Scand J Med Sci Sports 2014: 24: 461–468 doi: 10.1111/sms.12002
© 2012 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
The influence of birth date and place of development on youth sport participation J. Turnnidge, D. J. Hancock, J. Côté School of Kinesiology and Health Studies, Queen’s University, Kingston, Ontario, Canada Corresponding author: Jennifer Turnnidge, MSc, School of Kinesiology and Health Studies, Queen’s University, Kingston, ON K7L 3N6, Canada. Tel: +613-533-6000 ext. 78207, Fax: +613 533 2009, E-mail:
[email protected] Accepted for publication 13 August 2012
Previous research highlights the critical role that contextual factors play in shaping athlete development. The purpose of the present study was to investigate two of these contextual factors: birth date (known as the relative age effect, RAE) and city of development as determinants of participation in a sample of youth ice hockey players. The sample included 146 424 athletes registered with Ontario youth ice hockey between the 2004 and 2010 seasons. Chi-square statistics determined a significant
RAE in youth ice hockey. Findings also revealed a significant association between small cities of development and increased youth ice hockey participation. Finally, there was no evidence of an interaction between relative age and city of development. The characteristics of smaller communities that may facilitate sport participation across all youth are discussed, along with recommendations for future research.
A growing body of literature highlights the critical role that an individual’s early sport environment plays in facilitating athletic development. Indeed, evidence exists to suggest that contextual factors relating to young athletes’ initial exposure to sport may have an important impact on both their continued sport participation and their chances for attaining athletic success (Côté et al., 2007; MacDonald et al., 2009). Two contextual variables that have consistently been found to influence athlete development are the relative age effect (RAE) and the size of the city in which an athlete develops. The RAE refers to the relationship that exists between an individual’s month of birth relative to their peers and their attainment of sport expertise [see Cobley et al. (2009) and Musch and Grondin (2001) for detailed reviews]. In an effort to provide developmentally appropriate instruction and to promote fair competition, many sports place children into annual age groups based on specific cut-off dates. Unfortunately, while this organizational policy is often well-intentioned, the age disparities within these annual age bands can have significant consequences for athlete development (Cobley et al., 2009). Specifically, athletes who are relatively older than their peers (i.e., are born closer to the cut-off date for their annual age group classifications) are frequently overrepresented at elite levels of competition, while relatively younger athletes are underrepresented. The RAE is prevalent in a number of different sports at the professional and elite levels, including baseball (Thompson et al., 1991; Côté et al., 2006), soccer (Barnsley et al.,
1992; Hirose, 2009), and ice hockey (Sherar et al., 2007; Bruner et al., 2011). Explanations that have been offered to account for the RAE tend to center on maturational differences between young athletes (Bruner et al., 2011). It has been suggested that athletes who are relatively older may be mistakenly identified as “more talented” than their peers when, in reality, they may simply be more physically and cognitively mature than their younger teammates owing to their chronological age (Helsen et al., 2005). As a result of this misidentification, older athletes may receive more attention from their coaches and increased playing time (Starkes, 2000; Delorme & Raspaud, 2009). These factors may subsequently lead to enhanced skill development and an increased likelihood of being selected for higher levels of competition during development (Starkes, 2000; Davids & Baker, 2007; Sherar et al., 2007). Moreover, this misidentification process can ultimately contribute to, or detract from, youth’s athletic success and continued sport commitment (e.g., Barnsley & Thompson, 1988; Helsen et al., 1998). Although previous studies have helped illuminate the possible mechanisms and implications of RAEs, the existing literature has primarily focused on replicating RAE trends across different sports, competitive levels, and cultural contexts. Limited research, however, has examined RAEs in relation to other contextual factors that may affect athlete development. Given the important implications that RAEs can have for athlete development, further research exploring how this phenomenon
