Alinderamiento

Matching Groups

One way of controlling the contaminating or ―nuisance‖ variables is to match the various groups by picking the confounding characteristics and deliberately spreading them across groups. For instance, if there are 20 women among the 60 members, then each group will be assigned 5 women, so that the effects of gender are distributed across the four groups. Likewise, age and experience fac- tors can be matched across the four groups, such that each group has a similar mix of individuals in terms of gender, age, and experience. Because the sus- pected contaminating factors are matched across the groups, we may take com- fort in saying that variable X alone causes variable Y, if such is the result of the study. But here, we are not sure that we have controlled all the nuisance factors, since we may not be aware of them all. A safer bet is to randomize.

Randomization

Another way of controlling the contaminating variables is to assign the 60 mem- bers randomly (i.e., with no predetermination) to the four groups. That is, every member would have a known and equal chance of being assigned to any of these four groups. For instance, we might throw the names of all the 60 members into a hat, and draw their names. The first 15 names drawn may be assigned to the first group, the second 15 to the second group, and so on, or the first person drawn might be assigned to the first group, the second person drawn to the sec- ond group, and so on. Thus, in randomization, the process by which individuals are drawn (i.e., everybody has a known and equal chance of being drawn) and their assignment to any particular group (each individual could be assigned to any one of the groups set up) are both random. By thus randomly assigning members to the groups we would be distributing the confounding variables among the groups equally. That is, the variables of age, sex, and previous experience—the controlled variables—will have an equal probability of being distributed among the groups. The process of randomization would ideally ensure that each group is comparable to the other, and that all variables, including the effects of age, sex and previous experience are controlled. In other words, each of the groups will have some members who have more experience mingled with those who have less or no experience. All groups will have members of different age and sex com- position. Thus randomization would ensure that if these variables do indeed have a contributory or confounding effect, we would have controlled their confound- ing effects (along with those of other unknown factors) by distributing them across groups. This is achieved because when we manipulate the independent variable of piece rates by having no piece rate system at all for one group (con- trol) and having different piece rates for the other three groups (experimental), we can determine the causal effects of the piece rates on production levels. Any errors or biases caused by age, sex, and previous experience are now distributed

148 EXPERIMENTAL DESIGNS

Table 7.1

Cause and Effect Relationship after Randomization

Treatment Effect (% increase in production over

Groups Treatment pre–piece rate system)

Experimental group 1 $1.00 per piece 10 Experimental group 2 $1.50 per piece 15 Experimental group 3 $2.00 per piece 20 Control group (no treatment) Old hourly rate 0

equally among all four groups. Any causal effects found would be over and above the effects of the confounding variables.

To make it clear, let us illustrate this with some actual figures as in Table 7.1. Note that because the effects of experience, sex, and age have been controlled in all the four groups by randomly assigning the members to them, and the con- trol group had no increase in productivity, it can be reliably concluded from the result that the percentage increases in production are a result of the piece rate (treatment effects). In other words, piece rates are the cause of the increase in the number of toys produced. We cannot now say that the cause-and-effect rela- tions have been confounded by other ―nuisance‖ variables, because they have been controlled through the process of randomly assigning members to the groups. Here, we have high internal validity or confidence in the cause-and- effect relationship.

Advantages of Randomization

The difference between matching and randomization is that in the former case individuals are deliberately and consciously matched to control the differences among group members, whereas in the latter case we expect that the process of randomization would distribute the inequalities among the groups, based on the laws of normal distribution. Thus, we need not be particularly concerned about any known or unknown confounding factors.

In sum, compared to randomization, matching might be less effective, since we may not know all the factors that could possibly contaminate the cause-and- effect relationship in any given situation, and hence fail to match some critical factors across all groups while conducting an experiment. Randomization, how- ever, will take care of this, since all the contaminating factors will be spread across all groups. Moreover, even if we know the confounding variables, we may not be able to find a match for all such variables. For instance, if gender is a con- founding variable, and if there are only two women in a four-group experimen- tal design, we will not be able to match all the groups with respect to gender. Randomization solves these dilemmas as well. Thus, lab experimental designs involve control of the contaminating variables through the process of either matching or randomization, and the manipulation of the treatment.

INTERNAL AND EXTERNAL VALIDITY 149