Experimental Research

What is an experiment?

An experiment is a type of study designed specifically to answer the question of whether there is a casual relationship between two variables. In other words, whether changes in one variable (referred to as an independent variable) cause a change in another variable (referred to as a dependent variable. Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of independent variable are called conditions. The second fundamental feature of an experiment is that the researcher exerts controlover, or minimizes variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables.

Manipulation of the Independent Variable

To manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. The different levels of the independent variable are referred to as conditions, and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition”.

In nursing research, an example might be testing whether a structured mindfulness program reduces postoperative pain compared to standard discharge teaching. Here, the independent variable is the mindfulness program, and the dependent variable is reported pain level.

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, if a nurse researcher compares wound healing in patients that already eat a balanced diet with those who do not, the researcher has not actively manipulated the exercise variable; this is not a true experiment. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they ate a balanced diet, or it might have been caused by any of the other differences between people who do and do not eat a balanced diet. Thus the active manipulation of the independent variable is crucial for eliminating potential alternative explanations for the results.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, a nurse researcher cannot assign patients to have or not have a history of childhood hospitalization in order to examine its effect on coping with chronic illness. This caveat does not mean it is impossible to study the relationship between childhood hospitalization experiences and coping with chronic illness—only that it must be done using nonexperimental approaches.

Control of Extraneous Variables

As we have seen previously in the chapter, an extraneous variable is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their gender. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres (Knecht, 2000). Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise (variability) to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed nursing majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger lesbian women would apply to older gay men. In many situations, the advantages of a diverse sample (increased external validity) outweigh the reduction in noise achieved by a homogeneous one.

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable is an extraneous variable that differs on average across levels of the independent variable (i.e., it is an extraneous variable that varies systematically with the independent variable). For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs in each condition so that the average IQ is roughly equal across the conditions, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants in one condition to have substantially lower IQs on average and participants in another condition to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse, and this effect is exactly why confounding variables are undesirable. Because they differ systematically across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Figure 5.1 shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Controlling extraneous variables is especially important in nursing settings, where patients differ in age, health status, and environment. Even small differences, like time of day, nurse communication style, or room temperature, can affect patient outcomes.

Treatment and Control Conditions

In nursing research, a treatment is any intervention meant to improve patient health outcomes or promote well-being. This intervention includes medical treatments, medication administration, therapeutic communication techniques, patient education, and more. To determine whether a treatment works, participants are randomly assigned to either a treatment condition, in which they receive the treatment, or a control condition, in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, they have reduced pain, have increased quality of life—then the researcher can conclude that the treatment works. In research on the effectiveness of therapies and medical treatments, this type of experiment is often called a randomized clinical trial.

For example, a nursing researcher may compare a new aromatherapy intervention for reducing nausea in chemotherapy patients (treatment group) to standard care (control group).

There are different types of control conditions. In a no-treatment control condition, participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bed sheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008).

Placebo effects are interesting in their own right, but they also pose a serious problem for researchers who want to determine whether a treatment works.

Fortunately, there are several solutions to this problem. One is to include a placebo control condition, in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. For example, in a clinical trial testing a new pain-relief medication for postoperative patients, those in the placebo group might receive an identical-looking tablet that contains no active ingredient. This ensures that any difference in pain reduction between the treatment and placebo groups is due to the medication itself, not just the belief that one is receiving treatment.

Ethical considerations are particularly important for nurses conducting clinical research. Participants must be told if they might receive a placebo and assured that standard care will still be provided.

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a wait-list control condition, in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This disclosure allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?

Random Assignment

Random assignment is one of the most powerful tools for ensuring fairness and scientific rigor. In nursing, this process helps prevent bias when evaluating interventions. The primary way that researchers accomplish control of extraneous variables across conditions is called random assignment, which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research. For instance, in a wound-care study, patients could be randomly assigned to receive either a new dressing or the standard one. Random assignment ensures that differences in healing aren’t due to patient factors like age or mobility .

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 they are tested. When the procedure is computerized, the computer program often handles the random assignment.

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. The Research Randomizer website (http://www.randomizer.org) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Random assignment is not guaranteed to control all extraneous variables across conditions. The process is random, so 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.

Remixed from:

• Research Methods in Psychology by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton (2019) published by pressbooks under a CC BY-NC-SA license.
  • Chapter 23: Experiment Basics
  • Chapter 24: Experimental Designs

Media Attributions

• Dr. Fehr [avatar] by Research Assistant Katie Gregson on Canva using Canva AI image creation https://www.canva.com/ai-assistant/ is subject to the Canva Pro Content License.

References

Jhangiani, R. S., Chiang, I. A., Cuttler, C., & Leighton, D. C. (2019, August 1). Chapter 23: “Experimental basics“. Research Methods in Psychology. https://kpu.pressbooks.pub/psychmethods4e/chapter/experiment-basics/

Jhangiani, R. S., Chiang, I. A., Cuttler, C., & Leighton, D. C. (2019, August 1). Chapter 24: “Experimental design“. Research Methods in Psychology. https://kpu.pressbooks.pub/psychmethods4e/chapter/experimental-design/

Knecht, S. et al. (2000). Handedness and hemispheric language dominance in healthy humans. Brain: A Journal of Neurology, 123(12), 2512-2518. http://dx.doi.org/10.1093/brain/123.12.2512 

Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59, 565–590.