Worked in collaboration with several other investigators and had the responsibility to complete the unfinished ‘Statistical Analysis’ of the ‘Methodology’ section and then write an appropriate ‘Results’ section.

1. Foundation Degree in Sports Coaching
Research Analysis
Research Foundations Quantitative – SPO025-2
Carl Page (1008889)
University of Bedfordshire
Mr. M Lambert

2. SPO025-2 Research Foundations Quantitative
Carl Page (1008889) Page 2 Foundation Degree in Sports Coaching
Contents
Statistical Analysis & Results ................................................................................................. 2
References ............................................................................................................................. 8
Bibliography............................................................................................................................ 8
Appendices .............................................................................................................................. 11
Statistical Analysis & Results
36 subjects with Age of 22.75 ± 2.273 years, Height of 1.7897 ± 0.3806cm, Mass of
69.44± 4.017kg participated in the investigation. There appears to be some
differences in the mean power output measured in Watts between the three
conditions or groups of the different pain killer tablets as shown in Figure 1. From the
data can assume that the pain killer tablets adversely affects subject’s ability to
perform as measured by time presented in Figure 2. The Acetaminophen condition
subjects completed in a dramatically longer time than in the Placebo condition, yet
Aspirin condition did not last as long as the Placebo condition. However to see if this
relationship is significant, scrutiny of the ANOVA results was also applied. It is used
with parametric levels of data.
Figure 1. Mean change in Power Output (Wat ts) from Acetaminophen to Aspi rin to
Placebo condi t ion for al l subjects. The Acetaminophen condi t ion subjects performed at
a signi ficant ly higher power level than in the Placebo condi t ion. 24 of 36 subjects
completed from the Acetaminophen and Aspi rin harder than the Placebo condi t ion.

3. SPO025-2 Research Foundations Quantitative
Time to exhaustion (s)
4868
4773 4774
4880
4860
4840
4820
4800
4780
4760
4740
4720
Acetaminophen Aspirin Placebo
Time to exhaustion (seconds)
Conditional Groups
Acetaminophen
Aspirin
Placebo
Figure 2. Group mean rat ing of Time to exhaust ion (seconds) from
Acetaminophen to Aspi rin to Placebo condi t ion for al l subjects.
Q-plots were used to illustrate if the data is normal distribution, since trends to the
population as shown in Figure. 3, 4 and 5. This is a good data set because it is an S
shape spread all along close to the line, yet must be aware anomies are present in
the data. Through drawing the line of best fit along the data points this offers
distinguishes of the connection concerning the two variables. As acknowledging the
regression line, it can be applied to estimate or predict a subject’s result on the
conditional variable that has been established on the marking a subject conveys for
the forecast variable. The estimation or prediction influenced through two important
points of the regression line such as the slope and intercept.
Figure 3. Q-Plots of Height present Normal
Distribution.
Figure 4. Q-Plots of Mass show Normal
Distribution.
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4. SPO025-2 Research Foundations Quantitative
Figure 5. Q-Plots of Age discover Normal Distribution.
The value P < 0.05 was considered as statistical significance level. If P < 0.05
something is less than this then it is significantly different. However is P greater
above 0.05 then no significance. If P less than 0.05 in Homogeneity of Variances,
this means variance is significantly different. Although if P greater than 0.05 in
Homogeneity of Variances the variance is not significantly different. In Homogeneity
of Variances test the only one a significant difference in the mean Maximum Heart
Rate (MaxHR) achieved by subjects between conditions was observed (F2, 33 =
0.013, P < 0.05) This shows significantly difference in variance. However the rest of
the data doesn’t violate Homogeneity of Variances.
Simply the F-ratio expresses that the testing was effective, since the group averages
was dissimilar. Yet this does not purposely reveal which means of groups differ from
others. Therefore it is important to perform further testing, thus discover wherever
the dissimilarities be situated within the data. The measurement of distance between
individual distributions; as F goes up, P goes down for instance more confidence in
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5. SPO025-2 Research Foundations Quantitative
there being a difference between the two means. To work it out it is Mean Square of
X / Mean Square of Error. (iSixSigma 2012)
Height F (2, 33) = 0.089, 0.915
Mass F (2, 33) = 0.119, 0.888
Age F (2, 33) = 0.061 0.941
The F value associated with Height F (2, 33) =.089, p = .915) has a P value greater
than 0.05, cannot conclude that there is an interaction among these variables and
must retain the null hypothesis.
Independent Measures ANOVA (Between Subjects ANOVA) – (3 groups 1
condition), test whether there is a significant difference if any concerning subjects or
within subjects all together. Nonetheless this does not state where the significance
is. This is vital as specifically when the hypothesis is one-tailed. ANOVA computes
the value of a variable F; the larger the value of F, the more unlikely it is to have
occurred by chance, and hence the more likely that at least one of the populations
has an average different from the others.
If F is not large enough, the conclusion is that all the populations have equal
averages. (Colgate University 2011) There is no significant difference between the
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6. SPO025-2 Research Foundations Quantitative
three conditions for Height, Mass, Age, VO2 Max and Peak Power Output. This
shows normally distributed reliable and valid data used.
The post-hoc results compare individual groups against each other to see if there
are significant differences between groups’ means;
Time Till Exhaustion Acetaminophen – Aspirin and Placebo
End Blood Lactate Acetaminophen – Aspirin and Placebo
Mean Power Output Acetaminophen – Aspirin and Placebo
Mean Pain Acetaminophen – Aspirin and Placebo
It is possible for an ANOVA to be significant then again for two of the variables to
display no significance whatsoever. A One-way ANOVA revealed a significant effect
for level of analgesic on Time to Exhaustion (TTE) F (2, 33 = 13.87, p = 0.00) A Tukey
post hoc analysis showed that Time to Exhaustion in Acetaminophen group (4867.75
± 32.51) was significantly (P<0.05) greater than both the Aspirin (4773.08 ± 50.84).
The Placebo group (4774.33 ± 63.34). No significance Time to Exhaustion were
found Aspirin and Placebo group (P>0.05)
A One-way ANOVA revealed a significant effect for level of analgesic on End Blood
Lactate (EndBL) F (2, 33 = 18.14, p = 0.00) A Tukey post hoc analysis showed that
End Blood Lactate in Acetaminophen group (7.775 ± 0.95) was significantly (P<0.05)
Carl Page (1008889) Page 6 Foundation Degree in Sports Coaching

