The effect birth date has on choosing to study a sports related course at an educational institution

I declare that this is my own work and should this declaration be found to be untrue I

acknowledge that I may be guilty of committing an academic offence.

SPE001-3 Dissertation

The Effect Birth Date Has On Choosing To Study A Sports Related Course At An Educational Institution 26th April 2013

Carl Page (1008889) [email protected] Mr. D Pears

mailto:[email protected]


Carl Page (1008889) Page 1 BSc (Hons) Sports Science and Coaching

Contents

Abstract ...................................................................................................................... 2

Introduction ................................................................................................................ 3

Review of Literature ................................................................................................... 5

Methods ................................................................................................................... 14

Results ..................................................................................................................... 20

Discussion ................................................................................................................ 26

Conclusion ............................................................................................................... 37

References ............................................................................................................... 39

Appendices .............................................................................................................. 51



Abstract

Page, C.G. and Pears, D. (2013) The Effect Birth Date Has On Choosing To Study A

Sports Related Course At An Educational Institution.

Purpose: The Relative Age Effect (RAE) in the academic study of sport. The aim of

this study was to investigate the relationship of those students being born on a

certain date will indeed influence the decision to study a sports related course at an

educational institution.

Methods: Contacted various levels of educational institutions and requested student

information from sport related academic courses such as their date of birth, gender

and course title. The gatekeeper and the data managers securely passed the

relevant information requested. Organised and arranged data collection into three

birth date month groups; September-December, January-April and May-August

within appropriate statistical software and spreadsheet software.

Results: Statistical analysis was determined where appropriate using Chi-Square

tests and formulas. The main findings show there to be significant results in the birth

dates of an individual and studying a sports academic course as with the distribution

of births across all levels of education were not equally distributed throughout the

year. The observed frequencies showed the birth date is highest for the months of

September-December and lowest for the spring and summer months. It also showed

an over populace representation of male students compared to females who are

studying a sport related academic course at an educational institution.

Conclusions: Therefore it can be derived from the results presented that the birth

date did have a large influence on choosing to study a sports related course at an

educational institution.

Key Words: Relative Age Effect, birth date grouping, gender bias, sports academia.



Introduction

A number of things are under our control, while others are not under our control, for

instance our own birth date is something we cannot control by ourselves. The issue

of when we are first required to start compulsory schooling at an educational

institution in England has received considerable critical attention. It is possible that

two friends with birthdays merely days apart can be placed into different year groups.

Subsequently this may have an effect on your own life when placed and labelled as

being the youngest in the year group. Although our friend shall be identified as the

oldest in their year group, in which they have been situated in.

In recent years, there has been an increasing interest in the start dates for an

educational institution within England. Normally this occurs during the September

month apart from Universities which start in early October. An individual can decide

to participate in studying a sport related course at an educational institution at

various ages i.e. from thirteen onwards. However, a major problem with this kind of

application is the maturity of the individuals can possibly differ since their ages may

influence choosing to study a sports related academic course.

Throughout this paper the term RAE will refer to/will be used to refer to Relative Age

Effect. Currently a considerable amount of literature has been published on the so-

called ‘birth date effects’ or Relative Age Effect. These studies explain the bias that

is evident within individual youth team sport and within an educational setting. The

participation is typically higher between people who are born in the early appropriate

selection phase and have not attained an adequate understanding of the normal

distribution of live births. There is an increasing concern that there is a smaller



number amongst those who are born in the later selection phase being

disadvantaged. This concept has recently been challenged by other researchers’

studies demonstrating it does not apply to all sports at different levels of performance

and genders.

Hence Reilly, (2010) demonstrated the Relative Age Effect occurrence has been

observed in scientific visionary, and achievements academically within the primary

schools and entry into universities. However it is still unclear if this advantage is

simply from more experience which is linked with the earlier birth date. Van den

Honert, (2012) argues that his study does not support Reilly’s, (2010) view since it

was discovered there is a smaller amount of competition for places. Though it is

worth mentioning it was the opposite for senior elite level athletes having a

significant effect. Also it might have been the case for males yet findings indicate

there is an actual statistical significant Relative Age Effect linking of junior male

players with falling of the effect as they increase with age.

Some researchers (e.g. Ford and Williams, 2011) have attempted to draw fine

distinctions between there being no Relative Age Effect in the birth dates of award-

winning athletes in male professional team sports. Although they had expected

normal frequencies of birth dates to be equal across four equal groups and have no

over-representation of younger or fairly older performers. Nevertheless their

investigations also showed that performers are more likely to be born late in the

selection year when compared to those earlier in the year. As opposed to most

investigations into the Relative Age Effect in competitive sports and educational

success has been explored for instance by Larouch et al. (2010) who proposed it is



still unclear whether the RAE occurs in the recreational sports and leisure activities

too. Therefore it has been shown the Relative Age Effect not being as dominant

within the leisure physical activities through adulthood when compared to

competitive sport.

Review of Literature

The statute (Great Britain. Education Act 1996) established that:

A person begins to be of compulsory school age— a. when he attains the age of five, if he attains that age on a prescribed day, and b. otherwise at the beginning of the prescribed day next following his attaining

that age.

Bedford Borough Council (2013) advises the school year in which the children will

become five they are generally permitted to begin in or during the Reception year. In

particular this means children will start attending their first formally compulsory

educational institution either in September or the term in which they reach the age of

five. Nevertheless it is dependent upon the current course of action in place within

the child’s Local Education Authority. This is supported by Central Bedfordshire

Council (2012) which recommends children to be normally given entry to an

educational institution in the month of September following their fifth birthday. This is

known as the start of the reception year within an educational institution.

Consequently children who have not reached the legal school age will not have to

commence being in an educational institution, until they are five years of age.

Furthermore in northern European countries it is known for children to start their

education aged six or seven. Nonetheless in England it is different since they begin



at a younger age. According to, Cocozza, (2011) claims the positives of being born

in August students have the advantage of being able to leave school, college and

university before everyone else as they have not had to be at school for as many

months as the older students. Specifically this result means that summer birth

students get up to two fewer terms at an educational institution as opposed to their

fellow students who were born in the autumn and who started at the educational

institution in September.

Elsewhere, Paton (2012) has argued that compulsory education ought to be

postponed until a child reaches the age of six. He believes there is a current

increased importance on the three-Rs; reading, ’riting (writing), and ’rithmetic

(arithmetic). As a consequence during the early years of a child who is intelligent this

can result in significant long lasting harm. Therefore an alternative option is to have

the initial start date delayed by at least twelve months since this will then let infants

progress more naturally.

