Page 159 - [Uma_Sekaran]_Research_methods_for_business__a_sk(BookZZ.org)

CAUSAL RELATIONSHIPS 143

What does the above information tell us? How do we know what kinds of incen-
tives cause people not to absent themselves? What particular incentive(s) did the
22% of companies that found their strategies to be “very effective” offer? Is there a
direct causal connection between one or two specific incentives and absenteeism?

Scenario C The dagger effect of layoffs is that there is a sharp drop in the commitment of
workers who are retained, even though they might well understand the logic of
the reduction in the workforce.
Does layoff really cause employee commitment to drop off, or is something
else operating in this situation?

The answers to the questions raised in Scenarios A, B, and C might be found
by using experimental designs in researching the issues.
In the previous chapter we had touched on experimental designs. In this chap-
ter, we will discuss both lab experiments and field experiments in detail. Exper-
imental designs, as we know, are set up to examine possible cause and effect
relationships among variables, in contrast to correlational studies, which exam-
ine the relationships among variables without necessarily trying to establish if
one variable causes another.
To establish that variable X causes variable Y, all three of the following con-
ditions should be met:

1. Both X and Y should covary [i.e., when one goes up, the other should also
simultaneously go up (or down)].
2. X (the presumed causal factor) should precede Y. In other words, there must
be a time sequence in which the two occur.
3. No other factor should possibly cause the change in the dependent variable Y.

It may thus be seen that to establish causal relationships between two variables
in an organizational setting, several variables that might covary with the depen-
dent variable have to be controlled. This would then allow us to say that vari-
able X and variable X alone causes the dependent variable Y. Useful as it is to
know the cause-and-effect relationships, establishing them is not easy, because
several other variables that covary with the dependent variable have to be con-
trolled. It is not always possible to control all the covariates while manipulating
the causal factor (the independent variable that is causing the dependent vari-
able) in organizational settings, where events flow or occur naturally and nor-
mally. It is, however, possible to first isolate the effects of a variable in a tightly
controlled artificial setting (the lab setting), and after testing and establishing the
cause-and-effect relationship under these tightly controlled conditions, see how
generalizable such relationships are to the field setting.
Let us illustrate this with an example. Suppose a manager believes that staffing
the accounting department completely with personnel with M.Acc. (Master of
Accountancy) degrees will increase its productivity. It is well nigh impossible to