Page 162 - [Uma_Sekaran]_Research_methods_for_business__a_sk(BookZZ.org)

146 EXPERIMENTAL DESIGNS

we might want to manipulate the intensity of the light for the other two sub-
groups, by making one group work with 75-watt and the other with 100-watt
lightbulbs. After the different groups have worked with these varying degrees of
light exposure for 15 days, each group’s total production for these 15 days may
be analyzed to see if the difference between the preexperimental and the post-
experimental productions among the groups is directly related to the intensity of
the light to which they have been exposed. If our hypothesis that better lighting
increases the production levels is correct, then the subgroup that did not have
any change in the lighting (called the control group), should have no increase in
production and the other two groups should show increases, with the ones hav-
ing the most light (100 watts) showing greater increases than those who had the
75-watt lighting.
In this case the independent variable, lighting, has been manipulated by
exposing different groups to different degrees of changes in it. This manipula-
tion of the independent variable is also known as the treatment, and the results
of the treatment are called treatment effects.
Let us illustrate how variable X can be both controlled and manipulated in the
lab setting through Example 7.1.

Example 7.1 Let us say an entrepreneur—the owner of a toy shop—is rather disappointed
with the number of imitation “Ninja turtles” (greatly in demand) produced by his
workers, who are paid wages at an hourly rate. He might wonder whether pay-
ing them piece rates would increase their production levels. However, before
implementing the piece-rate system, he would want to make sure that switching
over to the new system would indeed achieve the objective.
In a case like this, the researcher might first want to test the causal relation-
ships in a lab setting, and if the results are encouraging, conduct the experiment
later in a field setting. In designing the lab experiment, the researcher should first
think of possible factors that would affect the production level of the workers,
and then try to control these. Other than piece rates, previous job experience
might also influence the rate of production because familiarity with the job
makes it easy for people to increase their productivity levels.
In some cases, where the jobs are very strenuous and require muscular
strength, gender differences may affect productivity. Let us say that for the type
of production job discussed earlier, age, gender, and prior experience of the
employees are the factors that would influence the production levels of the
employees. The researcher needs to control these three variables. Let us see how
this can be done.
Suppose the researcher intends to set up four groups of 15 people each, for
the lab experiment—one to be used as the control group, and the other three
subjected to three different pay manipulations. Now, the variables that may
impact on the cause-and-effect relationship can be controlled in two different
ways: either by matching the groups or through randomization. These concepts
are explained before we proceed further.