As Alger, (2004) reminds us investigators use numerous terms that focus on the

same subject when examining the distribution of birth dates. Specifically, this author

found that the term ‘Relative Age Effect’ does not appear in some studies since they

decide to use other terminologies which mean the same thing. For instance; relative

age bias, birth date effects, birth date bias and season-of-birth effects.

To determine the influences of the Relative Age Effect Wolstencroft, (2005)

compared selections of young performers in teams. This major study concluded a



bias towards young performers who are physically mature and consequently are

chosen for various sports, e.g. football, rugby and tennis but the opposite applies for

gymnastics. It has been recognised that the selection bias can be advantageous to

those performers being successful in sport. Other authors (see Simmons and

Geoffrey, 2001) point out that this selection bias for age-category in team sports is

prevalent due to the importance of winning at any cost.

Figure 1 illustrates how ultimately the other young performers get closer to their early

developed educational group; this ensures understanding that they were not extra

talented when in fact only had matured at a faster rate and are classified as being

unexpected “Non-Achievers”. As a result it would seem the requirement of being

selected for a sports team is related to the performer’s own birth date.

Figure 1. Performance and selection (cf. Hohmann & Carl. 2002, p. 10) In Chun, J. (2007) Identifying Highly Talented Athletes: Conception and Design of an Expert System: Specific to Track and Field. Norderstedt: GRIN Verlag

Evans, (2012) draws our attention to the fact that more students are choosing

practical courses e.g. food safety and music academic qualification over GCSEs.

This decline could be because of the size of the year groups and/or schools



increasing the opportunities to take a vocational qualification. This is supported by

Groby Community College (2012) which mentions that there is an option for students

to gain a vocational qualification such as the BTEC Level 2 First Award in Sport or

Dance while studying core Physical Education.

Male domination which is the topic for numerous sociological literatures formed the

central focus of a study by Laker, (2002) in which the author found there is a gender

bias in participation in sport and Physical Education. Plus it has been argued that the

educational system is simply part of mixing children into society and equally Physical

Education and sport plays a part in this. Hence the equality of opportunity is stated in

the National Curriculum, there are recognisable components which are evidently

unbalanced.

In another major study, the Royal Statistical Society Centre for Statistical Education

(2004) found that GCSE Dance is a low chosen subject where it is mainly girl

students who choose this subject. On the other hand it was revealed that in years

10-11 there was nearly twice as many boys when compared to girls who decided to

study PE and Games as a GCSE. However BBC Learning Parents (2012)

uses examples that particular subjects are compulsory at GCSE level whereas

others are known as ‘entitlement areas’ when informing parents. Furthermore they

recommend it is better for students to study a range of subjects offered for increased

career options and choices to study later on in their life. Consequently, it would seem

it’s dependent on the school and their provision of subjects by either allowing

students to study GCSE’s and/or vocational qualifications.



The relationship differences in genders and grades have been widely investigated

(Roberts & Fairclough 2012, cited in Burgess, McConnell, Propper & Wilson, (2004).

In their piece of research it analysed the gender difference within England and

concluded that girls usually do better than boys in educational achievements. Equally

recognised by BBC News (2008) announces that while 60.7% of September-born

girls and 50.3% of September-born boys achieved five good GCSEs grades A* to C.

As opposed to, 55.2% of August-born girls and 44.2% of August-born boys did.

Therefore this suggests those students who are the youngest in the year shall be

placed behind their older peers even up to the age of 16. Collectively this would

suggest more support and encouragement needs to be given at an earlier stage to

those students born in the summer months to counter balance the disadvantage of

being born later in the year compared to their class peers.

Curtis’s comparative study (2009) found that summer-born babies are less expected

to obtain the grades at GCSEs and A-levels which consequently hinder them from

progressing onto university. A further investigation from Sedghi, (2011) believes

specifically those children who were born in the month of August are more likely to

underperform in an educational institution when compared to those children who are

born in September. A recent study by Shepherd, (2011) publicised shows August

babies are less likely to go onto top universities as the study showed they are more

likely to study vocational courses. This would imply there is an under-achievement

across those students who are born in the summer month of August and influences

them succeeding at an educational institution.



Although Bell et al. (2009) reported the effects of the birth date effect in assessments

of sporting performance. This is continually being questioned by results of public

examinations, Physical Education teachers and the development of sporting talent.

Furthermore Deming and Dynarski (2008) described the ethical concerns for

investigators and methodological issues related to the age-at-test effects on test

results being robust and explicit. In particular they found that the cognitive

development of infants can produce large variations.

Furthermore Morris and Nevill (2006) exposes there is evidence to suggest the

Relative Age Effect exists in some sports which significantly sways the opportunities

for those wanting to reach the high-level of the sporting pyramid. Collectively this

would suggest from the observation that there is an over-representation from being

born early in the selection year in both junior and senior elite teams with what might

be expected since it is centred on the national birth rates.

This view is supported by Nakata and Sakamoto (2012) who points out that only in

the female sport of volleyball there was a significant Relative Age Effect. Whilst the

effect for males was evidenced in more sports such as baseball, soccer and track

and field, hence aiding to the factors of RAEs in sports could change between males

and females. Although some investigators reports showed that in women’s elite

performers of football produced mixed findings and it has since been revealed there

is no significant Relative Age Effect to be found involving youth players.



Despite this Chun, (2007) gave evidence for the possible difference involving young

performers real age and their biological age. The Matthew Effect as seen in Figure 2

below even occurs whenever class peers compare each other generally being much

faster, taller and more muscular.

Figure 2. The Matthew Effect. (Adapted from Tucker & Dugas, 2009)

As Oakley, (2012 p.1) puts it:

Children born immediately after the cut-off point in their specific sport or country are usually bigger and therefore are more likely to be picked in their early years leading to a cumulative advantage.

Grade IV player 10 on 1st day of year turning

11 in that year.

Jan – April birth e.g. 10 years 11 months at

school start.

Oct – Dec birth e.g. 10 years 1 month at school

start.

Up to 12 month difference in maturity. Stronger, faster,

improved co-ordination and decision-making ability.

Difference in ability at school level.

At ages younger than 12, ability is a poor guide to

talent & capacity.

Higher fall-out rate in first three years – self-removal

from talent pool.

Selection into squads

Better coaching More playing time Better competition & team

mates



Oakley clearly sees that the children who are born in September will have the

advantage over August born children since they will have approximately a whole

additional year which allows them to further grow and develop. Specifically, it is

intensified with the child’s early stages of puberty, as they are capable of spurting up

in one year.

