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)
Carl.Page@study.beds.ac.uk
Mr. D Pears
SPE001-3 Dissertation
Contents
Abstract ...................................................................................................................... 2
Introduction ................................................................................................................ 3
Review of Literature ................................................................................................... 5
Methods ................................................................................................................... 14
Results ..................................................................................................................... 20
Discussion ................................................................................................................ 26
Conclusion ............................................................................................................... 37
References ............................................................................................................... 39
Appendices .............................................................................................................. 51
Carl Page (1008889) Page 1 BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
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.
Carl Page (1008889) Page 2 BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
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 socalled ‘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
Carl Page (1008889) Page 3 BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
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 awardwinning 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
Carl Page (1008889) Page 4 BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
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
Carl Page (1008889) Page 5 BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
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
Carl Page (1008889) Page 6 BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
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
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SPE001-3 Dissertation
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.
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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.
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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.
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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.
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.
At ages younger than 12,
ability is a poor guide to
talent & capacity.
Difference in ability at
school level.
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
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.
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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
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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.
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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.
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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
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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 ChiSquare 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
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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
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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
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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.
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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).
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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.
Male
Female
30%
70%
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
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September-December and lowest for the spring and summer months as shown in
Figure 4.
Frequency of students
Expected
3300
3250
3200
3150
3100
3050
3000
2950
2900
2850
Observed
Linear (Expected )
3269
3044
3107
September-December
3107 3007
3107
January-April
May-August
Birth Date Month Groups
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).
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Frequency of students
Expected
3150
3100
3050
3000
2950
2900
2850
2800
2750
2700
Observed
Linear (Expected )
3110
2951
2951
September-December
2951
2860
2883
January-April
May-August
Birth Date Month Groups
Figure 5. The distribution of student’s birth dates who are studying a sport related
academic course at undergraduate level.
Male
Female
31%
69%
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
Carl Page (1008889) Page 23 BSc (Hons) Sports Science and Coaching
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group reported significantly more students than the other two birth date month
groups.
Expected
Observed
Linear (Expected )
Frequency of students
130
125
120
115
110
105
125
115
115
115
113
106
100
95
September-December
January-April
May-August
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).
Male
Female
20%
80%
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).
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Expected
Observed
Linear (Expected )
Frequency of students
60
50
40
30
20
41
41
34
41
41
48
10
0
September-December
January-April
May-August
Birth Date Month Groups
Figure 9. Observed and Expected frequencies in September-December, JanuaryApril 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).
Male
Female
13%
87%
Figure 10. Higher male distribution of students studying a sports related course at
BTEC Level 1 and 2 (GCSE).
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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.
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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-
Carl Page (1008889) Page 27 BSc (Hons) Sports Science and Coaching
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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
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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 ChiSquare test will become unreliable once the researcher’s table includes cells with the
expected frequencies of below 5. Therefore different statistical procedures may be
Carl Page (1008889) Page 29 BSc (Hons) Sports Science and Coaching
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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 crosssectional 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.
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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
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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
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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
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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
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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.
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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.
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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.
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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 indepth 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.
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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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Carl Page (1008889) Page 50 BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
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.
Carl Page (1008889) Page 51 BSc (Hons) Sports Science and Coaching
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
Department of Sport and Exercise Sciences,
University of Bedfordshire
Bedford Campus,
Polhill Avenue,
Bedford
Tel: 07749587386
Email: carl.page@study.beds.ac.uk
David Pears
Email: David.Pears@beds.ac.uk
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 carl.page@study.beds.ac.uk if you have any questions or
to discuss how you might provide this data. My supervisor is David Pears
(david.pears@beds.ac.uk 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 of
statistical analysis
Summarizing
univariate data
Descriptive
statistics
(mean,
standard
deviation,
variance, etc)
Exploring
relationships
between variables
Testing
significance of
differences
Form of data
Number of groups
Frequencies
Measurements
One: mean
compared to
a specified
value
Number of
variables
Number of
variables
One-sample
t-test
One: compared to
theoretical
distribution
Two: tested for
association
Two: degree of
relationship
Chi-square
goodness-of-fit
test
Chi-square test for
association
Level of
measurement
Ordinal
Spearman's
rho
Two
Independent
samples
Related
samples
Form of
data
Form of data
Multiple: effect
of 2+ predictors
on a dependant
variable
Multiple
regression
Multiple
Ordinal
Interval
Mann-Whitney
U test
Independentsamples t-test
Ordinal
Independent
samples
One
independent
variable
Multiple
independent
variables
One-way
ANOVA
Multifactorial
ANOVA
Related
samples
Repeatedmeasures
ANOVA
Interval
Interval
Pearson's
correlation
cooeficient
Wilcoxon
matchedpairs test
Figure 9. Choosing an appropriate statistical procedure
Pairedsamples
t-test
Source: Corston and Colman (2000)
SPE001-3 Dissertation
Appendix 4.
