RESEARCH METHOD COHEN ok

DESIGNING AN EX POST FACTO INVESTIGATION 269
There is the difficulty of interpretation and
the danger of the post-hoc assumption being
made, that is, believing that because X precedes
O, X causes O.
It often bases its conclusions on too limited a
sample or number of occurrences.
It frequently fails to single out the really
significant factor or factors, and fails to
recognize that events have multiple rather than
single causes.
As a method it is regarded by some as too
flexible.
It lacks nullifiability and confirmation.
The sample size might shrink massively with
multiple matchings (Spector 1993: 43).
Designing an ex post facto investigation
We earlier referred to the two basic designs
embraced by ex post facto research – the corelational
(or causal) model and the criterion
group (or causal-comparative) model. As we
saw, the causal model attempts to identify the
antecedent of a present condition and may be
represented thus:
Independent variable
X
Dependent variable
O
Although one variable in an ex post facto study
cannot be confidently said to depend upon the
other as would be the case in a truly experimental
investigation, it is nevertheless usual to designate
one of the variables as independent (X) and
the other as dependent (O). The left to right
dimension indicates the temporal order, though
having established this, we must not overlook the
possibility of reverse causality.
In a typical investigation of this kind, then,
two sets of data relating to the independent and
dependent variables respectively will be gathered.
As indicated earlier in the chapter, the data on the
independent variable (X) willberetrospectivein
character and as such will be prone to the kinds of
weakness, limitations and distortions to which all
historical evidence is subject. Let us now translate
the design into a hypothetical situation. Imagine a
secondary school in which it is hypothesized that
low staff morale (O) hascomeaboutasadirect
result of reorganization some two years earlier, say.
Anumberofkeyfactorsdistinguishingthenew
organization from the previous one can be readily
identified. Collectively these could represent or
contain the independent variable X and data
on them could be accumulated retrospectively.
They could include, for example, the introduction
of mixed ability and team teaching, curricular
innovation, loss of teacher status, decline in
student motivation, modifications to the school
catchment area, or the appointment of a new
headteacher. These could then be checked against
ameasureofprevailingteachers’attitudes(O),
thus providing the researcher with some leads at
least as to possible causes of current discontent.
The second model, the causal-comparative, may
be represented schematically as shown.
Group Independent variable Dependent variable
E X O 1
---------------------
C O 2
Using this model, the investigator hypothesizes
the independent variable and then compares
two groups, an experimental group (E) which
has been exposed to the presumed independent
variable X and a control group (C) which has
not. (The dashed line in the model shows
that the comparison groups E and C are not
equated by random assignment). Alternatively,
the investigator may examine two groups that
are different in some way or ways and then
try to account for the difference or differences
by investigating possible antecedents. These two
examples reflect two types of approach to causalcomparative
research: the ‘cause-to-effect’ kind
and the ‘effect-to-cause’ kind.
The basic design of causal-comparative investigations
is similar to an experimentally designed
study. The chief difference resides in the nature
of the independent variable, X. Inatrulyexperimental
situation, this will be under the control
of the investigator and may therefore be described
as manipulable. In the causal-comparative model
Chapter 12