Thriving Thesis

A slightly different one today! Below is the write up of my research thesis; the culmination of a year of study on the MSc Sport and Exercise Psychology course at Loughborough University. To summarize, it explores weather "thriving" co-exists across performance domains, specifically between sport and academic study. The paper also explores weather such "cross-domain thriving" can be predicted by an individual's perceived level of psychological resource spillover between the two domains. 

The piece achieved a final grade of 80, ranking it as a distinction. 

Hope you enjoy!

Tom 

30/09/2022

For layout purposes all tables and graphs have been excluded

Abstract

In recent years, thriving in sport has become a topic of significant interest to scholars with an ever increasing number of papers exploring predictive and outcome-based factors. However, there remains little work investigating how thriving in sport impacts subsequent thriving in other non-sport domains, and the mechanisms that would facilitate it. Such a proposition is of interest to the student athlete who seeks to thrive simultaneously in both their sport and studies. Student athletes (n = 59) completed questionnaires assessing thriving in sport and academic study and perceived positive spillover. Inferential statistics revealed significant correlations between sport and academic thriving scores, alongside spillover scores. Multilevel modelling showed spillover to be a significant positive predictor of cross-domain thriving, represented as a general thriving factor. These findings present a novel and important mechanism through which performers can reach a more holistic optimal functioning by striving to thriving across domains.

Introduction

At the turn of the 20th century, the academic study of psychology saw a paradigmatic shift away from pathology-centred research and towards an emphasising of that which is positive within the human experience and an exploration of factors that contribute to optimal human functioning (Seligman & Csikszentmihalyi, 2000). As enquiry into “positive psychology” gathered significant momentum, thriving emerged as a matter of significant interest to scholars. Notably, identifying factors that lead an individual to materialise their inherent drive for self-improvement (Deci & Ryan, 2002) such that they can prosper through developing well.

Within the literature however, thriving as a phenomenon largely lacked consensus in its conceptualisation. Early descriptions centred on mental toughness and stress reactions as key components (Carver 1998; Epel et al., 1998; O’Leary, 1998). Thriving definitions were also contextually variant. For example, in youth development scholarship, thriving is regarded as a concept related to growth and positivity (Arnold & Gagnon, 2019; Lerner et al., 2002; Lerner et al., 2011) whereas in performance domains, thriving concerns achievement and success (Porath et al., 2012; Elahi et al., 2019; Sarkar & Fletcher, 2014). As a result of such inconsistencies, Brown et al. (2017) proposed a more unified definition, conceptualising thriving to be the “joint experience of development and success” (pg 168). Through this definition then, thriving can be thought of as a discrete phenomenon, separate and distinct from seemingly related terms used within the literature (e.g. growth, flourishing or wellness) and can be understood as containing a wellbeing component adjacent to a performance component, for which both must be present for thriving to occur. Ultimately, research investigating the thriving experience is fundamental in addressing what can be regarded as one of the most predominant and significant issues within contemporary sport, that being understanding how to enhance athletes’ performance while concurrently protecting their wellbeing (Arnold & Fletcher, 2021).

Brown et al. (2017) suggests that it is possible to differentiate an individual who thrives from one who does not through self-report measures assessing components of wellbeing and performance to identify individuals who may have thrived. In tandem, McNeil et al. (2018) observed similar thriving structures when assessing coaches’ functioning. Collectively, the results of previous thriving research highlights the feasibility of utilising multiple indicators in assessing functioning in sport.

Equipped with this understanding, researchers have been able to examine potential predictors and consequences of thriving. Speaking on predictors, work by Brown et al. (2017) found the thriving experience to be predicted by satisfaction and frustration of the tenets of basic psychological needs (Deci & Ryan, 2000) where pre-game levels provided the strongest prediction of in-game thriving (Brown et al., 2020). Similarly, psychological skill use, personal resilient qualities and typology of challenge appraisal have been shown to be influential in the experience of thriving in sport (Brown et al., 2018). Turning to the outcomes of thriving, qualitative examination uncovered instances of both positive and negative outcomes of thriving (Brown et al., 2018), thereby putting into question whether an instance of thriving serves to facilitate or hinder future instances of thriving. However, recent longitudinal study has put forward that one instance of thriving can predict instances of thriving later in time, and that such a temporal effect is maintained for up to 28 days (Brown et al., 2021), implying an overall positive effect. Collectively, the aforementioned scholarship conceptualises thriving as a state experience, predicted by factors both trait and state in origin and able to transcend the temporal domain. As such, the natural progression for enquiry into thriving lies in assessing the potential for the thriving experience to permeate between domains whereby an instance of thriving in one domain (e.g. sport or work) influences the capacity to thrive in a second domain (e.g. study or family), whether this be positive or negative.

