Page 164 - [Uma_Sekaran]_Research_methods_for_business__a_sk(BookZZ.org)

148 EXPERIMENTAL DESIGNS

Table 7.1
Cause and Effect Relationship after Randomization
Treatment Effect (% increase
in production over
Groups Treatment pre–piece rate system)
Experimental group 1 $1.00 per piece 10
Experimental group 2 $1.50 per piece 15
Experimental group 3 $2.00 per piece 20
Control group (no treatment) Old hourly rate 0

equally among all four groups. Any causal effects found would be over and above
the effects of the confounding variables.
To make it clear, let us illustrate this with some actual figures as in Table 7.1.
Note that because the effects of experience, sex, and age have been controlled
in all the four groups by randomly assigning the members to them, and the con-
trol group had no increase in productivity, it can be reliably concluded from the
result that the percentage increases in production are a result of the piece rate
(treatment effects). In other words, piece rates are the cause of the increase in
the number of toys produced. We cannot now say that the cause-and-effect rela-
tions have been confounded by other “nuisance” variables, because they have
been controlled through the process of randomly assigning members to the
groups. Here, we have high internal validity or confidence in the cause-and-
effect relationship.

Advantages of Randomization
The difference between matching and randomization is that in the former case
individuals are deliberately and consciously matched to control the differences
among group members, whereas in the latter case we expect that the process of
randomization would distribute the inequalities among the groups, based on the
laws of normal distribution. Thus, we need not be particularly concerned about
any known or unknown confounding factors.
In sum, compared to randomization, matching might be less effective, since
we may not know all the factors that could possibly contaminate the cause-and-
effect relationship in any given situation, and hence fail to match some critical
factors across all groups while conducting an experiment. Randomization, how-
ever, will take care of this, since all the contaminating factors will be spread
across all groups. Moreover, even if we know the confounding variables, we may
not be able to find a match for all such variables. For instance, if gender is a con-
founding variable, and if there are only two women in a four-group experimen-
tal design, we will not be able to match all the groups with respect to gender.
Randomization solves these dilemmas as well. Thus, lab experimental designs
involve control of the contaminating variables through the process of either
matching or randomization, and the manipulation of the treatment.