RESEARCH METHOD COHEN ok

280 EXPERIMENTS AND META-ANALYSIS
limits of variability that will be used to define the
matching (e.g. ± 3 points). As before, the greater
the degree of precision in the matching here, the
closer will be the match, but the greater the degree
of precision the harder it will be to find an exactly
matched sample.
One way of addressing this issue is to place all
the subjects in rank order on the basis of the scores
or measures of the dependent variable. Then the
first two subjects become one matched pair (which
one is allocated to the control group and which
to the experimental group is done randomly, e.g.
by tossing a coin), the next two subjects become
the next matched pair, then the next two subjects
become the next matched pair, and so on until
the sample is drawn. Here the loss of precision is
counterbalanced by the avoidance of the loss of
subjects.
The alternative to matching that has been
discussed earlier in the chapter is randomization.
Smith (1991: 215) suggests that matching is
most widely used in quasi-experimental and nonexperimental
research, and is a far inferior means
of ruling out alternative causal explanations than
randomization.
The factorial design
In an experiment there may be two or more
independent variables acting on the dependent
variable. For example, performance in
an examination may be a consequence of
availability of resources (independent variable
one: limited availability, moderate availability,
high availability) and motivation for the subject
studied (independent variable two: little
motivation, moderate motivation, high motivation).
Each independent variable is studied
at each of its levels (in the example
here it is three levels for each independent
variable) (see http://www.routledge.com/
textbooks/9780415368780 – Chapter 13, file 13.9.
ppt). Participants are randomly assigned to groups
that cover all the possible combinations of levels
of each independent variable, as shown in the
model.
INDEPEN-
DENT
VARIABLE
Availability
of resources
Motivation
for the subject
studied
LEVEL
ONE
Limited
availability
(1)
Little
motivation
(4)
LEVEL
TWO
Moderate
availability
(2)
Moderate
motivation
(5)
LEVEL
THREE
High
availability
(3)
High
motivation
(6)
Here the possible combinations are: 1 + 4,
1 + 5, 1 + 6, 2 + 4, 2 + 5, 2 + 6, 3 + 4, 3 + 5and
3 + 6. This yields 9 groups (3 × 3combinations).
Pretests and post-tests or post-tests only can
be conducted. It might show, for example,
that limited availability of resources and little
motivation had a statistically significant influence
on examination performance, whereas moderate
and high availability of resources did not, or
that high availability and high motivation had
a statistically significant effect on performance,
whereas high motivation and limited availability
did not, and so on.
This example assumes that there are the same
number of levels for each independent variable;
this may not be the case. One variable may have,
say, two levels, another three levels, and another
four levels. Here the possible combinations are
2 × 3 × 4 = 24 levels and, therefore, 24 experimental
groups. One can see that factorial designs
quickly generate several groups of participants. A
common example is a 2 × 2design,inwhichtwo
independent variables each have two values (i.e.
four groups). Here experimental group 1 receives
the intervention with independent variable 1 at
level 1 and independent variable 2 at level 1; experimental
group 2 receives the intervention with
independent variable 1 at level 1 and independent
variable 2 at level 2; experimental group 3 receives
the intervention with independent variable 1 at
level 2 and independent variable 2 at level 1; experimental
group 4 receives the intervention with
independent variable 1 at level 2 and independent
variable 2 at level 2.