) can be given as: H I A =   p e r m a   H A S +   p e r m a   H A S O P +   p e r m a   H P

The happiness index, thus, obtained is linked to the academician’s performance in the classroom as well in the institute.

The ability Ai to perform has to be understood in a broader sense. It includes an employee’s knowledge, skills and abilities. This relationship is based on the AMO theory where (Pi) is the performance of an individual, (i) is function (f) of his or her ability (Ai) to perform, his or her happiness/willingness to perform happily (Hi) and the opportunity to perform in the job is Oi (Boxall and Purcell, 2011): P e r f o r m a n c e   P i   =   A b i l i t y   o f   a n   i n d i v i d u a l   t o   p e r f o r m   A i × H a p p i n e s s   H i × O p p o r t u n i t y   t o   p e r f o r m   O i

The derived happiness index obtained can further be used to measure the performance of an individual and, ultimately, the performance of an organization as a whole. The happiness index can be used in the AMO theory as follows:

Performance of an organization = Sum total of performance of employees of the organization. As per the results of present study, the performance of an academic institution can be measured as: P A I =   H I A     ×   N   A b i l i t y   o f   A c a d e m i c i a n   ×   O p p o r t u n i t y   p r o v i d e d   t o   A c a d e m i c i a n

Where, P AI is the performance of an academic institution and N is the number of academicians in the institution.

Conclusion and suggestions

The results of the study clearly show that most of the academicians irrespective of their age, experience and designation ranked F7 and F2 of an institute or college as most important happiness factors. The reason for ranking these factors as most important could be because of high family expectations along with student’s expectations from their faculty. Because of the increasing use of ICT tools in teaching and training, students and faculty involvement has become of 24/7, which might have become troublesome for faculty members. In comparison to government universities/aided colleges, private college faculties need more upgradation with the latest technological innovations; they have more work pressures, less holidays and no time barrier. Consequently, academicians do not find much time for their families and leisure activities. So, the management should provide them proper facilities, holidays to help them to lead a balanced life. When faculty stays for long hours in the campus, they should be compensated properly so that they should not feel that their jobs are taking a toll on them. Some faculty members look for more sponsored research work to be happy, so whenever management gets a sponsored project, interested faculty members should be given the opportunity to take that project further.

There are only few faculty members who have given importance to F6; this is somehow in contradiction to the earlier literature, where most of the faculty members specifically in the age group of 25-30 years and at the assistant professor level, have ranked F6 as the most important happiness factor.

The study analyzed the various factors which impact academicians’ happiness and found that except for F7, F1 and F2, all other factors are available to academicians according to their ranked importance assigned to them by respondents. This study also obtained a happiness index using matrix and has developed an equation which can be applied to find out the relationship between happiness and performance. This study contributes to the body of literature by applying a customized set of happiness factors on understudied but important respondents, i.e. higher education academicians.

Implications of the study and scope for further research

This study quantified the qualitative aspects by converting the happiness factors thus obtained in to numerical value through which the degree of performance can be ascertained. So, the research findings can help the management to develop effective strategies for keeping academicians happy, thus leading to quality teaching. The results of the study can be further used to find the ability index, opportunity index of the employees and, ultimately, the entire quantification of performance can be done.

Limitations

This study has certain limitations, which should be kept in mind while applying the findings. First, this study has been conducted on academicians working in higher education institutes situated in Delhi/NCR, and thus entails a specific socio-cultural environment that may limit the potential level of generalization.

Reliability tests

Demographic profile of respondents

Source: Primary Data; F1: Research activities, F2: Working environment, F3: Fringe benefits, F4: Personal growth, F5: Job security, F6: Salary, F7: Work–life balance, F8: Social endeavors; * indicates significance at 0.00 level

All India Survey on Higher Education 2016‐17 ( 2017 ), Ministry of Human Resource Development Department of Higher Education , Govt. of India , New Delhi .

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Further reading

Andrew , S.S. ( 2011 ). “ S.M.I.L.E.S.: the differentiating quotient for happiness at work ”, available at: www.happiestminds.com

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Pogue , J. and Lucken , E. ( 2014 ), “ Happiness (or is it really purpose?) at work ”, available at: www.gensleron.com/work/2014/6/5/happiness-or-is-it-really-purpose-at-work.html

Rego , A. and Cunha , M.P. ( 2009 ), “ How individualism and collectivism orientations predict happiness in a collectivistic context? ”, Journal of Happiness Studies , Vol. 10 , pp. 19 - 35 .

Suojanen , I. ( 2012 ), “ Work for your happiness: theoretical and empirical study defining and measuring happiness at work ”, Thesis, University of Turku .

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