7. SPO025-2 Research Foundations Quantitative
greater than both the Aspirin (5.833 ± 1.05). The Placebo group (5.708 ± 0.82). No
significance Time to Exhaustion were found Aspirin and Placebo group (P>0.05)
A One-way ANOVA revealed a significant effect for level of analgesic on Mean
Power Output (Mean PO) F (2, 33 = 18.54, p = 0.00) A Tukey post hoc analysis
showed that Mean Power Output in Acetaminophen group (240.67 ± 9.93) was
significantly (P<0.05) greater than both the Aspirin (222.50 ± 10.13). The Placebo
group (214.50 ± 12.16). No significance Mean Power Output were found Aspirin and
Placebo group (P>0.05)
A One-way ANOVA revealed a significant effect for level of analgesic on Mean Pain
F (2, 33 = 10.407, p = 0.00) A Tukey post hoc analysis showed that Mean Pain in
Acetaminophen group (6.92 ± 0.99) was significantly (P<0.05) lower than both the
Aspirin (8.08 ± 0.67). The Placebo group (8.25 ± 0.62). There was significance
difference Mean Pain were found Aspirin and Placebo group (P>0.05)
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8. SPO025-2 Research Foundations Quantitative
References
Colgate University (2012) Introduction to One-Way Analysis of Variance. [online]
Available at: http://math.colgate.edu/math102/dlantz/examples/ANOVA/anova.html
[Accessed: 16/11/2011]
iSixSigma (2012) F-value (ANOVA). [online] Available at:
http://www.isixsigma.com/dictionary/f-value-anova/ [Accessed: 16/11/2011]
Bibliography
Carl Page (1008889) Page 8 Foundation Degree in Sports Coaching
Books
Field, A. (2009). Discovering Statistics Using SPSS (3rd Edition). Sage Publications.
Mason, J. (2009) Quantitative Researching, 2nd edn, London, Thousands, Oask and
New Delhi: Sage.
Kerr, A., Hall, H., & Kozub, S. A. (2002) Doing Statistics Using SPSS. Sage
Publications
Websites
Canadian News (2012) Eight types of drug. [online] Available at:
http://www.cbc.ca/sports/indepth/drugs/glossary/classes.html [Accessed:
24/02/2012].
Khan Academy (2012) ANOVA 3 -Hypothesis Test with F-Statistic. [online] Available
at: http://www.khanacademy.org/video/anova-3--hypothesis-test-with-f-statistic?
topic=statistics [Accessed: 16/11/2011]