Although Simmons, (2001a) establishes the season-of-birth bias is evident when

observed within both the educational and sport sectors in England. This is supported

by Simmons, (2001b) who reports that there is considerable data on the findings of

age-bias in educational year groups showing the season-of-birth bias is not limited to

sport but in school education too. Therefore it would seem the advantages of school

attainment are presented from the season of birth bias in an educational institution

for particularly the older students within each year.

Nonetheless the possible explanations as to why this occurs is mentioned by

Roberts and Fairclough, (2012) who observed the supporting staff being

inexperienced in the Relative Age Effect. Equally Verachtert et al. (2010) suggested

that autumn-born students should remain in a teacher’s classroom with the younger

students. This is because they possibly will under-rate the stages of development in

the younger students. Moreover the difference in gender achievements at GCSE has

revealed certain perspectives from students about teachers who perceive they are

more easy-going and understanding with girls rather than with students who are

boys (Younger and Warrington, 1996). Based on this they then suggest the

consequences are significant when involving the interaction of teachers and the



students and is dependent on the student’s gender and their birth date period and

end-of-year attainment levels in sport and education across all levels.

Whilst the reasoning for this is from an investigation which discovered students who

are born between the months of June-August are more likely to be identified with

behaviour challenges and/or moderate learning difficulties (Roberts & Fairclough

2012, cited in Wilson, (2000). However the phrase “red-shirting” was initially utilised

to explain the practice of retaining college athletes out of play while waiting for them

to grow bigger and stronger. Other studies show that in sport this has a long-lasting

competitive advantage for those athletes (Deming and Dynarski, 2008). Collectively

this proposes more investigations are needed to be carried out at all levels of the

educational ladder in which teachers/coaches should become experienced in

identifying the Relative Age Effect within their group of performers/students.

Consequently through directing Physical Education teachers through the Relative

Age Effect this will go away as soon as everybody is mature. In addition typically

those in youth sport who are named as the least mature ones are the youngest. On

the other hand it attributes the older athletes being known as the better ones and

more mature (Thomas et al. 2008). As a result the Relative Age Effect (RAE) is used

to describe a bias, evident in youth sport and academia. This is where involvement is

greater between those born early in the appropriate selection phase and

correspondingly lowers between those born late in the selection phase. This would

then be expected from the normalised distribution of live births.



Although extensive research has been carried out on birth dates, no single study

exists which has examined if there is a double relationship between the students’

birth date with the Relative Age Effect in the academic study of sport. The key

research question of this study was thus whether or not those students being born

on a certain date will indeed influence the decision to study a sports related

academic course at an educational institution. Another question is establishing

whether there are any other factors such as the effect of a student’s gender, culture,

and social class as to why they had selected a specialist sports educational

institution too.

Methods

This study was granted ethical approval in agreement with the guidelines of the

Research Ethics Committee of University of Bedfordshire. The participants were

given an information sheet for guidance about the study taking place as shown in

appendix 1.

Plus for the duration of the tests the data was made to follow the Declaration of

Helsinki which is a set of ethical principles for the medical community regarding

human experimentation. Also this is largely considered to be the foundation

document of human research ethics. Likewise the study was processed in

accordance with The Nuremberg Code, in paper or electronic form, and must ensure

that these are kept confidential and appropriately destroyed or deleted after use.



The sample group chosen for this research is comprised of students who study a

sports related course from various levels of educational institutions and are split into

three categories with both genders. The method of data collection for this involved

contacting a range of neighbouring educational institutions that run sport courses this

can be seen in appendix 2. The researcher was aware of the need for anonymity for

the students and organisation (educational institutions), confidentiality of data, and

the need for a professional approach during the investigation to minimise any

potential risk to participants

Therefore the gatekeepers and the data managers of the educational institutions

accessed and securely passed the relevant information about the individuals such as

their date of birth, gender and the course title the students are studying. Thus all

parties involved were notified that this process was to be kept strictly confidential

(subject to legal limitations) and follow the Data Protection Act’s 8 key principles.

Hence the data generated by the study will be retained in accordance with the

University's policy on Academic Integrity. The data generated in the course of the

research is kept securely in paper or electronic form for a period of ten years after

the completion of a research project.

Furthermore the next phase involved collective organised data collection into

statistical software and spreadsheet software. The use of IBM SPSS Statistics 19

(SPSS, Inc., Chicago IL, USA) determined statistical analysis where appropriate.

Plus exploring the relationships between variables within this study followed the

Inferential Statistical Decision Tree (Corston and Colman 2000) as shown in



appendix 3. This involved the tallying of data into frequencies which form a number

of variables such as the expected and observed, subsequently either used Chi-

Square tests for comparing to theoretical distribution or been tested for association.

Plus with the use of spreadsheet software Microsoft Excel 2010 (Office Home and

Student 2010 for Windows Vista, Microsoft Corporation, Redmond WA, USA) was

used to calculate significant differences within the data collected through a range of

formulas, filtering, frequency and pivot tables. The spreadsheet processing computer

program allows numerical data to be inputted and displayed in cells of a worksheet.

These rows and columns then have hidden formulas which are able to carry out

arithmetic on the visible data. Thus altering the contents of a particular cell will result

in a program updated result in other specified cells.

Nonetheless the controlled variables within this structured independent study of

those who are studying a particular sports related course are being kept the same

throughout the experiments. Therefore the Independent Variable (IV) is the students

being born on a certain date, for instance in January-April, May-August and

September-December. In addition the Dependent Variable (DV) is specifically the

frequency of students choosing to study sports related courses at an educational

institution.

The significant difference was set at p ≥ 0.05. The data is presented with a 95%

confidence interval. The expected and observed birth months for those studying

sports related academic course at an educational institution was examined using



Chi-Square tests. The test worked by observing comparisons of frequencies in

addition to the frequencies that would be expected if the statistical independence

and the null hypothesis of no association were true. Moreover the researcher was

capable of calculating the likely frequency for each cell in the contingency

table/crosstabulation through presuming each of the variables was independent.

The first group started in September and finished in December, the second was from

January-April and the last was from May-August.

Following similar investigations from Nakata & Sakamoto (2012) the Chi-Squared

tests were appropriate to the birth dates in line with the four quarters which assist

measuring the statistical significance of deviations for the expected number of births

in all the quarters. This is supported by Easton & Mc Coll (2012) who specifies that

to determine whether there is any relationships between the data sample involved

comparing two attributes with the process of Chi-Squared Test of Association.

The formulae and functions which are arithmetic operations +, -, ^, * and % to be

used in the spreadsheet. VLookup; searches for a value in the first column of a table

and returns a value in the same row from another column in the table array. CountIf;

it counts the number of cells within a range that meet the given criteria. Minimum;

this will be used to calculate the lowest recorded amount. Maximum; this was

employed to calculate the largest amount number.

