SEKE 2012 Proceedings - Knowledge Systems Institute

D. Analysis and Interpretation
We summarized the collected data by calculating the
metrics CompPLA for the thirty AGM configurations generated
by the subjects, as well as verifying the complexity
rating of such configurations. Table II presents the observed
values for the CompPLA metric from the generated AGM
configurations.
Table II
OBSERVED VALUES FOR THE COMPPLA METRIC FROM THE
GENERATED CONFIGURATIONS.
Configuration # CompPLA
1 0.51
2 0.56
3 0.51
4 0.83
5 0.91
6 0.50
7 0.47
8 0.53
9 0.67
10 0.90
11 0.53
12 0.97
13 0.48
14 0.69
15 0.74
Configuration # CompPLA
16 0.98
17 0.77
18 0.82
19 0.52
20 0.82
21 0.49
22 1.00
23 0.52
24 0.42
25 0.62
26 0.47
27 0.53
28 0.70
29 0.40
30 0.78
1) Descriptive Statistics: Figure 1 presents the CompPLA
observed values (Table II) plotted in a boxplot.
• Alternative Hypothesis (H 1 ): the CompPLA observed
values distribution is non-normal, i.e., the significance
value (p) is less or equal to 0.05 (p ≤ 0.05).
Taking into account a sample size (N) of 30, with mean
(μ) 0.6545, standard deviation (σ) 0.1842, and median (˜x)
0.5895, the CompPLA metric obtained a significance value:
• p0.05); and
6
ρ( Corr.1 )=1−
1 − 0.07 = 0.93
30(30 2 −30)
×293.5 =1−
6
26970 ×293.5 =
Thus, according to Figure 2, there is a strong positive
correlation (ρ( Corr.1 ) = 0.93) between the metric CompPLA
and the subjects complexity rating.
Based on the proposed correlation, we have evidence to
reject the null hypothesis H 0 of the study, and accept the
alternative hypothesis H 1 (Section III-B5), which states that
complexity metrics are significantly correlated to complexity
of PLA.
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