Page 51 - DMGT404 RESEARCH_METHODOLOGY

Unit 3: Research Design

studied. There are many reasons for this, one of them being that true random assignment is not Notes
possible in many cases. The three main reasons why you can't test everything deal with:
1. Technology, or the impossibility by today's technology to be able to do certain tasks, such
as assign gender.
2. Ethics, because we can't randomly assign that some people receive a virus to test its
effects, or that some participants have to act as slaves and others as masters to test a
hypothesis, and
3. Resources, if a researcher does not have the money or the equipment needed to perform a
study, then it won't be done.
Causal design is the study of cause and effect relationships between two or more variables.
William J. Goode & Paul K. Hatt in Methods in Social Research define cause and effect relationship
as:
"when two or more cases of given phenomenon have one and only one condition in common, that condition
may be regarded as the cause and effect of that phenomenon."
The set of causes generated to predict their effects, can be deterministic or probabilistic in
nature. The deterministic cause is the one which is essential and adequate for stimulating the
occurrence of another event. While the probabilistic is the one that is essential, but is not the
only one responsible for the stimulation of the occurrence of another event.

The objective is to determine which variable might be causing certain behaviour i.e., whether
there is a cause and effect relationship between variables, causal research must be undertaken.
This type of research is very complex and the researcher can never be completely certain that
there are not other factors influencing the causal relationship, especially when dealing with
people's attitudes and motivations. There are often much deeper psychological considerations
that even the respondent may not be aware of.
In marketing decision making, all the conditions allowing the most accurate casual statements
are not usually present but in these circumstances, casual inference will still be made by marketing
managers. Because in doing so they would want to be able to make casual statements about the
effects of their actions.

Example: The new advertising campaign a company developed has resulted in percentage
increase in sales or the sales discount strategy a company followed has resulted in percentage
increase in sales. In both of these examples, marketing managers are making a casual statement.
However, the scientific concept of casuality is complex and differs substantiality from the one
held by the common person on the street. The common sense view holds that a single event (the
cause) always results in another event (the effect) occurring. In science, we recognize that an
event has a number of determining conditions or causes which act together to make the event
probable. Note that the common sense notion of casuality is that the effect always follows the
cause. This is deterministic causation in contrast to scientific notion which specifies the effect
only as being probable. This is termed as probabilistic causation. The scientific notion holds that
we can only infer casuality and never really prove it. That is the chance of an incorrect inference
is always thought to exist. The world of marketing fits the scientific view of casuality. Marketing
effects are probabilistically caused by multiple factors and we can only infer a casual relationship.
The condition under which we can make casual inference are:
(a) Time and order of occurrence of variables.
(b) Concomitant variation

(c) Elimination of other possible causal factors.

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