Average; this will be used to calculate the average of a group of numbers by adding

each member of the group and dividing the total by the number of members. Total,

the sum of several amounts added or considered together. Range, is the highest



minus the lowest value. Interquartile Range is a measure of the spread of a group of

values equal to the difference between the upper limit for the lower quarter and the

lower limit for the upper quarter. While Excel Frequency Tables are applied to work

out the ratio of that number to the total results obtained.

Furthermore the Pivot Table report automatically extracted and summarised the

data. This consequently allowed for reported analysis of the data to make

comparisons of trends. Equally with the Pivot Chart report when this was simply

carried out to discover any relationships or patterns within the data set. Therefore in

comparing the Pivot Table report and Pivot Chart report this allowed the researcher

to compare any significant information in the study.

The Pivot Table report is used since it is an interactive method to swiftly sum up

large quantities of information in a very user-friendly manner. Likewise when altering

the rows to columns or columns to rows of the data will allow the researcher to

concentrate on the information that they want or need. Additionally by means of

filtering, categorisation, alignment and conditional format tools to the cells this allows

for specific pieces of information to be presented. Consequently the spreadsheet

Pivot Table showed how many students are at a particular level of studying a sports

related course at an educational institution. Plus the researcher can easily indicate

which month the student’s birth dates are the most common out of the data sets.

Additionally it is shown there to be poor relationship between the observed and

expected frequencies. Plus rejected the null hypothesis of independence/no

association occurs during the Chi-Squared Test of Association is too big of a value



for the test statistic (Easton & Mc Coll, 2012). Auto filtering; this is used so that it

displays only a part of a list the researcher requires. As by providing the specific

criteria in the spreadsheet then Excel will display only the data that match the

criteria. Filtering is especially useful when having a large amount of data and the

need to work with only a small part of the data in the list.

Microsoft includes safety features to help protect the documents from numerous

possible unsafe actions, for instance current versions of the spreadsheet software

include security measures intended to maintain the computer safety. Therefore

password protected spreadsheet offers safety measure points and macro virus

avoidance. These are the essential safeguards accessible in Excel. This stops

unauthorised staff and other individuals being able to access the researcher’s data.

However it is through electronic communication the data becomes legalised, yet the

researcher will be accountable for the security of the data. Also identified were

sufficient protection actions for each stage of information being used like password

protection or encryption of the document. Therefore with password protection the

researcher’s Excel worksheets prevented others from tampering with the data. The

easiest methods are to password protect the entire sheet or word document. Also

the encryption document increased the security since through restricted permissions

being only granted people could access data while restricting their ability to edit,

copy and print the data.



In addition the Excel Advanced Filter was applied to take out a record of distinctive

items in the database. For instance, the document files of students from the specific

spreadsheet and those from the same birth date range. Furthermore utilising the

AutoFilter feature to filter data; this is a quick and easy way to find and work with a

subset of data in a range of cells or table. To filter data in a range of cells or table,

you can either reapply a filter to get up-to-date results, or clear a filter to redisplay all

of the data. Plus the search technique was applied for displaying individual records

which was dependent on the criteria being entered. Along with it presents the

matched criteria of students across the entire spreadsheet for analysing particular

results.

Results

Chi-Square tests were used to analyse the relationship between the observed and

expected frequencies of students birth months studying a sport related academic

course at an educational institution. Data was collected from a range of educational

institutions with a total of nine thousand three hundred and twenty students from

sport related academic courses (N = 9320). Also used a significance level of p = 0.05

level for all statistical tests. The breakdown of those students decision to study a

sports related course at GCSE BTEC Level 1 and 2 (N = 123), A-Level courses

includes BTEC’s at Level 3 (N = 344) and Undergraduate courses such as

Foundation Degrees, Bachelor of Arts with Honours and Bachelor of Science

Honours (N = 8533).



70%

30%

Male Female

The total sample group distribution of males (N = 6522) and females (N = 2798).

This can be seen in Figure 3 below and compares the experimental data on genders

studying a sport related academic course at an educational institution with an over

populace representation of male students compared to females.

Figure 3. The total distribution of genders studying a sport related academic course at an educational institution.

Also, Chi-Square tests revealed in the crosstabulation for analyses the whole sample

frequencies of all students’ birth months in each third period based on the

assumption of an unequal distribution across the trifold. As in Table 3 this can be

seen in appendix 4 and shows there is a significant difference, X2 (3, N = 9320) =

17185.29, p < 0.05 indicated between the three groups. The relationship between

these variables was significant so agrees with the hypothesis as it occurs with the

distribution of births across all levels of education is not equally distributed

throughout the year.

The results obtained from the preliminary analysis of the whole samples of the

observed frequencies it would appear that the birth date is highest for the months of



3107 3107 3107

3269

3007 3044

2850

2900

2950

3000

3050

3100

3150

3200

3250

3300

September-December January-April May-August

Fre

qu

en

cy o

f st

ud

en

ts

Birth Date Month Groups

Expected Observed Linear (Expected )

September-December and lowest for the spring and summer months as shown in

Figure 4.

Figure 4. The total distribution of student’s birth dates studying a sport related academic course at educational institutions.

Further Chi-Square tests revealed in appendix 5 for the frequencies of

undergraduate students’ birth months in each third period based on the assumption

of an unequal distribution across the trifold. The statistical results, X2 (3, N = 8853) =

16402.47, p < 0.05, is significant with the participation of students on undergraduate

courses and the distribution of their birth dates are not being distributed as expected

throughout the year.

Figure 5 presents the intercorrelations among the observed frequency is higher

amongst those born early in September and is also the starting date for the

educational institution and correspondingly in comparison lower between those

students who were born later in the year. The pie chart in Figure 6 shows the

breakdown of undergraduates, females (N = 2714) in contrast to a higher percentage

of male undergraduate students (N = 6139).



2951 2951 2951

3110

2860 2883

2700

2750

2800

2850

2900

2950

3000

3050

3100

3150


Fre

qu

en

cy o

f st

ud

en

ts



Figure 5. The distribution of student’s birth dates who are studying a sport related academic course at undergraduate level.

Figure 6. A higher percentage of undergraduate’s who are male compared to female students in the academic study of sport.

There was a similarity of results when using Chi-Square tests. This was used in

examining the A-Level courses which include BTEC’s at Level 3 expected and

observed frequencies being significant in appendix 6. The statistical results, X2 (3, N

= 344) = 620.92, p < 0.05, specifies data from observed frequencies of A-level

students in Figure 7 can be compared with the data in undergraduate students

Figure 5 which is consistent with the hypothesis that the September-December

69%

31%

Male Female



115 115 115

125

106 113

95

100

105

110

115

120

125

130


Fre

qu

en

cy o

f st

ud

en

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80%

20%

Male Female

group reported significantly more students than the other two birth date month

groups.