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Turnnidge et al. may interact with other developmental factors to influence both participation and performance in youth sport is warranted. One example of another environmental factor that may influence athletes’ early sport experiences is the size of the city where an athlete develops. Stemming from the work of Curtis and Birch (1987) and Carlson (1988), several researchers have examined the association between the size of the city in which an individual is born and the development of sport expertise, commonly referred to as the birthplace effect (e.g., Côté et al., 2006; Schorer et al., 2010). Studies have investigated this relationship at both the professional and the amateur levels from sports, including football, basketball, ice hockey, and handball. In general, results in North America and Australia indicate that athletes born in cities of fewer than 500 000 are systematically overrepresented at the elite levels and athletes born in cities with populations over 500 000 are systematically underrepresented. For example, in their analysis of professional leagues, Côté et al. (2006) found that the best odds of becoming a professional athlete in the United States were for cities with populations between 50 000 and 100 000. However, discrepancies regarding the optimal city size for development have been found across different sports and different countries (Baker et al., 2009). Studies examining the birthplace effect propose that the developmental opportunities that exist in smaller cities may be more salient to expert athlete development in comparison to the opportunities that exist in larger cities (e.g., Côté et al., 2006; MacDonald et al., 2009). Some of the possible benefits that have been suggested to be present in smaller towns and cities include (a) increased access to spaces supporting sport play and practice opportunities (MacDonald et al., 2009); (b) an integrated approach to sport participation that involves families, schools, and communities (Lidor et al., 2010); and (c) enhanced social support (Davids & Baker, 2007). It is important to note, however, that these benefits are largely speculative in nature and have yet to be comprehensively tested. Alternatively, some of the contextual features of larger cities may be less conducive to expert athlete development and sport participation in comparison to those of smaller cities. According to social exchange theory (Thibaut & Kelley, 1959), the decision to continue an activity can depend on the availability or attractiveness of alternative activities. Previous research has employed this theoretical framework in conjunction with the sport commitment model (e.g., Scanlan et al., 1993) to demonstrate that the attractiveness of competing activities is negatively related to sport commitment and participation (e.g., Guillet et al., 2002). These findings are also in line with qualitative sport drop out studies, which indicate that interest in other activities is a consistently cited motive for sport withdrawal (Gould, 1987). Given that larger cities may offer a greater variety of alternative
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leisure-time activities, such as arts and music (Curtis & McPherson, 1987), it is possible that young athletes may drop out of sport in favor of these activities. One previous study that investigated the influence of city size at the developmental level was conducted by Fraser-Thomas et al. (2010). This study explored the relationships among city size, personal development, and drop out in competitive swimming. Results from this study suggested that the odds of dropping out of sport significantly increased for athletes that practiced their sport in larger cities. Furthermore, results indicated that athletes who practiced their sport in smaller cities scored significantly higher on certain aspects of personal development. Therefore, these findings suggest that city size may not only influence performance, but participation and personal development as well. In most previous birthplace studies, the population of an athlete’s birthplace has served as a proxy for the city where an athlete spent their formative developmental years and was first socialized into sport (Côté et al., 2006; Bruner et al., 2011). While this may often be the case, it is possible that one’s birthplace does not always coincide with one’s place of development. For example, individuals from small rural areas may move to larger urban centers during their childhood or vice versa. Because of the possible incongruence between these locations, Schorer et al. (2010) recently proposed that the location of an athlete’s first club may be a more appropriate measure of an athlete’s early developmental context. Consequently, Bruner et al. (2011) suggested that studies investigating birthplace effects using a more detailed proxy of athletes’ place of development would be beneficial. Moreover, as the majority of birthplace studies have examined birthplace effects at the professional and elite levels of competition, research exploring this phenomenon at the developmental level is warranted. Given that birthplace effects and RAEs are contextual factors that have been linked with the attainment of athletic success, it is possible that these factors may interact in contributing to athlete development. Support for exploring a potential relationship between these two factors can be drawn from both theoretical and applied research. In their extensive review of the possible mechanisms that may facilitate RAEs, Musch and Grondin (2001) proposed that competition is a necessary condition for RAEs to occur. More specifically, they indicated that as the depth of competition in a given sport increases, there will also be an increased likelihood of RAEs. Conversely, in sports where little or no competition occurs, the likelihood of RAEs is decreased. This suggestion has received some empirical support in both youth ice hockey (Grondin et al., 1984) and youth soccer (Helsen et al., 1998). These findings are compatible with the hypothesis that where there is a limited depth of competition, such as in small communities, RAEs may be alleviated.
