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MANIPULATION OF THE INDEPENDENT VARIABLE 145

variable Y. In such a case, it will not be possible to determine the extent to which
Y occurred only because of X, since we do not know how much of the total vari-
ation of Y was caused by the presence of the other factor A. For instance, a
Human Resource Development manager might arrange for special training to a
set of newly recruited secretaries in creating web pages, to prove to the VP (his
boss), that such training would cause them to function more effectively. How-
ever, some of the new secretaries might function more effectively than others,
mainly or partly because they have had previous intermittent experience with the
web. In this case, the manager cannot prove that the special training alone
caused greater effectiveness, since the previous intermittent experience of some
secretaries with the web is a contaminating factor. If the true effect of the train-
ing on learning is to be assessed, then the learners’ previous experience has to
be controlled. This might be done by not including in the experiment those who
already have had some experience with the web. This is what we mean when
we say we have to control the contaminating factors, and we will later see how
this is done.

MANIPULATION OF THE INDEPENDENT VARIABLE

In order to examine the causal effects of an independent variable on a dependent
variable, certain manipulations need to be tried. Manipulation simply means that
we create different levels of the independent variable to assess the impact on the
dependent variable. For example, we may want to test the theory that depth of
knowledge of various manufacturing technologies is caused by rotating the
employees on all the jobs on the production line and in the design department,
over a 4-week period. Then we can manipulate the independent variable, “rota-
tion of employees,” by rotating one group of production workers and exposing
them to all the systems during the 4-week period, rotating another group of work-
ers only partially during the 4 weeks (i.e., exposing them to only half of the man-
ufacturing technologies), and leaving the third group to continue to do what they
are currently doing, without any special rotation. By measuring the depth of
knowledge of these groups both before and after the manipulation (also known
as the “treatment”), it would be possible to assess the extent to which the treat-
ment caused the effect, after controlling the contaminating factors. If deep knowl-
edge is indeed caused by rotation and exposure, the results would show that the
third group had the lowest increase in depth of knowledge, the second group had
some significant increase, and the first group had the greatest gains!
Let us look at another example on how causal relationships are established by
manipulating the independent variable. Let us say we want to test the effects of
lighting on worker production levels among sewing machine operators. To estab-
lish cause-and-effect relationship, we must first measure the production levels of
all the operators over a 15-day period with the usual amount of light they work
with—say 60-watt lamps. We might then want to split the group of 60 operators
into three groups of 20 members each, and while allowing one subgroup to con-
tinue to work under the same conditions as before (60-watt electric lightbulbs)