9. SPO025-2 Research Foundations Quantitative
Northwestern University Medical School (1997) PROPHET StatGuide: One-way
analysis of variance (ANOVA). [online] Available at:
http://www.basic.northwestern.edu/statguidefiles/oneway_anova.html [Accessed:
16/11/2011]
Northwestern University Medical School (1997) PROPHET StatGuide: Glossary.
[online] Available at: http://www.basic.northwestern.edu/statguidefiles/sg_glos.html
[Accessed: 16/11/2011]
Richland Community College (2012) Jones, James. Stats: One-Way ANOVA.
[online] Available at: http://people.richland.edu/james/lecture/m170/ch13-1wy.html
[Accessed: 16/11/2011]
Sport Science (2011) A New View of Statistics. [online] Available at:
http://www.sportsci.org/resource/stats/index.html [Accessed: 16/11/2011]
Talk Stats (2009) What F value stands for in ANOVA analysis. [online] Available at:
http://www.talkstats.com/showthread.php/7481-What-F-value-stands-for-in-ANOVA-analysis
[Accessed: 16/11/2011].
The Athlete (2011) World Anti-Doping Agency. [online] Available at:
http://www.theathlete.org/wada.htm [Accessed: 24/02/2012].
The University of Texas (1997) Repeated Measures ANOVA Using SAS PROC
GLM. [online] Available at: http://www.ats.ucla.edu/stat/sas/library/repeated_ut.htm
[Accessed: 16/11/2011]
University of Leicester (2000) Analysis of Variance (ANOVA). [online] Available at:
http://www.le.ac.uk/bl/gat/virtualfc/Stats/anova.html [Accessed: 16/11/2011]
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10. SPO025-2 Research Foundations Quantitative
WADA (2012) The World Anti-Doping Code The 2012 Prohibited List International
Standard. [online] Available at: http://www.wada-ama.org/Documents/World_Anti-
Doping_Program/WADP-Prohibited-list/2012/WADA_Prohibited_List_2012_EN.pdf
[Accessed: 24/02/2012].
Carl Page (1008889) Page 10 Foundation Degree in Sports Coaching
Journals
Amann M, Proctor LT, Sebranek JJ, Pegelow DF, Dempsey JA. (2009) Opioid-mediated
muscle afferents inhibit central motor drive and limit peripheral muscle
fatigue development in humans. The Journal of Physiology, January, 587(Pt 1)
pp.271–283 271.
Castle, PC., Macdonald, AL., Philp, Webborn, A. Watt, PW. & Maxwell NS. (2006)
Precooling leg muscle improves intermittent sprint exercise performance in hot,
humid conditions. Journal of Applied Physiology, December 100: pp.1377–1384.
Duffield R, Green R, Castle P, Maxwell N. (2010) Precooling Can Prevent the
Reduction of Self-Paced Exercise Intensity in the Heat. Journal of the American
College of Sports Medicine, 42(3) March, pp.577-584.
Lambert, EV., Gibson, A.St.C., Noakes, TD. (2005) Complex systems model of
fatigue: integrative homoeostatic control of peripheral physiological systems during
exercise in humans. British Journal of Sports Medicine, 39(1) January, pp.52–62.
Mauger, AR., Jones AM., & Williams, CA. (2010). Influence of acetaminophen on
performance during time trial cycling. Journal of Applied Physiology January, 108:
pp.98–104.

11. SPO025-2 Research Foundations Quantitative
Maxwell, NS, Castle, PC, Spencer, M. (2008) Effect of recovery intensity on peak
power output and the development of heat strain during intermittent sprint exercise
while under heat stress. Journal of Sport Science and Medincine in Sport, August
100: pp. 491–499.
Appendices
Appendix 1. The unfinished manuscript
Appendix 2. Microsoft Excel Spreadsheet Assignment 1 data set of Preliminary
Testing Scores Data Set, Performance Trial Mean Scores Data Set And Power
Output Over Time In Performance Trial Data Set.
Appendix 3. SPSS Results
Carl Page (1008889) Page 11 Foundation Degree in Sports Coaching