Music Preference 1 - Brent Hugh's personal and business web pages

Music Preference 35
applied to each repertoire selection once. The three treatments were applied to the three
repertoire selections as shown in Table 3.
When the data from these experiments were analyzed, data from the repertoire selections
that received treatment N from all three subgroups were grouped together. The data from
treatment N can be compared with similar data from repertoire selections that have received
treatments ID and IP. This way of gathering and analyzing data has the advantage of canceling
out any differences among the subgroups, because data for each particular treatment come in
equal parts from each of the three subgroups. The experimental method equalizes differences in
preferences due to the different repertoire selections (data for each particular treatment have
equal amounts of data from each of the three repertoire selections) and differences due to order
of presentation (each treatment has equal amounts of data from subgroups where that treatment
was presented first, second, and third; see Table 4).
In the latin-square design, the subgroups are ideally of equal size. Because of naturally
varying class sizes in the public schools and audience sizes at concerts, the subgroup sizes were
initially unequal. For the analyses presented hereafter, all subgroups of a particular group have
been made equal in size by randomly deleting members of larger subgroups.
For groups LE, UE, and MS, three repertoire selections were presented and three treatments
(N, ID, and IP) were tested (see Table 3). For groups HS and RA, more time was available for
the presentations. Four repertoire selections were presented with two different treatments (N and
ID). This effectively doubles the latin square and allows twice the data to be gathered from each
group (see Table 5 and Table 6). Preliminary studies had shown that music preference ratings
have a high variance and that the effect of informational introductions on music preference is
moderate or small. Therefore it was known that a large amount of data would be required to