Contextual factors and sport participation A second theory that may lend support to this view is Barker’s “theory of behavior settings” (Barker, 1978), which suggests that the number of individuals in a particular setting will influence an individual’s behavior. Barker (1978) proposed that in situations with fewer participants (underpopulated) than the optimal number needed, individuals will partake in a greater variety of roles, put forth a greater effort, and experience higher levels of success and failure. Applying this theory to the sport context, teams in smaller communities may be more likely to be underpopulated and may thus foster beneficial developmental opportunities. This hypothesis is complemented by the recent work of Balish and Côté (2011), which examined how one small, successful sporting community (population: 646) facilitated athlete talent development. Results indicated that this small community provided its young athletes with ample access to recreational areas where they engaged in large amounts of unorganized, youth-led sport activities. In addition, athletes reported that their community afforded them the opportunity to participate in a variety of different sports. In light of these findings, it may be possible that small communities are a more salient context for athlete development as they provide the type of developmental experiences and practice/play opportunities that are known to be associated with athletic success (Côté et al., 2007). Interestingly, although these theories suggest that there may be a possible interaction between these two contextual factors, previous studies have failed to find empirical support for this interaction. Côté et al. (2006) explored the proposed relationship between RAEs and birthplace effects in their analysis of a sample of professional male athletes in ice hockey, basketball, baseball, and golf. Results indicated that city size was not statistically related to the RAE, thus suggesting that birthplace and birth date effects are independent of one another. Similarly, in their examination of World Junior ice hockey players from four countries, Bruner et al. (2011) found no evidence of an interaction between RAE and birthplace. Despite these consistent results, the studies have limited generalizability as they focused on elite, rather than developmental, athletes. Moreover, the results of both studies were possibly restricted by the use of birthplace as a proxy for where the athletes spent their developmental years. Therefore, the purpose of this study was to build upon and extend previous research by examining the RAE and city of development as contextual influences in a sample of developmental ice hockey players. Using a player registration database, we sampled 146 424 youth athletes who were registered with the Ontario Hockey Federation (OHF) during the 2004–2010 seasons. With this sample, our specific objectives were to investigate (a) the relationship between birth date and youth ice hockey participation; (b) the relationship between city of development and youth ice hockey participation; and (c) the
interaction among birth date, city of development, and youth ice hockey participation. Based upon previous research, it was hypothesized that (a) RAEs will be present in the sample; (b) youth ice hockey players will be more likely to come from cities between 1000 and 500 000; and (c) there will be no interaction between birth date and city of development in influencing youth ice hockey participation. Method Participants Participants were male, youth ice hockey players from Ontario, the most populous Canadian province. These athletes were registered with the OHF during the 2004–2010 seasons. The athletes ranged from 8 to 16 years of age and their competitive levels ranged from recreational to competitive. In general, the recreational level was characterized by teams that tended to play against teams in their own city, whereas the competitive level was characterized by teams that often traveled to other cities for their competitions.