Such a proposition is of key interest to the student athlete for whom the thriving experience of both development and success must be an ambition in sport and academic study in tandem (Coe-Nesbitt et al., 2021). Speaking on success, performance through competition is a key tenet of the athletic endeavour but is also paramount academically as top grades must be achieved and maintained for scholarships and entry into the top academic (and often athletic) institutions (UCAS, 2020). Turning to development, the mental health of athletes has significantly increased in its salience within public discourse (Parrott et al., 2021, Cassiol & Kluch, 2021; Beauchamp et al., 2021). This highlights a need for deeper understanding in developing performance while simultaneously protecting performers’ wellbeing, particularly for the student athlete who, on average, face significantly greater stressors than their non-athletic peers (Wilson & Pritchard, 2005) due to the combination of educational demands, the pressures of sporting performance and managing relationships beyond sport (Heller, 2004). While student athletes may seek to achieve a state of thriving in multiple domains, scholarly assessment of cross-domain thriving to date remains understudied.

To the researcher’s knowledge, there exists no work examining the potential for the thriving experience to permeate across sport and academic domains. However, application of a psychological resources model may provide theoretical support for such a phenomenon. Resource theory specifies single or multiple individual difference variables that are considered key for optimal management of the demands of life, including self-efficacy (Bandura, 1997), dispositional optimism (Carver & Scheier, 1998), or psychological capital (Luthans & Youssef, 2004). Scholarship in organisational and environmental psychology hint towards the possibility for such psychological resources to spillover between domains (Truelove et al., 2014; Thomas et al., 2016; Thøgersen, 1999; Van der Werff et al., 2013; Barr et al., 2010; Miller et al., 2007; Barnett & Marshall, 1992; Stephens et al., 1997; Calderwood & Gabriel, 2017), resulting in cross-domain effects.

While interest in spillover has grown considerably (Nilsson et al., 2016), there remain multiple definitions conceptualising its effects (Westman et al., 2002; Truelove et al., 2014; Calderwood & Gabriel, 2017; Nash et al., 2017). For example, Nash et al. (2017) defines spillover as an observable and causal effect that one behaviour has on another, noting that the behaviours must be different (not related components of the same behaviour), sequential, and sharing an underlying motive. While this definition outlines the conditions necessary for spillover to occur, only behavioural components of the human experience are outlined. In thriving contexts, both behavioural (success) and mental (development) components must be considered in a spillover definition. Conversely, based on work in examination of stress within the work-family interface (see Bolger et al., 1989; Wethington, 2000), Westman (2002) characterised spillover to be the transmission of states of well-being from one domain to another. Overall, it can be argued that a merging of Nash et al (2017) and Westman’s (2002) definitions is perhaps most appropriate in the current context to most fully encapsulate both the “success” (behavioural) and “development” (mental) components of thriving. Therefore, spillover shall hereon be conceptualised as a causal effect in which behaviour and states of well-being in one domain effect behaviour and states of well-being in a second domain in which behaviours are different, sequential and share and underlying motive. Resource spillover is also considered to be directional such that a domain can be positively, negatively, or indeed not impacted by involvement in a second domain (Thøgersen & Ölander, 2003).

Scholars investigating spillover have presented several mechanisms explaining why spillover occurs with little consensus within the literature regarding which accounts for spillover most wholly. However an understanding of such theories can give deeper comprehension to the potential processes behind cross-domain thriving and drive the field forward. Two main theories have emerged interpreting the systems behind positive spillover. Firstly, cognitive dissonance theory suggests that participating in an action that contradicts an individual’s feelings, ideas, beliefs, and values results in psychological stress (Dawson, 1999) and that through such inconsistency, people change their actions to become consistent thereby reducing any discomfort caused by acting outside one’s established values (Festinger, 1957; 1962). Secondly, role theory can elucidate positive spillover effects. Central to this approach is the assumption that all activities exist within socially defined categories with a set of rights, duties, expectations, norms, and behaviours (Barnett, 2014). Through this role enhancement approach, engaging in multiple roles is considered to be beneficial to well-being, such that participation in one role generates psychological resources thereby making participation in a second role easier, ultimately yielding a variety of sources of stimulation, gratification and social validation (Marks, 1977; Seiber, 1974).

