research analysis: performance comparison against different pain killer tablets

Foundation Degree in Sports Coaching

Research Analysis

Research Foundations Quantitative – SPO025-2

Carl Page (1008889) University of Bedfordshire

Mr. M Lambert

SPO025-2 Research Foundations Quantitative

Carl Page (1008889) 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 (Watts ) from Acetaminophen to Aspirin to

Placebo condit ion for al l subjec ts . The Acetaminophen condit ion subjects performed at a s ignificant ly higher power level than in the Placebo condit ion. 24 of 36 subjects

completed from the Acetaminophen and Aspirin harder than the Placebo condit ion.



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.

4868

4773 4774

4720

4740

4760

4780

4800

4820

4840

4860

4880

Acetaminophen Aspirin Placebo

Tim

e t

o e

xhau

stio

n (

seco

nd

s)

Conditional Groups

Time to exhaustion (s)

Acetaminophen

Aspirin

Placebo

Figure 2. Group mean rat ing of Time to exhaust ion (seconds) from Acetaminophen to Aspirin to Placebo condit ion for al l subjec ts .



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

Figure 5. Q-Plots of Age discover Normal Distribution.



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



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)



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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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