Data collection The researchers were permitted access to an anonymous database maintained by the OHF that included all registered players during the 2004–2010 seasons. The database included players’ birth date, gender, division, competitive standard, and the name of their youth ice hockey association. The authors extracted all athletes born between the years of 1994–2001 from the database and organized them into birth date quartiles, which reflected the OHF selection year that has a cut-off date of January 1. Beginning with the older quartiles: Q1 = January 1–March 31, Q2 = April 1–June 30, Q3 = July 1–September 30, and Q4 = October 1–December 31. Players born in the first and second quartiles (January–June) were considered to be relatively older than their peers, and players born in the third and fourth quartiles (July–December) were considered to be relatively younger than their peers. The athletes’ city of development was determined by the youth ice hockey association with which each athlete registered during their first season in the database. While it is possible that the city of first registration within the database was not necessarily the location of where the athletes were first introduced into sport, it is felt that this construct represents a more detailed measure of the athletes’ developmental environment in comparison to birthplace. Based on the registration data, the population size of each participant’s city of development was retrieved from a combination of Statistics Canada data (http: // www12.statcan.gc.ca/census - recensement / 2006 / dp-pd / prof/92-591/index.cfm) and two population websites (http:// citypopulation.de/index.html, http://www.world-gazetteer.com), as well as personal correspondence with local officials. As the database reflected athletes’ participation during the 2004–2010 seasons, 2006 census and demographic data (i.e., distribution of males between the ages of 5–19) were used to estimate city sizes and population distributions at the time when the players were first registered.
Data analysis To evaluate possible RAEs in youth ice hockey in Ontario, frequencies of birth dates in each quartile were calculated. Chisquare goodness-of-fit tests were then conducted to ascertain if the observed frequency of youth ice hockey participants in a given quartile deviated significantly from the expected frequency for that quartile. Consistent with previous RAE research, expected frequencies were based on the assumption of an equal distribution of
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Turnnidge et al. Results Birth date
births across each quarter of the year (i.e., 25%). The chi-square w effect size statistic was also evaluated to determine the strength of the RAE. For the chi-square values that were statistically significant, post-hoc tests (standardized residuals; Hancock et al., 2011) were conducted to establish which quartiles differed from the anticipated distribution. To assess the association between city of development and ice hockey participation, the cities of first registration (within the database) of the youth ice hockey players were compared with the distribution of males in the general population using data from the 2006 Canadian census. Rather than compare our sample to the entire Canadian population, we were able to narrow the census data down to cities in the province of Ontario. Furthermore, we narrowed our focus to the number of males aged 5–19 years in those cities. Males 5–19 years of age were selected as they were representative of the participants’ developmental years in youth ice hockey. Similar to previous studies, the distribution of males was segmented into nine population categories: (a) 0–999; (b) 1000–4999; (c) 5000–9999; (d) 10 000–24 999; (e) 25 000–49 999; (f) 50 000–99 999; (g) 100 000–249 999; (h) 250 000–499 999; and (i) >500 000. Odds ratios (OR) were then calculated across the different city size categories. These ratios provided a likelihood estimate of participating in youth ice hockey in each community size, while accounting for the general population distributions. Ninety-five percent confidence intervals (CIs) were also calculated around each OR. Ratios greater than 1 (with upper and lower limits above 1) indicated that an individual developing in a given city size was more likely to participate in youth ice hockey than expected. Conversely, ratios less than 1 (with upper and lower limits below 1) indicated that an individual developing in a given city size was less likely to participate in youth ice hockey than expected. An OR that included the null value of 1 within their CI range was considered to be statistically insignificant. The final step of the analysis was to inspect the interaction between birth date (using the birth quartiles) and city of development (using the nine city size categories) as determinants of youth ice hockey participation. As both variables were categorical, we accomplished this by conducting a 4 (birth quartile) ¥ 9 (city size category) chi-square analysis. All analyses were conducted using SPSS Version 20 (SPSS Inc., Chicago, IL, USA).