Role theory can also account for presence of negative spillover between domains in which participation in one domains serves unfavourably to a second. Alternative to role enhancement, the role scarcity hypothesis suggests people have limited resources (e.g. time, energy) that various roles draw upon and that the use of resource in one domain limits availability of resource use in a second domain (Edwards et al., 2000; Goode, 1960) thereby leading to negative spillover. Similar theory suggests that roles people participate in can be considered as institutions and that these institutions are inherently greedy making all-encompassing demands upon participants thereby discouraging involvement in other domains or institutions (Gilmore, 1974).

In regards to the absence of spillover, academics propose the possibility for domains to be segmented and functionally independent of one another (Miri-Lavassani & Mocahedi, 2014) meaning spillover cannot occur in any form between the two domains.

In sum, the purpose of the current study is twofold. Firstly, to investigate the potentiality for the thriving experience to transcend domains such that behaviours (success) and states of wellbeing (development) in one domain influence behaviours and states of wellbeing in a second, related domain. Such a proposition is key to student athlete populations who strive to thrive in both sport and academic study and as such shall form the central tenet to this paper. Secondly, to assess a resource spillover model as the theoretical basis in which positive spillover provides a predictive pathway for cross-domain thriving.

Method

Participants

The final participant sample consisted of 58 student athletes (45 male) aged between 18 and 30 (Mage=21.7). Sports represented included: rowing, cricket, rugby, tennis, football, and ultimate frisbee, with an average time competing  of 7.7 years. Most participants had competed to at least the national level.

Procedure

Following ethical approval from Loughborough University, student athletes aged 18 and over were invited to participate in the study via social media channels or direct correspondence with student athletes themselves, coaches or university teams; both online and physical copies of the questionnaire were offered. Participants were initially presented with an information sheet summating the purpose of the study in conjunction with the participants’ ethical rights, including their right to withdraw, anonymity within the study, and assurance of confidentiality. All participants were asked to provide informed consent to continue. In adherence to British Psychological Society guidelines (BPS, 2021), the participant information sheet was piloted on a naïve individual (female, aged 21) to test for suitability and readability of the language. Finally, participants were presented with a questionnaire consisting of four sections.

Measures

Thriving in sport. In order to identify student athletes who were thriving in their sport, participants were asked to provide subjective evaluations of their performance and wellbeing. Performance was assessed through participants’ satisfaction with their sporting performance in the previous month on an 11-point Likert Scale (0 = totally dissatisfied, 10 = totally satisfied). Such an approach is common within the literature (Brown et al., 2018; Pensgaard & Duda, 2003; Levvy et al., 2011; Butt et al., 2003; Edwards & Hardy, 1996). As wellbeing can be conceptualised within a differentiated approach (Ryan et al., 2013), separate measures were utilised to assess hedonic and eudaimonic wellbeing. 

As an indicator of hedonic wellbeing, the positive affect scale from the International Positive and Negative Affect Schedule Short Form (Thompson, 2007) was applied in which participants report the extent to which they had experienced various emotional descriptors (alert, inspired, determined, attentive and active) in their sporting encounters within the previous month on a 5-point Likert scale (1 = never, 5 = always). The Subjective Vitality Scale (Ryan & Fredrick, 1997) was utilised as a measure of participants’ eudaimonic wellbeing over the previous month. Participants were asked to respond to four items on a 6-point Likert Scale (1 = not at all true, 6 = very true). Cronbach’s alpha values for the Positive Affect Scale and the Subjective Vitality Scale were 0.708 and 0.793 respectively. 

Thriving in academic study. To the researcher’s knowledge, there currently exists no measure that serves to differentiate those that thrive in their academic studies from those who do not. As such, the most appropriate method to assess thriving in academic contexts is to adapt the aforementioned thriving in sport scales such that the language relates to participants’ academic, rather than sporting, encounters over the past month. Cronbach’s alpha for the entire scale was 0.872.

