Research Methods for Statistical Analysis - Desklib

RUNNING HEADER: RESEARCH METHODS 1
Research Methods
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Research Methods 2
Scenario 1
To carry out this test for scenario 1 a one-way ANOVA was used. The statistical method was
adopted since it establishes whether there are any statistically significant differences between
the means of two or more unrelated (independent) groups.
The assumptions that were made entailed the independent variables consisted of more than
two categorical and independent groups. Consequently, it was assumed that there was the
independence of observation in that there was no relationship between observations of the
groups themselves. The final assumption was the test of homogeneity which was shown that
the variance for time for each group was not equal, F(3, 56) = 6.247, p= 0.001.
From the test, it was established that there was a statistically significant difference between
the four groups (F=3,56) = 66.725,p = 0.00). The Tukey post hoc test showed that the time to
complete a 30m sprint was statistically different for the 1st and 2nd team, 1st and 3rd and the 1st
and 4th team. A similar observation could also be made for the 2nd team with respect to team
1, 3 and 4. However, there was no statistically significant difference between the 3rd and the
4th team (p=0.213).
Scenario 2
To determine the limits of agreement between methods and is the automatic system, a
suitable method for evaluating blood pressure, a paired sample t-test was used. The method
was chosen since it is suitable in comparing the means of two related groups on the same
dependent and continuous variable (Lakens, 2013). The strength of this method is that it
controls for the effect of the environment (Wellek, 2010). However, with the lower degrees
of freedom, it is harder to reject the null hypothesis (De Winter, 2013).

Research Methods 3
The assumptions made was that the independent variables consisted of two categorical and
related groups. Consequently, it was assumed that there was no significant outlier in the
differences between the two groups and that the distribution differences between the two
related groups were approximately normally distributed.
From the results of the paired sample t-test, it was found out that there was no statistically
significant improvement in the limits of the agreement since t(24) = -0.44, p>0.05. Thus, the
automatic system used is not as good as the manual system.
Scenario 3
To measure the effects of the two methods of warm-up on the cyclist for the three groups of
athletes, a repeated measure ANOVA was adopted. The method was chosen since it
compares three or more group mean where the participants in each group are the same. In this
scenario, the participants were subjected to more than one conditions and the response to
each condition was desired to be compared.
The main strength of this method is that the method’s design is very powerful since it
controls for factors which cause variability between subjects (Cardinal & Aitken, 2013).
However, the method is at risk of being influenced by the exposure of subjects to multiple
treatments (Levine, 2013).
The assumptions made were that the dependent variable was continuous while the
independent variables consisted of at least two categorical matched groups. Moreover, the
assumption of sphericity was upheld. However, the Maulchy test showed that the assumption
sphericity was violated (p<0.05). Thus, the focus will be on the Greenhouse-Geisser
correction. From the tests of within-subject effects, it was observed that with the Greenhouse
–Geisser correction, the mean scores were statistically different (F(1.524, 13.717 ) = 444.54,
P <0.00).