Table 1 provides the percentage distributions of the players’ birth months and the results of the chi-square analysis. Overall, there was a significant RAE [c2 (3, n = 146 424) = 1000.34, P < 0.01, w = 0.08). The chi-square w statistic reflected a small effect. It is important to note that the observed RAE varied from the traditional RAE found in ice hockey. In contrast to previous studies, results revealed that players born in the first quartile were underrepresented. According to the posthoc tests, however, this finding was not statistically significant. Consistent with previous research, results indicated that players born in the second and third quartiles of the year were significantly overrepresented. Finally, as expected, players born in the fourth quartile were significantly underrepresented. City of development Table 2 illustrates the results of the analysis performed to evaluate the relationship between city of development and ice hockey participation. Data are presented on the percentage of males between the ages of 5 and 19 that lived in Ontario communities of different sizes from the 2006 Canadian census and the percentage of youth ice hockey players in the OHF that registered in these different areas. Table 2 also contains the OR of participating in youth ice hockey for each city size category. The results indicate that cities with populations below 100 000 all showed OR higher than 1, whereas cities with populations over 100 000 provided OR lower than 1. This finding suggests that smaller cities produced significantly more youth ice hockey participants than
Table 1. Chi-square statistics, effect sizes, relative age percentages, and standardized residuals
n
146 424
c2
1000.34
w
0.08
Relative age percentages
Standardized residuals
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
24.92
27.76
25.37
21.95
-0.63
21.14*
2.84*
-23.35*
*P < 0.01. Table 2. Odds ratios (ORs) and confidence intervals (CIs) across city size categories
City size
ONT pop (%)*
OHF pop (%)†
OR
CI
>500 000 250 000–499 999 100 000–249 999 50 000–99 999 25 000–49 999 10 000–24 999 5000–9999 1000–4999 0–999
34.35 9.18 20.19 10.63 6.45 10.56 5.26 2.62 0.76
23.37 6.42 19.49 11.44 9.32 11.43 6.53 10.91 1.07
0.58 0.68 0.96 1.09 1.49 1.09 1.26 4.57 1.41
0.58–0.59 0.67–0.69 0.94–0.97 1.07–1.10 1.46–1.52 1.07–1.11 1.23–1.29 4.48–4.65 1.34–1.49
*Percentage of males between the ages of 5 and 19 in Ontario in each of the subdivisions of the 2006 Canadian census. † Percentage of players born between 1994 and 2001 registered in the Ontario Hockey Federation (OHF) in each of the subdivisions of the Canadian census.
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Contextual factors and sport participation expected, while larger cities produced significantly fewer youth ice hockey participants than expected. Finally, the results revealed that the city size category with the lowest odds of ice hockey participations was cities with populations greater than 500 000, whereas the city size category with the highest odds of ice hockey participation was cities with populations between 1000 and 5000.
sample included athletes that ranged from 8 to 16 years of age within the database. Previous research suggests that RAEs generally start to decrease around ages 16/17 (Cobley et al., 2009), and thus, it is possible that the inclusion of the older portion of our sample may have skewed the RAE. Nonetheless, as it is unclear what specifically contributed to the inconsistent RAE, it would be worthwhile for future studies to further explore RAEs in relation to youth sport participation.
Interaction between birth date and city of development Finally, in an effort to examine the possible interaction between birth date and city of development, we further evaluated these two factors using a chi-square analysis. Results of the analysis were nonsignificant [c2 (2, n = 24) = 23.20, P > 0.05]. This finding suggests that there is no significant interaction between the two variables (birth quartile and city size category). Discussion There are three main findings from this study. First, we observed an RAE for youth ice hockey participation in Ontario. Second, there was a general trend for youth ice hockey participants to be registered in smaller cities (less than 100 000). Third, there was no significant interaction between birth quartile and city size category. Birth date The results revealed a significant RAE for youth ice hockey participation. As expected, second and third quartile athletes were significantly overrepresented, and fourth quartile athletes were significantly underrepresented. This finding is congruent with our hypothesis and previous reviews (e.g., Musch & Grondin, 2001; Cobley et al., 2009) and provides further evidence that birth date can have an important influence on youth’s sport participation. Interestingly, however, the RAE in this study also showed some inconsistencies with previous RAE research. Specifically, the results indicated that athletes born in the first quartile were underrepresented among youth ice hockey participants, as opposed to being overrepresented as expected. There are two possible explanations for this atypical RAE. First, our sample included both competitive and recreational ice hockey participants. Previous research suggests that RAEs in ice hockey may be moderated by competitive level and that RAEs may strengthen as competitive level increases (Hancock et al., 2011). As recreational ice hockey represents a lower competitive standard, it is possible that the mechanisms known to facilitate RAEs may be less salient in this context. As such, the inclusion of recreational players may have lessened the advantage for Q1 athletes. However, as the focus of this study was on participation in youth ice hockey at all levels, the inclusion of recreational athletes was necessary. Second, our