Spillover. Literature regarding scale development assessing resource spillover remains slim in sporting and thriving contexts and therefore, scales from related fields of study must be considered. The current study enlists an adapted version of the Multidimensional Scale of Perceived Work-Family Positive Spillover [WFPS] (Hanson et al., 2006), a psychometric tool used frequently in the study of organisational psychology (McNall et al., 2010; Greenhaus & Allen, 2011; Kossek et al., 2012; van Steenbergen et al., 2007). The WFPS assesses participants’ affective, behaviour-based and value-based spillover from work to family and family to work on a 5 point Likert scale (1 = strongly disagree 5 = strongly agree). For use in the current study, phrases relating work and family will be adapted to fit sport and academic study terminology. Cronbach’s alpha values for spillover from sport and from study were 0.902 and 0.916 respectively.

Data Analysis

Data Screening

All analysis was conducted using SPSS 27 (IBM, 2020). Data was initially screened for missing values and subsequently removed through case-wise analysis. To discriminate participants’ levels of thriving and spillover, item-level data were standardised and summated to create proxy thriving and spillover scores such that greater scores indicated a greater degree of thriving and positive spillover, a method previously used in thriving literature (Brown et al., 2021). Values were then grand mean centered, as is often recommended when generating a regression model (Myers et al., 2010) thereby allowing for testing of absolute, between subject effects.

Following data screening, preliminary analysis was conducted to ascertain the strength and significance of the potential relationships between thriving in sport, thriving in academic study, and resource spillover from both domains in form of a correlation matrix. Thereby indicating weather a general thriving factor can be developed through standardisation and summation of thriving scores across sporting and academic domains.

Testing for hierarchical data structures

To ascertain the role and predictive value of spillover in cross-domain thriving, a regression equation can be developed. However, as each participant generated thriving and spillover scores relative to both their sport and academic study, the current data can be considered to be hierarchical such that observation values are nested within the participant. Given thriving scores were correlated across domains, participants’ experience of thriving between sport and study can be considered more similar than it is different meaning the residuals associated with thriving scores of any one person will be closer than the residuals associated with thriving scores from different people, meaning values are not independent. Ignoring this may inadvertently produce biased standard errors thereby producing false-positives or false-negative depending on the nature of the nonindependence (Scariano & Davenport, 1987). Therefore, methods of analysis must be employed that are suitable to such a data structure, such as multilevel modelling. One must first determine, through statistical method, the hierarchical structure of the current data.

One method of determining hierarchical data structure is an Intraclass Correlation Coefficient (ICC) which establishes the degree of resemblance between observations within the same cluster on a scale of 0 to1, where 0 represents perfect independence. Scholars suggest with an ICC value below 0.05, the hierarchical structure of the data can be ignored and traditional regression methods can be used (Hayes, 2006). However it has been argued that an ICC as low as 0.01 can multiply the false positive rate (Musca et al., 2011), where any small non-zero value cannot be taken as an indicator that multilevel modelling is not warranted (Huang, 2018).

In tandem to an ICC value, a Design Effect (DEFF) calculation further informs the potential need for multilevel modelling (Kish, 1965; Muthen & Satorra, 1995). Scholars suggest that with a value <2, the hierarchical structure of the data can be ignored (Peugh, 2010) and traditional regression methods can be used instead. However, more recent work suggests a more appropriate threshold be <1.5 (Lai & Kwok, 2015).

Multi-level modelling

Based on the recommendations of Sommet and Morselli (2021), constrained and augmented intermediate models were created and tested against one another to compare and estimate the residual slope variance and covariance terms (Aguinis et al., 2013), thereby determining appropriateness in retaining or removing the slope residuals in the final model. In testing for model best fit, a likelihood-ratio chi-squared test (LR χ²) was calculated. Interpretation of the LR values varies amongst scholars as to when to retain the slope residuals in the final model. While Bates et al. (2015) suggests models ought be stringent and slope residuals over a 0.05 threshold should be discarded, Barr et al. (2013) propose that models should be maximal and that regardless of p-value, slope residuals should be kept outright. A more nuanced criterion suggests raising the alpha value to 0.20 (Matuschek et al., 2017). A final model can then be built aiming to predict cross-domain thriving from perceived spillover scores.