Figure 7. The distribution of student’s live births who are studying an A-level course.

From the graph below in Figure 8 we can see that the breakdown of genders there is

a higher distribution of males (N = 276) when compared to females (N = 68).

Figure 8. The distribution of more male students studying an A-level sports related course when compared to female students.

The single most striking observation to emerge from the data comparison was that

the observed frequencies is highest for the months of May-August and lower for the

educational institutions start date and in the winter months from students studying a

GCSE at BTEC Level 1 and 2 at an educational institution (Figure 9).



41 41 41 34

41 48

0

10

20

30

40

50

60


Fre

qu

en

cy o

f st

ud

en

ts



87%

13%

Male Female

Figure 9. Observed and Expected frequencies in September-December, January-April and May-August groups at GCSE BTEC Level 1 and 2 courses.

This is inconsistent with the hypothesis as presented in the contingency table of

appendix 7. The statistical results, X2 (3, N = 123) = 175.29, p > 0.05, revealed to

accept the null hypothesis since there is significant difference between the

distribution of births not being equally distributed throughout the year. It is apparent

from the Figure 10 that very few females study a sports related academic course

even at GCSE level, males (N = 107) and females (N = 16).

Figure 10. Higher male distribution of students studying a sports related course at BTEC Level 1 and 2 (GCSE).



Discussion

The present study was designed to determine the effect of those students being born

on a certain date will indeed influence the decision to study a sports related course

at an educational institution. As in reviewing the literature, no data was found on the

association between the Relative Age Effect (RAE) in the academic study of sport.

The main findings show there to be significant results in the birth dates of an

individual and studying a sports academic course as with the distribution of births

across all levels of education were not equally distributed throughout the year.

The results of this study did show that higher percentages of students were born in

the months of September-December and lower percentages in the spring and

summer months which it is consistent with the set hypothesis. The results of this

study show/indicate that an over populace representation of male students

compared to females who are studying a sport related academic course at an

educational institution. Contrary to expectations, this study did not find a significant

difference between the observed frequencies and expected frequencies from GCSE

BTEC Level 1 and 2.

The crosstabulation is identified as a helpful tool in assessing the statistical

significance in the relationship involving the categories of two or more variables.

Hence Virginia Polytechnic Institute and State University (1999) informs that if there

is a large quantity of statistical instances, the Chi-Square returned a value which is of

great importance even when there is not that much of a correlation. Consequently

they were able to effectively calculate through the Chi-Square the observed and

expected frequencies for each birth date month group.



Whilst Northern Illinois University Department of Biological Sciences (2004) test

results will match the expectations more closely. Although even when the researcher

carries out statistical analysis properly which is based on the suitable hypothesis the

distribution of data can become very one-sided. As a result there are two ways in

which the researcher is able to obtain a large high Chi-Square value or an unfamiliar

result from the right theory or secondly it may be a consequence of using the wrong

theory. Hence both are interchangeable since the data is always able to be

separated between being the truth and false with a 100% confidence.

This study confirms that birth dates in the earlier months is associated with a biased

selection (Wilson, 1998). As a result of the Chi-Square tests representing the

observed distribution of birth dates, by month is contrasted considerably from the

expected frequencies. In the same way Jiménez and Pain (2008) highlighted

specifically in Spain, even though their selection period for education usually starts in

January the Relative Age Effect exists since this then influences the development

amongst the students of the oldest and the youngest in a group being different

physically, emotionally and academically.

However there are discrepancies between the data sets of students studying sport

related academic courses at educational institutions. This is because there is a much

larger sample size of students from undergraduate courses. Consequently Radford

University (2002) show larger differences occur between the expected and actual

data which generate an increased Chi-Square value. Along with the increased Chi-



Square value there is a greater likelihood of there really being a significant

difference.

Specifically, with the smaller sample size for those students studying GCSE’s which

can be either at BTEC Level 1 or Level 2 courses; caution must be applied, as the

findings might not be transferable to the Relative Age Effect. The reason for this is

not clear but Berk and Carey (2010) discussed the validity of the Chi-Square test

with small frequencies. Specifically the authors found that researchers could face

problems when applying the Pearson Chi-Square test on a table which has a large

amount of empty cells and it might not be valid to do so. Cells with few inputted

amounts can be problematic as this means a researcher is required to have a bigger

sample size for the Pearson Chi-Square test to function properly.

One of the issues that emerge from these findings is that various conditions for the

application of the Chi-Square test need to be carried out before it can be fully

functional (Kothari, 2004). For instance the input of observed frequencies should be

generated from a random basis. Hence most statisticians believe the group of items

must contain no less than 10 items as some regard this to be better than 5. Whilst it

is recognised that a typical sample size study should be at least 50, since this total

quantity of items is needed for increasing the size of analysis into the effects.

In contrast Howell, (2010) reports that in numerous previous investigations there

appears to be no issues around the definition of large being known as "at least 5".

Following the analysis of contingency tables, his report found that a researcher could

make use of the normal Pearson Chi-Square test even when the expected cell



frequencies are moderate. Consequently the constraints have to be linear as each of

the items within the sample is independent.

Furthermore a smaller sample size was generated of students from educational

institutions who study a sport related academic course at A-Level and includes

BTEC’s at Level 3. Ford and Williams (2011) investigation had a low sample size

when contrasted against the previous literature into the Relative Age Effect. This is

supported by Baker et al. (2009) whose study encountered sampling issues when

evaluating the effects of other linear studies. Therefore it would seem that sample

size is connected to the analyses of how much of an influence can be discovered

from analysing an individual’s date of birth.

Additionally it is shown there to be a poor relationship between the observed and

expected frequencies of GCSE BTEC Level 1 and 2 sports related academic

courses. Therefore with the observed frequencies in the birth date month groups

they did not match the expected frequencies; whereby adapting Sharp, (2004)

suggests this could be due to the influences of the sample size being too small or the

incorrect birth date was received from the contacted educational institutions.

There are, however, other possible explanations specifically Easton & Mc Coll,

(2012) indicates this occurs when the researcher rejects the null hypothesis of

independence / no association. This is because the Chi-Squared Test of Association

is too big of a value for the test statistic. Similarly Glynn, (2000) testifies the Chi-

Square test will become unreliable once the researcher’s table includes cells with the

expected frequencies of below 5. Therefore different statistical procedures may be



used to better determine if statistical significance occurs from deviations for the

expected frequency number of births within the particular sample size groups.

