RESEARCH METHOD COHEN ok

564 MULTIDIMENSIONAL MEASUREMENT
Box 25.4
Initial SPSS output for principal components analysis
Total variance explained
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Component Total % of Cumulative Total % of Cumulative Total % of Cumulative
variance % variance % variance %
1 9.343 38.930 38.930 9.343 38.930 38.930 4.037 16.820 16.820
2 1.424 5.931 44.862 1.424 5.931 44.862 2.810 11.706 28.527
3 1.339 5.580 50.442 1.339 5.580 50.442 2.779 11.578 40.105
4 1.220 5.085 55.526 1.220 5.085 55.526 2.733 11.386 51.491
5 1.085 4.520 60.047 1.085 4.520 60.047 2.053 8.556 60.047
6 0.918 3.825 63.872
7 0.826 3.443 67.315
8 0.723 3.013 70.329
9 0.685 2.855 73.184
10 0.658 2.743 75.927
11 0.623 2.596 78.523
12 0.562 2.342 80.864
13 0.532 2.216 83.080
14 0.512 2.132 85.213
15 0.493 2.055 87.268
16 0.466 1.942 89.210
17 0.437 1.822 91.032
18 0.396 1.650 92.682
19 0.376 1.566 94.247
20 0.364 1.517 95.764
21 0.307 1.280 97.044
22 0.271 1.129 98.174
23 0.232 0.965 99.138
24 0.207 0.862 100.000
Extraction method: Principal components analysis
A scree plot can also be used at this stage, to
identify and comment on factors (this is available
at the click of a button in SPSS). A scree plot
shows each factor on a chart, in descending
order of magnitude. For researchers the scree plot
becomes interesting where it flattens out (like the
rubble that collects at the foot of a scree), as
this indicates very clearly which factors account
for a lot of the variance, and which account
for little. In the scree plot here (Box 25.5) one
can see that the scree flattens out considerably
after the first factor, then it levels out a little
for the next 4 factors, tailing downwards all the
time. This suggests that the first factor is the
significant factor in explaining the greatest amount
of variance.
Indeed, in using the scree plot one perhaps
has to look for the ‘bend in the elbow’ of
the data (after factor one), and then regard
those factors above the bend in the elbow as
being worthy of inclusion, and those below
the bend in the elbow as being relatively
unimportant (Cattell 1966; Pallant 2001: 154).
However, this is draconian, as it risks placing
too much importance on those items above the
bend in the elbow and too little importance on
those below it. The scree plot adds little to the
variance table presented in Box 25.4, though it
does enable one to see at a glance which are the
significant and less significant factors, or, indeed
which factors to focus on (the ones before the
scree levels off) and which to ignore.