Page 186 - [Uma_Sekaran]_Research_methods_for_business__a_sk(BookZZ.org)

170 EXPERIMENTAL DESIGNS

APPENDIX

FURTHER EXPERMENTAL DESIGNS

In this chapter we discussed different types of experimental designs where groups
were subjected to one or more treatments and the effects of the manipulation
measured. However, the simultaneous effects of two or more variables on a
dependent variable may sometimes be desired to be assessed, and this would call
for more complex designs. Among the many advanced experimental designs that
are available, we will examine here the completely randomized design, the ran-
domized block design, Latin square design, and the factorial design.
It would be useful to understand some terms before describing the various
designs. The term factor is used to denote an independent variable—for exam-
ple, price. The term level is used to denote various gradations of the factor—for
example, high price, medium price, low price—while making it clear as to what
these gradations signify (e.g., high price is anything over $2 per piece; medium
is $1–$2 per piece; low price is anything less than $1 per piece). Treatment
refers to the various levels of the factors. A blocking factor is a preexisting vari-
able in a given situation that might have an effect on the dependent variable in
addition to the treatment, the impact of which would be important to assess. In
effect, a blocking factor is an independent variable that has an effect on the
dependent variable, but which preexists in a given situation, as for example, the
number of women and men in an organization; or teenagers, middle-aged men,
and senior citizens as customers of a store, and so on.

THE COMPLETELY RANDOMIZED DESIGN

Let us say that a bus transportation company manager wants to know the effects
of fare reduction by 5, 7, and 10 cents, on the average daily increase in the num-
ber of passengers using the bus as a means of transportation. He may take 27
routes that the buses usually ply, and randomly assign nine routes for each of
the treatments (i.e., reduction of fares by 5, 7, and 10 cents) for a 2-week period.
His experimental design would look as shown in Figure 7.8, where the Os on
the left indicate the number of passengers that used the bus for the 2 weeks pre-
ceding the treatment; X 1 , X 2 , and X 3 indicate the three different treatments (fare
reductions of 5, 7, and 10 cents per mile), and the Os on the right indicate the
number of passengers that used the bus as the transportation mode during the 2
weeks when the fares were reduced. The manager will be able to assess the
impact of the three treatments by deducting each of the three Os on the left from
its corresponding O on the right. The results of this study would provide the
answer to the bus company manager’s question.