This inconsistency/discrepancy may be due to the fact that the researcher is only

able to make the interpretation of the Chi-Square test as it does not identify the

relationship within the data (Heavey, 2011). Equally DiMaria, (2007) warns that the

Chi-Square statistic will not present clear results so the researchers are required to

consider if a relationship exists. As a consequence Franke et al. (2012) recognised

researchers will be more likely to either wrongly read or over interpret the results of

Chi-Square tests. This practice influences their reports which could include

inadequate or no backing from the statistical analyses which has been carried out as

part of the research.

Verachtert et al. (2011) addressed the issue of limited longitudinal investigations on

the continuation of the season of birth effect over time. They mainly used cross-

sectional data instead from various cohorts. Specifically their study was able to

signify the difference amongst children who are born in the first and the fourth

quarter lessened significantly. On the other hand Baker et al. (2009) discovered the

role of date of birth can either promote or limit an individual’s prospects for improved

achievement and developing skills. Consequently with the current investigations

season of birth and school success in the early years of primary education in which

other factors such as using the growth curve modelling to analyses the longitudinal

data into the Relative Age Effect.



Edgar and O’Donoghue (2005) identified limitations to the study which needed to

take into account that the researchers must be familiar with age subgroups, gender,

area and gender for homogeneity to larger groups of people. Hence this distribution

of larger groups of people will help overcome problems of alterations in the

population season of birth distributions concerning the different calendar years the

students were born in. Likewise the researcher needs to take into consideration the

different amount of days there are in every month of the year.

Sykes et al. (2009) expresses concern that the Relative Age Effect lowers through

time but continues to be significant at GCSE, A-level and into university. Rosenbaum

(2013) discovered the likelihood of becoming a university student for either Oxford or

Cambridge is clearly affected by date of birth. Collectively this suggests the Relative

Age Effect is already present for students who are academically strong and are

currently applying to Oxbridge. The combination of findings provides some support

for the conceptual evidence that the Relative Age Effect is present in sports

academia. Consequently in future investigations it might be possible to have a

different start date in an educational institution and whether universities should begin

to consider offering applicants places dependent on their dates of birth.

Additionally Crawford, et al. (2007) evidenced recently qualified teachers are

inexperienced in the impact relative age has on test results. Collectively this

suggests the issue has to be more recognised so that with students of different ages

the lessons can be adapted to suit their needs. Therefore the results mean that

teachers could be mislabelling the students born in the summer months as having

special education needs. This may just be that the older class peers are basically



further developed emotionally and cognitively to those students born later in the year

(Sykes et al. 2009).

These results are consistent with those of other studies and suggest that the

Relative Age Effect is advantageous for the students born during the beginning of

the academic year when matched with the other students who are born in the later

months (Cobley et al. 2008). Furthermore Georgakis et al. (2011) learnt the Relative

Age Effect influences a student’s physical education accomplishments as well as

their representation in school sport competitions.

The data must be interpreted with caution because the researcher is unable to

identify from the obtained results which students have possibly either skipped or

been held back a year. Subsequently this creates issues to the observed age and

the expected age of starting at an educational institution to study a sports related

course (Crawford, et al. 2007). Some of the issues emerging from the findings relate

specifically to students who are the youngest within their year group at an

educational institution and will be more likely on average to do considerably poorer in

terms of educational achievement.

Deaner et al. (2013) revealed the negative impact the Relative Age Effect has on

relatively younger individuals which is evidenced in various educational and sporting

backgrounds. Smith, (2007) discovers through the National Curriculum Physical

Education (NCPE) in years 10 and 11 for 15—16 year olds the involvement in

diverse sports and physical activities changed significantly and differently according

to the gender, the school, specialist Sports College and if the student was studying



GCSE PE. Sykes and colleagues in 2009 believe when trying to solve the issue of

the birth date effect this will not happen by using a common start date for an

educational institution. Nonetheless Crawford, et al. (2007) proposed the starting age

at an educational institution needs to be studied and be flexible. Consequently

nurseries would be available as a substitute option to full-time education.

On the other hand Bono and Galindo-Rueda (2004) presents Local Education

Authorities are responsible for the implementation of the start dates for compulsory

primary educational institutions. Furthermore Local Education Authorities would also

be responsible if they decided to defer the entry date. This would be more of an

exception than a possibility.

Cobley, et al. (2008) encountered multiple circumstances of the Relative Age Effect

in school Physical Education and sport environments with the present selection,

assessment and age-grouping approaches. Similarly trying to compare the evidence

of Relative Age Effect in other educational institutions from around the world can be

difficult. For instance variables like the differences in start date of the school year,

legalised compulsory education and whether there is ability grouping in early years

(Sykes et al. 2009).

For instance Laker, (2002) noticed that there is a gender bias in America; therefore a

law was made in 1972 to allow equal opportunities for women in sport. Plus this

effected women’s sport in education establishments trying to reflect the male varsity

sports arrangement. However in some social circles it is considered inappropriate for

women to participate in sport, for instance it is believed a woman’s role was to look



after the children and were physiologically not suited to sport performance and

physical activity because of the perceptual differences in abilities.

Grenet, (2010) exposes a number of problems which exist in institutional education

systems which possibly can be responsible for the birth effects carrying on into

adulthood through influential differentiated educational trajectories. Specifically they

found men have a lower probability of possessing an academic A-level or above if

born late in the year. However Sykes et al. (2009) believed it is very important to

recognise that only a number of summer-born students really get to the highest level

of education. This effect has been insufficiently investigated within all levels of

education. Therefore researchers would have to examine if this is one of the reasons

why the summer birth students are currently not continuing with education especially

right up to university level.

Bono and Galindo-Rueda (2004) reveals the present qualification system in England

challenges students with the key intermediate level assessments which normally are

taken at the end of the summer educational term. However the differences in school

leaving dates from birth date of students in the same classroom this generates a

significant incoherence in the educational achievement. This is because it indicates a

minor variance of two to three months of education. Although it is interesting to note

that those students who attended a specialist school it was often found to their

grades increased at GCSE level (Levačić and Jenkins, 2006).

Whilst Crawford, et al. (2007) suggested former investigations possibly limited the

importance of their findings which contained small sample sizes. Also there needs to



be further investigations into the expected accomplishments of August born students

when contrasted against the cohort. This is because a student may just necessarily

be developing at a slower rate when compared against fellow students. Therefore it

assures parents that their child is not special needs. However Cobley (2009)

exposes more students from the later birth months of January to August were

recognised as having special educational needs from the referrals for learning

support.