Table 1. The crosstabulation of student’s birth dates studying a sport related
academic course at educational institutions.
Observed * Expected Crosstabulation
Expected
SeptemberDecember
Observed
September-December
January-April
Count
Total
169
0
3269
Expected Count
1089.8
1089.4
1089.8
3269.0
% within Observed
94.8%
5.2%
.0%
100.0%
7
2930
70
3007
1002.4
1002.1
1002.4
3007.0
.2%
97.4%
2.3%
100.0%
0
7
3037
3044
1014.8
1014.4
1014.8
3044.0
.0%
.2%
99.8%
100.0%
3107
3106
3107
9320
Expected Count
3107.0
3106.0
3107.0
9320.0
% within Observed
33.3%
33.3%
33.3%
100.0%
Count
% within Observed
Count
Expected Count
% within Observed
Total
May-August
3100
Expected Count
May-August
January-April
Count
Carl Page (1008889) BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
Appendix 5.
Table 2. The contingency table of student’s birth months studying a sport
related undergraduate degree at an educational institution.
Observed * Expected Crosstabulation
Expected
SeptemberDecember
Observed
September-December
January-April
Count
Total
159
0
3110
Expected Count
1036.7
1036.7
1036.7
3110.0
% within Observed
94.9%
5.1%
.0%
100.0%
0
2792
68
2860
953.3
953.3
953.3
2860.0
.0%
97.6%
2.4%
100.0%
0
0
2883
2883
961.0
961.0
961.0
2883.0
.0%
.0%
100.0%
100.0%
2951
2951
2951
8853
Expected Count
2951.0
2951.0
2951.0
8853.0
% within Observed
33.3%
33.3%
33.3%
100.0%
Count
% within Observed
Count
Expected Count
% within Observed
Total
May-August
2951
Expected Count
May-August
January-April
Count
Carl Page (1008889) BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
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.
Observed * Expected Crosstabulation
Expected
SeptemberDecember
Observed
September-December
May-August
Total
May-August
Total
Count
115
10
0
125
Expected Count
41.8
41.4
41.8
125.0
92.0%
8.0%
.0%
100.0%
0
104
2
106
Expected Count
35.4
35.1
35.4
106.0
% within Observed
.0%
98.1%
1.9%
100.0%
0
0
113
113
Expected Count
37.8
37.4
37.8
113.0
% within Observed
.0%
.0%
100.0%
100.0%
Count
115
114
115
344
115.0
114.0
115.0
344.0
33.4%
33.1%
33.4%
100.0%
% within Observed
January-April
January-April
Count
Count
Expected Count
% within Observed
Carl Page (1008889) BSc (Hons) Sports Science and Coaching
SPE001-3 Dissertation
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.
Observed * Expected Crosstabulation
Expected
SeptemberDecember
Observed
September-December
Count
0
34
11.3
11.3
11.3
34.0
100.0%
.0%
.0%
100.0%
7
34
0
41
13.7
13.7
13.7
41.0
17.1%
82.9%
.0%
100.0%
0
7
41
48
Expected Count
16.0
16.0
16.0
48.0
% within Observed
.0%
14.6%
85.4%
100.0%
41
41
41
123
41.0
41.0
41.0
123.0
33.3%
33.3%
33.3%
100.0%
Count
Expected Count
% within Observed
Total
Total
0
% within Observed
May-August
May-August
34
Expected Count
January-April
January-April
Count
Count
Expected Count
% within Observed
Carl Page (1008889) BSc (Hons) Sports Science and Coaching