Page 121 - [Uma_Sekaran]_Research_methods_for_business__a_sk(BookZZ.org)

HYPOTHESES DEVELOPMENT 105

Example 5.19 There is a relationship between age and job satisfaction.

Example 5.20 There is a difference between the work ethic values of American and Asian
employees.
Nondirectional hypotheses are formulated either because the relationships or
differences have never been previously explored and hence there is no basis for
indicating the direction, or because there have been conflicting findings in previ-
ous research studies on the variables. In some studies a positive relationship
might have been found, while in others a negative relationship might have been
traced. Hence, the current researcher might only be able to hypothesize that there
would be a significant relationship, but the direction may not be clear. In such
cases, the hypotheses could be stated nondirectionally. Note that in Example 5.19
there is no clue as to whether age and job satisfaction are positively or negatively
correlated, and in Example 5.20 we do not know whether the work ethic values
are stronger in Americans or in Asians. However, in Example 5.20, it would have
been possible to state that age and job satisfaction are positively correlated, since
previous research has indicated such a relationship. Whenever the direction of the
relationship is known, it is better to develop directional hypotheses for reasons
that will become clear in our discussions in a later chapter.

Null and Alternate Hypotheses
The null hypothesis is a proposition that states a definitive, exact relationship
between two variables. That is, it states that the population correlation between
two variables is equal to zero or that the difference in the means of two groups
in the population is equal to zero (or some definite number). In general, the null
statement is expressed as no (significant) relationship between two variables or
no (significant) difference between two groups, as we will see in the various
examples in this chapter. The alternate hypothesis, which is the opposite of the
null, is a statement expressing a relationship between two variables or indicating
differences between groups.
To explain it further, in setting up the null hypothesis, we are stating that there
is no difference between what we might find in the population characteristics (i.e.,
the total group we are interested in knowing something about) and the sample
we are studying (i.e., a limited number representative of the total population or
group that we have chosen to study). Since we do not know the true state of
affairs in the population, all we can do is to draw inferences based on what we
find in our sample. What we imply through the null hypothesis is that any differ-
ences found between two sample groups or any relationship found between two
variables based on our sample is simply due to random sampling fluctuations and
not due to any “true” differences between the two population groups (say, men
and women), or relationships between two variables (say, sales and profits). The
null hypothesis is thus formulated so that it can be tested for possible rejection. If
we reject the null hypothesis, then all permissible alternative hypotheses relating
to the particular relationship tested could be supported. It is the theory that allows
us to have faith in the alternative hypothesis that is generated in the particular