It is encouraging to compare this with that found by Zubero et al. (2008) who found

that there is a relationship between the birth date and entering the university. The

study examined the effect is possibly from the difference of maturity between the

teenage students who were born in the months of January and December. Besides,

when at being at a mature student and it is possible to achieve better grades. Since

this difference has not been found elsewhere it is probably not due to the

socialisation influences but differences of being through the multifaceted relations

involving the individual’s biological and maturational existing between both genders

(Vincent and Glamser, 2006). For that reason there are possible shortcomings

related to being born later in the year and should be of particular focus.

The results may be explained by the fact that evidenced from Bell and Daniels

(1990) birth date effect exists in the performance even for students aged 11, 13 and

15. Whereby it is seen less so in the older age when compared to the younger age

group. This also accords with Zubero et al. (2008) observations, which showed that

students who are roughly older by a single year are more skilled at playing sports.



Subsequently this may possibly increase inspiration to take an interest in sport

along with developing self-esteem.

Van Wersch et al. (1992) identified at the age of 14 a girls' interested in Physical

Education was significantly lower than that of boys but was found to be the opposite

in the younger age groups. This finding corroborates the ideas of Evans et al. (1997),

who suggested that more needs to be done to aid promotion of getting female

students attracted into Physical Education and sport. This applies equally to primary

and secondary educational institutions with reference to the National Curriculum PE

(NCPE) which will prepare them for their future careers in leisure and/or paid

employment. The possible difference of gender cannot be ruled out since Dexter

(1999) evidenced the effect of gender varied across schools. Yet also found there to

be stability across schools of students’ educational ability on sports knowledge as

well as their sport performance.

Additionally Simmons, (2001a) found a significant contribution of those born

September-December lead to a higher percentage of being chosen for selection

against those born between the months of May and August. It may be that these

students benefitted from being older and being recognised as “talented” this is

because of their physiological/psychological advantages which the students have

over their “younger” cohort (Helsen et al. 2005). Likewise Simmons, (2001b)

reasoned that it is mainly from the September-December born individuals enjoying

dominance in the earlier years amongst their cohort.



Conclusion

This dissertation has investigated the double relationship between the students’ birth

dates with the Relative Age Effect in the academic study of sport. Therefore

returning to the hypothesis/question posed at the beginning of this study, it is now

possible to state that birth date has a large influence on the decision to study a

sports related course at an educational institution. The results of this study also

indicate that elements of Relative Age Effect (RAE) in the academic study of sport

may vary amongst male and female students.

These findings suggest that in general the birth dates of an individual and studying

sport academic course at all levels of education were not equally distributed. One of

the more significant findings to emerge from this study is that the observed

frequencies show the birth date is highest for the months of September-December

and lowest for January-April and May-August months from all data sets apart from

those at GCSE BTEC Level 1 and 2. The study has gone some way towards

enhancing our understanding of the Relative Age Effect (RAE) in the academic study

of sport.

In general, therefore, it seems that it confirms previous findings of the birth date

effect and contributes additional evidence that suggests there is an over populace

representation of male students compared to females who are studying a sport

related academic course at an educational institution. However the study was

limited in several ways. First, the dissertation used a convenience sample that

grouped subjects together from the same educational level to increase the data size.



Although the reader should bear in mind that the study was based on a small sample

for GCSE students at BTEC Level 1 and 2 due to data protection and would be

deemed unethical practice in terms of students giving consent for use of data.

Secondly the researcher was unable to establish whether there are any other factors

such as the effect of whether practical vs. theory examinations took place to gain

entry, cultural background and social economic class as to why they had selected a

specialist sports educational institution. It is suggested that the association of these

factors is investigated in future studies. Consequently the implication of these

findings is that both birth date and gender are taken into account when deciding to

study a sports related course at an educational institution.

Therefore future investigations need to include a focus group which allows for an in-

depth analysis with a small group of cross representative people who are questioned

about their opinions as part of the research. The focus group meetings will be

recorded with a Dictaphone; this is so that it can be transcribed for the researcher.

Furthermore this allows for new insights to be discovered as to whether the students

realised that this is happening through their educational life and give examples of

how it has affected them positively and/or negatively. Further research in this field

regarding the role of birth date would be of great help to those studying or teaching

in an educational institution.



Finally unless the government adopt strategies on the Relative Age Effect (RAE) and

gender bias of those studying sports related courses at educational institution,

summer born students will not have good career prospects. This information can

then be used worldwide to develop targeted interventions aimed at supporting those

born later in the year and stop discrimination to allow for equal opportunities to study

sports academia.

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Appendices

Appendix 1. Information sheet for participants.

Appendix 2. Letter to requesting for student information.

Appendix 3. Corston and Colman (2000) Inferential Statistical Decision Tree.

Appendix 4. Table 1. The crosstabulation of student’s birth dates studying a sport

related academic course at educational institutions.

Appendix 5. Table 2. The contingency table of student’s birth months studying a

sport related undergraduate degree at an educational institution.

Appendix 6. Table 3. The expected and observed frequencies of student’s birth

months studying A-Level (BTEC Level 3) sport related course at an educational

institution.

Appendix 7. Table 4. The contingency table of student’s birth months studying a

GCSE (BTEC Level 1 & 2) sport related course at an educational institution.

DEPARTMENT OF SPORT &

EXERCISE SCIENCES

Bedford Campus

Polhill Avenue

Bedford

MK41 9EA

Web: www.beds.ac.uk/departments/sport

INFORMATION SHEET

The effect birth date has on choosing to study a sports related course at an educational

institution.

Dear Participant,

Thank you for showing an interest in participating in the study. Please read this information

sheet carefully before deciding whether to participate. If you decide to volunteer we thank you

for your participation. If you decide not to take part there will be no disadvantage to you of

any kind and we thank you for considering our request.

What is the aim of the project?

The purpose of the study is to test if there is a relationship between student’s birth date and

decisions to study a sports related course at an educational institution. Furthermore examines

the students gender, any cultural factors, social class and if they had selected a specialist

sports educational institution. This study is being undertaken as part of the requirements of a

BSc (Hons) Sports Science and Coaching degree.

What type of participant is needed?

The participant studies a sports related course from various educational institutions as

two/three per category with range of ages and be of mixed genders.

What will participants be asked to do?

Answer a set of questions used to gather information in a survey since take part in a focus

group.

What are the possible risks of taking part in the study?

All of information collected about the individual will be kept strictly confidential (subject to

legal limitations) and follow the Data Protection Act’s 8 key principals. As data generated by

the study will be retained in accordance with the University's policy on Academic Integrity.

The data generated in the course of the research will be kept securely in paper or electronic

form for a period of ten years after the completion of a research project.

