A Step by Step Guide for SPSS and Exercise Studies

146 Statistical tests
rotation and factor extraction. For each extracted factor, present its eigenvalue
and the percentage of variance it explains. It is also worth reporting the total
percentage of variance explained by all extracted factors. Finally, present the
scree test and the item loadings in the rotated factor matrix or structure matrix
(see Table 55).
Scale/Reliability Analysis
Reliability analysis measures the internal consistency of a group of items. This
analysis is frequently used in questionnaire construction. Often, questionnaires
have more than one scale. Reliability analysis examines the homogeneity or
cohesion of the items that comprise each scale. Cronbach’s alpha coefficient ()
is the most frequently used index of reliability, although other indices are also
used (e.g., split-half reliability). Alpha coefficients reflect the average correlation
among the items that constitute a scale. Ideally, alphas should be between .70 and
.90. Low alphas indicate poor internal consistency of a scale, because the items
that make up the scale are poorly related to each other. Very high alphas indicate
that the items are almost identical (and perhaps redundant) and, therefore, the
generic meaning of the scale is too narrow. Note that the number of items in a
scale can affect the size of the alpha coefficient. For example, a scale may have
an alpha of .60 because it consists of only three items. If this is the case, by
increasing the number of items to four or five, the alpha coefficient can rise to .70
or above, provided that none of the items correlates poorly with the rest (see
alpha if item deleted in Table 56). Sometimes, the alpha coefficient is negative
indicating that the items are very poorly correlated. However, often the reason for
the negative alpha is the inclusion of an item which has not been recoded (see
Recode into different variables in the Transform menu). Figure 43 tests whether a
proposed enjoyment scale, consisting of five enjoyment items, has adequate
internal consistency.
Select Cronbach’s alpha coefficient from the available list (Model). Make
sure that you tick the Descriptives for scale if item deleted option in the Statistics
dialog box (see Dialog box 97) because, as you will see below, it is a very useful
option. When you finish, go back to Dialog box 96 and click OK. Table 56
shows part of the output.
As can be seen, the alpha coefficient is acceptable ( ˆ .86). It is always
useful to look at the corrected item-total correlations. Low corrected
correlations indicate that the particular item is problematic and perhaps it
should be removed. It is called corrected item-total correlation because the total
is composed of all scale items except the one it is correlated with. Problematic
items can also be detected by looking at the new alpha of the scale if an item is
deleted. If the alpha increases considerably with the deletion of a particular item,
it might be appropriate to delete that item.
The Reliability Analysis option provides another useful coefficient, the
intraclass correlation coefficient. This coefficient compares changes in the
mean scores of a variable over multiple measures. In other words, it estimates