Experiments are an excellent data collection strategy for those wishing to observe the consequences of very specific actions or stimuli. Most commonly a quantitative research method, experiments are used more often by psychologists than sociologists, but understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings based on experimental designs.

An experiment is a method of data collection designed to test hypotheses under controlled conditions, with the goal to eliminate threats to internal validity.  There are different experiment designs. In the classic experiment, the effect of a stimulus is tested by comparing two groups: one that is exposed to the stimulus (the experimental group) and another that does not receive the stimulus (the control group). The control group, often called the comparison group is treated equally to the experimental group in all respects, except it does not receive the independent variable.  The purpose of the control group is to control for rival plausible explanations.

In an experiment, the effects of an independent variable upon a dependent variable are tested. Because the researcher’s interest lies in the effects of an independent variable, the researcher must measure participants on the dependent variable before and after the independent variable (or stimulus) is administered. In this type of experiment researchers employ random assignation, which means that one group is the equivalent of the other.  Random assignation is more fully explored in the following section “Random Assignment”.

Students in research methods classes often use the term experiment to describe all kinds of empirical research projects, but in social scientific research the term has a unique meaning and should not be used to describe all research methodologies. In general, designs considered to be “true experiments” contain three key features: 1) independent and dependent variables, 2) pretesting and post-testing, and 3) experimental and control groups.

Pretesting and post-testing are both important steps in a classic experiment. Here are a couple of hypothetical examples.

As you can see from example 1, the dependent variable is reported levels of PTSD symptoms (measured through the pre and post-test) and the independent variable is visual exposure to trauma (video). You ask yourself: Is the reported level of PTSD symptoms dependent upon visual exposure to trauma (as depicted through the video). Table 6.1 depicts the design of the study from example 1, above.

Where:

• • X stands for the treatment
  • E stands for the experimental group (e.g., car accident video)
  • C stands for the control or comparison group (e.g., scenic byways of Manitoba video)
  • O stands for time, subscripts stand for time: 1=time one; 2=time two.

Now it is your turn.  See if you can fill in Table 6.2, based upon what you read in Example 2.

Where:

• • X stands for the treatment
  • E stands for the experimental group (e.g.,                                                    )
  • C stands for the control or comparison group (e.g.,                                      )
  • O stands for time, subscript stand for (                                                      )
  • The dependent variable is                                                        )
  • The independent variable is                                                        )

Answer for Table 6.2, a true experiment design.

Where:

• • X stands for treatment
  • E stands for the experimental group (e.g., article on severe prejudice within group)
  • C stands for the control or comparison group (e.g., article on severe prejudice outside group)
  • O stands for time, 1 and 2 subscripts stand for time: 1=time one; 2=time two.
  • The dependent variable is depression
  • The independent variable is feelings that prejudice is a significant issue within your racial group

Random Assignment

As previously mentioned, one of the characteristics of a true experiment is that researchers use a random process to decide which participants are tested in which conditions. Random assignation is a powerful research technique that addresses the assumption of pre-test equivalence – that your experimental and your control group are equal in all respects before the administration of the independent variable (Palys & Atchison, 2014).

Random assignation is the primary way that researchers attempt to control extraneous variables across conditions. In its strictest sense, random assignment should meet two criteria.  One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus, one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested.

However, one problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible.

One approach is block randomization. In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. When the procedure is computerized, the computer program often handles the random assignment, which is obviously much easier.  You can also find programs online to help you randomize your random assignation. For example, The Research Randomizer website will generate block randomization sequences for any number of participants and conditions.

Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this possibility is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.  Note: Do not confuse random assignation with random sampling. Random sampling is a method for selecting a sample from a population and we will talk about this in Chapter VII, Sampling Techniques.

Text Attributions

• Part of this chapter has been adapted from Chapter 12.2 in Principles of Sociological Inquiry, which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.
• Part of this chapter has been adapted from Unit 1: Experimental Research in Research Methods by Joseph K. Adjei. © Creative Commons Attribution-ShareAlike 3.0 License.