City of development The results highlighted a general trend for more youth ice hockey players to be registered in cities with populations of less than 100 000. More specifically, the data indicated that youth developing in communities with population fewer than 100 000 were significantly more likely to participate in youth ice hockey compared to youth developing in communities with population greater than 100 000. This finding lends support to the view that smaller cities may provide a more suitable context for athlete development than larger communities. While previous studies have primarily focused on the influence of community size in relation to elite athlete development (e.g., Carlson, 1988; Côté et al., 2006; MacDonald et al., 2009), the present results add to the existing literature by suggesting that community size may also affect participation in youth sport. Interestingly, the results indicate that cities with populations less than 1000 produced significantly more youth ice hockey participants than expected. This differs from Côté et al.’s (2006) finding that rural areas of this size produced significantly fewer professional players than expected. The authors suggested that populated areas of less than 1000 may lack the facilities or playing partners common to larger city sizes, which may be detrimental to elite athlete development. By examining this suggestion in conjunction with the results of the present study, it is possible that while rural areas may not be a suitable context for professional athlete development, they may have a positive influence on youth sport participation. The notion that smaller community sizes may positively contribute to sport participation complements previous work by Fraser-Thomas et al. (2010), which revealed that the odds of dropping out of sport significantly increased for athletes that practiced their sport in larger cities. Indeed, after controlling for age, gender, and developmental assets, athletes who dropped out of sport were almost five times more likely to come from larger cities. Moreover, the authors found that athletes from smaller communities scored significantly higher on three personal development categories: support, boundaries and expectations, and commitment to learning. Thus, it is possible that smaller communities may be more likely to both produce elite athletes and to develop
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Turnnidge et al. “well-rounded” youth that are more likely to stay involved in sport. Balish and Côté (2011) echoed this sentiment as they posited that small communities may possess characteristics that are conducive to athlete development. In their in-depth case study of a small, successful sporting community, the authors found that the community provided its athletes with the opportunity to participate in a variety of organized sports. This is congruent with Barker’s (1978) proposition that individuals in underpopulated settings, such as smaller communities, may have an opportunity to engage in a greater number of roles. Additionally, results from Balish and Côté (2011) indicated that youth in this community engaged in large amounts of youth-led unorganized play. Given that sampling and deliberate play are both factors known to be associated with athletic success (Côté et al., 2007), these findings suggest that small communities may create positive environments that foster continued sport involvement. Interpreting the results of these studies in light of the present research, it might be argued that smaller cities provide developmental experiences that positively influence youth’s participation in sport. Thus, it is important to identify what it is about the sport environments in smaller cities that differentiate them from the sport environments in larger cities. In 2002, the National Research Council and Institute of Medicine (2002) identified eight contextual setting features that can facilitate positive development in youth. These setting features included (a) physical and psychological safety; (b) appropriate structure; (c) supportive relationships; (d) an opportunity to belong; (e) positive social norms; (f) support of efficacy and mattering; (g) opportunities for skill building; and (h) integration of family, school, and community. Fortunately, evidence exists to suggest that smaller cities may help to cultivate some of these features in their sport environments (e.g., Kyttä, 2002; Bale, 2003; Fraser-Thomas et al., 2010; Balish & Côté, 2011). In line with the first setting feature, Kyttä (2002) demonstrated that smaller cities can provide youth with safer environments and greater access to spaces that are suitable for unorganized physical activity (i.e., cycling, playing sports). Furthermore, previous research (e.g., Côté et al., 2007; MacDonald et al., 2009; FraserThomas et al., 2010) posited that smaller communities may afford youth more opportunities to develop close interpersonal relationships and enhanced integration within the community (the third and eighth setting feature). Finally, Balish and Côté (2011) and Bale (2003) argued that sports in small towns may foster a strong sense of community. This creation of a sense of community lends itself to establishment of opportunities to belong (the fourth setting feature). Given the variety of ways in which smaller communities can embody the NRCIM’s (2002) eight setting features, these findings reinforce the idea that smaller cities may facilitate youth