Results

Data screening and preliminary analysis

After case-wise removal of cases with missing values, the final sample size was n=59 participants culminating a total of n=118 thriving scores. While n ought to be maximised (Lüdtke et al., 2008; Hoyle & Gottfredson, 2015), the current sample size can be considered appropriate as samples <50 can lead to biased estimates of second-level standard errors within multi-level models and is frequently presented as a recommended lower threshold (Maas & Hox 2005; Moineddin et al., 2007).

As data was found to not violate assumptions of normality (W(58) ≥ 0.075, p = 0.200), a Pearson’s correlation was developed, visualised in Table 1. Thriving in sport and thriving in academic study were found to share a moderate, significant correlation (r=0.491, p<0.001), suggesting that those that experienced a greater magnitude of thriving in sport, also experienced greater thriving in their academic study. Spillover scores and thriving in sport scores were, while significant, weakly correlated (r≤0.393, p<0.001), whereas correlations between spillover were moderate (r≤0.545, p<0.001). These initial correlations highlight the potential for the creation of a general thriving factor including thriving scores from both sport and academic study, generated by summating the previously standardised scores. Indeed, spillover in both directions (from sport to study and from study to sport) output moderate, significant correlations with the general thriving factor (r≤ 0.553, p<0.001). Correlations indicate that the data is appropriate to be modelled using spillover score as a predictor for cross-domain thriving. However, the structure of the present data must be considered before equation modelling.

Testing for hierarchical data structures

The current data presents an of ICC=0.625 (see Table 2), meaning there is large homogeneity within clusters (Kreft & Leeuw, 1998) and ~62% of variance in thriving and spillover scores can be attributed to participant differences, ultimately indicating hierarchical structure of the present data.

Furthermore, DEFF = 1.625 which is below the threshold presented. Overall then, the DEFF value, in conjunction with the ICC, strongly suggests a hierarchical structure to the current data and confirms the appropriateness of multilevel modelling.

Multi-level modelling

In the first instance, a constrained model following the structure outlined below:

General Thriving Factorij = B00 + B10 x [Spillover Score]ij + µ0j + eij

Where B00 represents the fixed intercept, B10 the model coefficient estimate, µ0j and eij being the level-2 and level-1 residuals respectively

Secondly, the augmented intermediate model:

General Thriving Factorij = B00 + (B10 + µ1j) x [Spillover Score]ij + µ0j + eij

The augmented model now includes µ1j: the slope residuals

However, the current LR test resulted in a non-significant p-value that exceeds any of the discussed thresholds (LR χ²(1353) = 535.65, p = 1.00), therefore slope residuals ought be discarded in the final model.

In creation of the final model, spillover was found to be a significant positive predictor of the general thriving factor such that those that perceive greater spillover between sport and study relative to the mean, regardless of directionality, are more likely to record a general thriving score above the mean average (B=1.117, SE=0.273, 95% CI [0.561, 1.673], p<0.001). Ultimately the following equation was developed:

General Thriving Factorij = -2.334 + 1.117[Spillover Score] + µij + eij

Overall, the multilevel model confirmed the second hypothesis made, observing a significant effect of spillover on cross-domain thriving whereby the greater spillover recorded by an individual, the greater the likelihood of general thriving. 

Discussion

The aim of the current study was to conduct the first examination of thriving across domains, specifically between sport and academic study and in doing so providing novel insight into the effects of thriving, beyond the singular performance domain. To the researcher’s knowledge, this is the first time thriving has been examined in this capacity. Ultimately, the present results highlight evidence for the occurrence of thriving across domains. Correlations between thriving scores suggest that student athletes thriving in sport are more likely to also thrive in academic contexts meaning success and development in one domain facilitates, rather than precludes, thriving in a second domain.

In addition, positive spillover was found to be a necessary component of cross-domain thriving. Firstly, correlations demonstrated significant relationships between thriving and spillover scores, and that this relationship was not depended on the direction of the spillover. Secondly, a multilevel model demonstrated spillover to be a predictive pathway for the general thriving factor. A similar conclusion has been made in similar spillover-based research in differing scholarly fields (Truelove et al., 2014; Thomas et al., 2016; Thøgersen, 1999; Van der Werff et al., 2013; Barr et al., 2010; Miller et al., 2007; Barnett & Marshall, 1992; Stephens et al., 1997; Calderwood & Gabriel, 2017). Consequently, a resource model can be applied and integrated as the mechanism behind cross-domain thriving whereby thriving across domains can be attributed to either cognitive dissonance (Festinger, 1962) or role enhancement (2014); future research may wish to explore such mechanisms to greater depth.

