Statistical analysis followed data collection for each variable under investigation. The
analysis was conducted to establish meaning and answer the research questions. Students data
were excluded from analysis if their attendance was less than thirteen sessions (80%) or two
consecutive lessons were missed (Gonzalez-Aguero et al., 2012; Negra et al., 2016). All students
met the required threshold; hence all student data were included for statistical analysis to answer
each research question. A detailed description of the statistical procedures conducted are
presented below.
3.8.1 Analysis to Examine Research Questions
Analysing quantitative data in this thesis required establishing meaning from data that
was collected from the 61 students who participated in both pre- and post-tests. The procedure
was sequential with a prearranged step-by-step method. All data were entered into Microsoft
Excel by the lead researcher and double-checked for accuracy. The data were then exported from
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Armonk, New York, 2018). As previously reported in this chapter (Section 3.7), treatment
fidelity data were analysed throughout the study to ensure the intervention was conducted in an
appropriate manner.
Data analysis involved a chronological approach that included multiple steps as follows:
1. Calculation of study variables;
2. Descriptive analysis of group characteristics and study variables (illustrate the overall background of participants);
3. Reliabilities of study measures (establish the appropriateness of each measure);
4. Data Screening and Repeated Measures ANOVA (examination of significant between and within-group changes);
5. Post Hoc Analysis (examine the location of the significant ANOVA calculations);
6. Correlational analysis (examine relationships between variables).
3.8.2 Calculation of Study Variables
The calculation of study variables for use within this thesis focused on processing the raw
data from each study measure and calculating each into the study variables used for further
analysis. The process for calculating each study variable followed the guidelines and procedures
outlined in Section 3.4. As such, study variables were calculated for: • Motor performance skills proficiency;
• Upper body muscular power; • Lower body muscular power; • Peak power;
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3.8.3 Descriptive Analysis of Group Characteristics and Study Variables
Descriptive statistics included calculations of means and standard deviations for all pre-
and post-test study variables (i.e. motor performance skills proficiency; upper body muscular
power; lower body muscular power; peak power; reactive strength index), and the group
characteristics which included age, height, weight and gender.
3.8.4 Reliabilities of study measures
Twelve students (six boys and six girls) were randomly selected as part of the assessment
for test-retest reliability of each data collection measure. A sample of students were chosen rather
than all students due to school time and curriculum constraints. Cronbach alpha tests were used
to examine the reliability of the study measures. Cronbach alpha scores were calculated for all
pre- and post-test study variables of FMS-Polygon, medicine ball chest throw, squat jump, drop
jump RSI 10cm, drop jump RSI 20cm and drop jump RSI 30cm. A threshold of .80 was
identified as being appropriately reliable based on the recommendations of Thomas et al. (2015).
Paired sample t-tests were used to compare the differences between dependent variable scores in
test and retest sessions.
3.8.5 Data Screening and Repeated Measures ANOVA
To examine the first two research questions, a repeated measure analysis of variance (RM
ANOVA) was conducted to examine any significance between and within-group differences for
each study variable. Before conducting a RM ANOVA, all data were screened for assumptions
that a RM ANOVA would be an appropriate analysis. The specific assumption tests were to (a)
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homogeneity of variance. The examination of outliers was performed to identify and remove any
data that were unrepresentative scores and may abnormally influence the results if included.
Shapiro Wilks test was used to test the null hypothesis that sample data were drawn from a
normally distributed population. Lastly, the Brown-Forsythe test was used to verify if the data is
normally distributed.
A (2 x 2) (Group x Time) repeated-measures ANOVAs was used to evaluate the effects of
the intervention for each variable as a function of group (comparison and plyometric) and time
(pre-and post-intervention). The between-subject independent variable was the intervention
groups with two levels: the comparison group and the plyometric group. The within-subject
independent variable was the time with two levels: pre- and post-intervention. Due to conducting
multiple RM ANOVAs, the original significance was set at p < .05, except where Bonferroni
correction was employed in which case an alpha level of 0.05 was divided by the number of
calculations performed.
3.8.6 Post Hoc Analysis
When a significant RM ANOVA was returned, the location of this difference (e.g.
between groups) was examined using a Bonferonni pairwise comparison. Bonferroni correction
Alpha was set at p ≤ 0.05 for FMS-Polygon and medicine ball chest throw. For all other
measures, the Bonferroni correction Alpha was adjusted and set at p ≤ 0.01 due to multiple
measures for the lower body muscular power domain. As part of the initial analysis using SPSS,
a pairwise comparison provided insight to examine where the significant differences are located
114 3.8.7 Correlational analysis
Correlation analysis in the form of the Pearson product-moment coefficients were
performed to establish the strength of the relationships between motor performance skill
proficiency and muscular power variables. Pearson’s product-moment correlation coefficients
were performed to assess the associations (both strength and direction) between variables of
differences for all outcome measures. Correlational indices were set at small (0.1-0.3), moderate
(0.3-0.5), large (0.5-0.7), and very large (0.7-0.9) using the guidelines of Hopkins, Marshall,
Batterham, and Hanin (2009).