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sport participation. However, future research testing this hypothesis is warranted. Finally, although the results of this study indicate that smaller cities may offer environments that are more salient to youth sport participation, it is also important to recognize the possible benefits that larger communities may provide their athletes. Previous retrospective studies with elite athletes demonstrate that athletes may move to larger cities in order to train with better coaches in better training facilities (e.g., Law et al., 2007; Phillips et al., 2010). However, these findings also indicate that this shift to larger communities tends to occur once an athlete has chosen to specialize in a given sport. Consequently, smaller cities may still foster environments that are conducive to youth sport participation and expert athlete development during young athletes’ early developmental years. Furthermore, while larger cities may provide important resources for elite athletes, it is also possible that the wide variety of leisure activities available to youth in larger cities may be linked to youth’s withdrawal from sport (Thibaut & Kelley, 1959). It would thus be worthwhile for future studies to explore the association between alternative activities and city size, and its potential influence on youth’s sport participation. Birth date and city of development A unique aspect of this study was its investigation of the interaction between birth date and city of development with regard to youth sport participation. The results revealed no significant interaction between the two variables. This result confirms previous research (Côté et al., 2006; Bruner et al., 2011), which found no interaction between relative age and birthplace with professional athletes and extends this literature by suggesting that there is also no interaction with developmental athletes. In doing so, this finding provides evidence that birth date and city of development are independent predictors of youths’ sport participation. By drawing upon Barker’s (1978) theory, one possible explanation for this finding may be that smaller cities represent an underpopulated setting that may positively influence the sport participation of all athletes, irrespective of their relative age. Consequently, the impact of RAEs may be lessened and the subsequent disadvantage for Q4 athletes may be alleviated in smaller communities. However, as this was the first evaluation of the possible interaction between RAE and city of development in youth sport, further studies explaining this lack of association are needed. Perspectives The results of the present study suggest that both birth date (RAE) and city of development are important determinants of youth sport participation. It was also found that birth date and city of development are independent predictors of participation in sport. This complements
Contextual factors and sport participation the work of Bruner et al. (2011) and Côté et al. (2006) which similarly found no interaction between birth date and birthplace. Furthermore, the results lend support to the notion that the sport environments in smaller cities may positively influence youths’ performance, participation, and personal development. Indeed, communities that provide youth with opportunities to play and participate in a variety of sports can help lay the foundation for athlete development. In addition, sport programs that wish to increase the quality of their athletes’ sport experiences should consider incorporating the eight setting features into their own sport environments. Overall, this study advanced our understanding regarding how birth date and city of development may influence youth’s sport participation. This study adds to the growing body of literature, which suggests that contextual factors are important determinants of youth’s sport experiences. Moreover, the results lend support to the proposition that smaller communities may foster a more salient context for youth sport participation. However, future qualitative and observational studies are needed to explore the possible mechanisms underlying
these contextual factors. Such studies may also shed light on possible confounding factors, such as socioeconomic status, that could influence the associations between contextual factors and youth sport participation. Furthermore, studies examining these relationships from sociological perspectives may enhance our understanding of the present results. Finally, future research should aim to extend the generalizability of the current findings by examining whether the influence of birth date and city of development on youth sport participation holds across age, gender, competitive level, country, and different sport types. Key words: youth sport participation, relative age effect, community size, athlete development.
Acknowledgements The authors would like to thank Bill Pearce, Jeffrey Moon, Melissa Wolk, and the OHF for their assistance with this project. Preparation of this manuscript was supported by the Social Sciences and Humanities Research Council of Canada standard research grant (No. 410-2011-0472).
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