What if you decide you want to withdraw from the project?

If, at any stage you wish to leave the project, then you can. There is no problem should you

wish to stop taking part and it is entirely up to you. There will be no disadvantage to yourself

should you wish to withdraw.

What will happen to the data and information collected?

Everyone that takes part in the study will receive their own results for the tests that they

complete. All information and results collected will be held securely at the University of

Bedfordshire and will only be accessible to related University staff. Results of this project

DEPARTMENT OF SPORT &

EXERCISE SCIENCES

Bedford Campus

Polhill Avenue

Bedford

MK41 9EA

Web: www.beds.ac.uk/departments/sport

may be published, but any data included will in no way be linked to any specific participant.

Your anonymity will be preserved.

What if I have any questions?

Questions are always welcome and you should feel free to ask myself Carl Page or David

Pears (Supervisor) any questions at any time. See details below for specific contact details.

Should you want to participate in this study then please complete the attached consent form,

which needs to be returned before commencing the study.

This project has been reviewed and approved by the Ethics Committee of the Department of

Sport and Exercise Sciences.

Many Thanks,

Carl Page David Pears

Department of Sport and Exercise Sciences, Email: [email protected]

University of Bedfordshire

Bedford Campus,

Polhill Avenue,

Bedford

Tel: 07749587386

Email: [email protected]

Carl Page University of Bedfordshire Polhill Avenue Bedford Bedfordshire MK419EA 14/11/2012

Principal of Corby Business Academy Corby Business Academy Academy Way Gretton Road Corby, Northamptonshire, NN17 5EB

To Whom It May Concern:

I am currently studying for a BSc (Hons) Sports Science and Coaching degree at the University of Bedfordshire and am writing my dissertation on the area of Relative Age Effect (RAE) in the academic study of sport. The term RAE is used to describe a bias, evident in youth sport and academia, where participation is higher amongst those born early in the relevant selection period (and correspondingly lower amongst those born late in the selection period) than would be expected from the normalised distribution of live births.

Therefore, I wish to seek access to some aspects of data that you may hold on students/applicants to sport related academic courses. I would only need access to the following data held on those students;

Date of birth

Gender

Course title

I have ethical approval for this study and can send a copy if required. Please contact me by phone on 07749587386 or by email at [email protected] if you have any questions or to discuss how you might provide this data. My supervisor is David Pears ([email protected] 01234 793357) and he can be contacted if you have any concerns or further questions.

Thank you for your assistance. I look forward to hearing from you.

Yours sincerely,

Carl Page



Purpose ofstatistical analysis

Summarizingunivariate data

Exploringrelationships

between variables

Testingsignificance of

differences

Descriptivestatistics(mean,

standarddeviation,

variance, etc)

Form of data Number of groups

Frequencies

Number ofvariables

Measurements

Number ofvariables

One: compared totheoreticaldistribution

Two: tested forassociation

Chi-squaregoodness-of-fit

test

Chi-square test forassociation

Two: degree ofrelationship

Multiple: effectof 2+ predictorson a dependant

variable

Multipleregression

Level ofmeasurement

Spearman'srho

Pearson'scorrelationcooeficient

One: meancompared toa specified

value

One-samplet-test

Two

Independentsamples

Relatedsamples

Form ofdata Form of data

Ordinal Interval

Mann-WhitneyU test

Independent-samples t-test

Ordinal Interval

Ordinal Interval

Wilcoxonmatched-pairs test

Paired-samples

t-test

Multiple

Independentsamples

Relatedsamples

Repeated-measuresANOVA

Oneindependent

variable

One-wayANOVA

Multipleindependent

variables

MultifactorialANOVA

Source: Corston and Colman (2000)

Figure 9. Choosing an appropriate statistical procedure


Carl Page (1008889) BSc (Hons) Sports Science and Coaching

Appendix 4.

Table 1. The crosstabulation of student’s birth dates studying a sport related

academic course at educational institutions.

Observed * Expected Crosstabulation

Expected

Total

September-

December January-April May-August

Observed September-December Count 3100 169 0 3269

Expected Count 1089.8 1089.4 1089.8 3269.0

% within Observed 94.8% 5.2% .0% 100.0%

January-April Count 7 2930 70 3007

Expected Count 1002.4 1002.1 1002.4 3007.0

% within Observed .2% 97.4% 2.3% 100.0%

May-August Count 0 7 3037 3044

Expected Count 1014.8 1014.4 1014.8 3044.0

% within Observed .0% .2% 99.8% 100.0%

Total Count 3107 3106 3107 9320

Expected Count 3107.0 3106.0 3107.0 9320.0

% within Observed 33.3% 33.3% 33.3% 100.0%



Appendix 5.

Table 2. The contingency table of student’s birth months studying a sport

related undergraduate degree at an educational institution.


Expected

Total

September-



Expected Count 1036.7 1036.7 1036.7 3110.0

% within Observed 94.9% 5.1% .0% 100.0%


Expected Count 953.3 953.3 953.3 2860.0

% within Observed .0% 97.6% 2.4% 100.0%


Expected Count 961.0 961.0 961.0 2883.0

% within Observed .0% .0% 100.0% 100.0%

Total Count 2951 2951 2951 8853

Expected Count 2951.0 2951.0 2951.0 8853.0

% within Observed 33.3% 33.3% 33.3% 100.0%



Appendix 6.

Table 3. The expected and observed frequencies of student’s birth months

studying A-Level (BTEC Level 3) sport related course at an educational

institution.


Expected

Total

September-



Expected Count 41.8 41.4 41.8 125.0

% within Observed 92.0% 8.0% .0% 100.0%


Expected Count 35.4 35.1 35.4 106.0

% within Observed .0% 98.1% 1.9% 100.0%


Expected Count 37.8 37.4 37.8 113.0

% within Observed .0% .0% 100.0% 100.0%

Total Count 115 114 115 344

Expected Count 115.0 114.0 115.0 344.0

% within Observed 33.4% 33.1% 33.4% 100.0%



Appendix 7.

Table 4. The contingency table of student’s birth months studying a GCSE

(BTEC Level 1 & 2) sport related course at an educational institution.


Expected

Total

September-



Expected Count 11.3 11.3 11.3 34.0

% within Observed 100.0% .0% .0% 100.0%


Expected Count 13.7 13.7 13.7 41.0

% within Observed 17.1% 82.9% .0% 100.0%


Expected Count 16.0 16.0 16.0 48.0

% within Observed .0% 14.6% 85.4% 100.0%

Total Count 41 41 41 123

Expected Count 41.0 41.0 41.0 123.0

% within Observed 33.3% 33.3% 33.3% 100.0%