Experiments can be done in the laboratory or in the field, but the logic is the same, no matter where they are done. There are, of course, differences in experiments with people versus experiments with rocks or pigeons or plants. But these differences involve ethical issues—like deception, informed consent, and withholding oftreatment—not logic. More on these ethical issues later.

The Logic of True Experiments

There are five steps in a classic experiment:

  • 1. Formulate a hypothesis.
  • 2. Randomly assign participants to the intervention group or to the control group.
  • 3. Measure the dependent variable(s) in one or both groups. This is called O1 or “observation at time 1.’’
  • 4. Introduce the treatment or intervention.
  • 5. Measure the dependent variable(s) again. This is called O2or “observation at time 2.’’

Later, I’ll walk you through some variations on this five-step formula, including one very important variation that does not involve Step 3 at all. But first, the basics.

Step 1. Before you can do an experiment, you need a clear hypothesis about the relation between some independent variable (or variables) and some dependent variable (or variables). Experiments thus tend to be based on confirmatory rather than exploratory research questions (see box 1.1).

The testing of new drugs can be a simple case of one independent and one dependent variable. The independent variable might be, say, ‘‘taking vs. not taking’’ a drug. The dependent variable might be ‘‘getting better vs. not getting better.'' The independent and dependent variables can be much more subtle. ‘‘Taking vs. not taking’’ a drug might be ‘‘taking more of, or less of'' a drug, and ‘‘getting better vs. not getting better'' might be ‘‘the level of improvement in high-density lipoprotein’’ (the so-called good cholesterol).

Move this logic to agriculture: ceteris paribus (holding everything else—like amount of sunlight, amount of water, amount of weeding—constant), some corn plants get a new fertilizer and some don’t. Then, the dependent variable might be the number of ears per corn stalk or the number of days it takes for the cobs to mature, or the number of kernels per cob.

Now move this same logic to human thought and human behavior: Ceteris paribus, people in Nairobi who take this course in AIDS awareness will report fewer high-risk sex practices than will people who don’t take this course. Ceteris paribus here means that people in both groups—the treatment group and the control group—start with the same amount of reported high-risk sexual activity.

Things get more complicated when there are multiple independent (or dependent) variables. You might want to test two different courses, with different content, on people who come from three different tribal backgrounds. But the underlying logic for setting up experiments and for analyzing the results is the same across the sciences. When it comes to experiments, everything starts with a clear hypothesis.

Step 2. You need at least two groups, called the treatment group (or the intervention group or the stimulus group) and the control group. One group gets the intervention (the new drug, the new teaching program) and the other group doesn’t. The treatment group (or groups) and the control group(s) are involved in different experimental conditions.

In true experiments, people are randomly assigned to either the intervention group or to the control group. This ensures that any differences between the groups are the consequence of chance and not of systematic bias. Some people in a population may be more religious, or more wealthy, or less sickly, or more prejudiced than others, but random assignment ensures that those traits are randomly distributed through all the groups in an experiment.

Random assignment doesn’t eliminate selection bias. It makes differences between experimental conditions (groups) due solely to chance by taking the decision of who goes in what group out of your hands. The principle behind random assignment will become clearer after you work through chapter 6 on probability sampling, but the bottom line is this: Whenever you can assign participants randomly in an experiment, do it.

Step 3. One or both groups are measured on one or more dependent variables. This is called the pretest.

Dependent variables in people can be physical things like weight, height, systolic blood pressure, or resistance to malaria. They can also be attitudes, moods, knowledge, or mental and physical achievements. For example, in weight-loss programs, you might measure the ratio of body fat to body mass as the dependent variable. If you are trying to raise women’s understanding of the benefits of breast-feeding by exposing them to a multimedia presentation on this topic, then a preliminary test of women’s attitudes about breastfeeding before they see the presentation is an appropriate pretest for your experiment.

You don’t always need a pretest. More on this in a bit, when we discuss threats to validity in experiments.

Step 4. The intervention (the independent variable) is introduced.

Step 5. The dependent variables are measured again. This is the posttest.

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