With growing interest in the experience of thriving in sport (Brown et al., 2021), scholars have been increasingly exploring factors that may precede thriving including basic psychological needs, challenge appraisal, and salivary biomarkers (Brown et al., 2020). The present study provides original evidence relating to novel predictors: thriving in academic study and its spillover into sport, and vice versa. Additionally, the current results provide further proof for the positive outcomes of thriving, in this case being thriving in a second domain after thriving in a preliminary domain. Such conclusions are key to student athlete populations who strive to thrive in their sport in tandem with academic study.

The present study suggests those who attain success and development in their sport spill the gained resources into their academic study ultimately resulting in experiencing thriving in both domains. Such a finding is key to the endeavours of the student-athlete who seek to attain high performance and wellbeing in both sporting and academic domains (Coe-Nesbitt et al., 2021), ultimately resulting in greater stress when compared to other student populations (Wilson & Pritchard, 2005). The present results suggest aiming to attain performance and wellbeing in academic study can be beneficial to sporting performance and wellbeing. Ultimately the current study furthers current understanding as to how to enhance athletes’ performance while concurrently protecting their wellbeing, one of the most significant contemporary issues of sport psychology and indeed sport as a whole (Arnold & Fletcher, 2021).  

However, it is worth noting that any scale utilised to discriminate extent of spillover between participants can only measure perceived spillover in that actual and perceived spillover levels may not be congruent. An individual may perceive to a greater to lesser extent their spillover between domains relative to the actual spillover if recorded by an observer. As such, it is more accurate to state that it is the extent of perceived spillover between domains that holds predictive power regarding cross-domain thriving, in a similar manner to the use of subjective evaluations of success and well-being components in discriminating thriving levels (Brown et al., 2017). Ultimately then, general thriving is predicted by the degree of an individual’s perception as to how the two domains positively influence each other. Although cross-domain thriving has been evidenced between sport and academic study, one can begin to consider the potential applicability to other domains.

While the foci of the current study centred on thriving across sporting and academic contexts in a student athlete population, questions begin to arise concerning other domains through which thriving may permeate as a result of spillover effects, and how such domains need be related for such effects to occur. Indeed, spillover is thought to occur when behaviours across domains are different, sequential, and sharing an underlying motive (Nash et al., 2017), however these conditions are not thriving specific and therefore the nuanced criteria regarding the similarity between domains required for cross-domain thriving remain unknown. Future research may wish to explore domains between which cross-domain thriving can occur, as well as the specific factors and conditions that allow for spillover in thriving contexts.

Notwithstanding these findings, it is prudent to acknowledge limitations within the current study. Firstly, the scales employed to assess spillover were only capable of discriminating the degree to which participants experienced positive spillover. Therefore, the role of potential negative spillover in thriving across domains remains untested and unknown despite being a key component of resource theory (Thøgersen & Ölander, 2003). Future research may wish to address this and include negative spillover scores when modelling cross-domain thriving.

Further issues relate to the sample used within the present study. While the sample size (n=59) can be considered appropriate for multilevel modelling (Maas & Hox 2005; Moineddin et al., 2007), scholars also recommend that n is maximised to assure enough information is gathered and that estimates of group level measures remain reliable (Lüdtke et al., 2008; Hoyle & Gottfredson, 2015). Although a greater sample size would be desirable, the timing of data collection precluded the ability to maximise sample size primarily due to the questionnaire having been distrusted at the end of many sporting seasons and after academic examinations. Consequently, many potential participants had not competed athletically or been assessed in their studies within the previous month, thereby impeding any longitudinal thriving effects (Brown et al., 2021) and therefore not meeting inclusion criteria. Ultimately, a more desirable sample size could be achieved through consideration of sporting and academic seasons.

Further sampling issues lie in the gender of participants as the current sample presented a significant male bias, a common theme plaguing previous thriving literature (Brown et al., 2017). It therefore remains unclear the specific impact gender may have on the thriving experience and how it impacts present results. Future research would do well to address this disparity.

To conclude, the present study provides original insight into the positive effects of thriving, demonstrating the construct’s potentiality to transcend related domains; specifically between sporting and academic contexts in a student athlete population. Such cross-domain thriving was demonstrated within multilevel modelling to be predicted by the extent of positive spillover an individual perceives between the two domains.