Consider an experiment with three conditions: A, B, and C. The trick in counterbalancing is to make sure that each condition appears in each position an equal number of times. One way of counterbalancing the conditions would be to use the following three orders of presentation:. Notice that all three conditions are performed once first, once second, and once third.
All variables, which are not the independent variable, but could affect the results DV of the experiment. EVs should be controlled where possible. Variable s that have affected the results DV , apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.
Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition. The principle of random allocation is to avoid bias in the way the experiment is carried out and to limit the effects of participant variables. Examples of order effects include:. McLeod, S. Experimental design. Simply Psychology. Toggle navigation. Three types of experimental designs are commonly used:. Ecological validity.
The degree to which an investigation represents real-life experiences. Experimenter effects. Demand characteristics.
Independent variable IV. Dependent variable DV. Variable the experimenter measures. This is the outcome i. Extraneous variables EV. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research.
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 in psychology and other fields too. 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. 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. 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.
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. Table 5. 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. 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. An alternative to simple random assignment of participants to conditions is the use of a matched-groups design. Using this design, participants in the various conditions are matched on the dependent variable or on some extraneous variable s prior the manipulation of the independent variable.
This guarantees that these variables will not be confounded across the experimental conditions. We could then use that information to rank-order participants according to how healthy or unhealthy they are.
Next, the two healthiest participants would be randomly assigned to complete different conditions one would be randomly assigned to the traumatic experiences writing condition and the other to the neutral writing condition. The next two healthiest participants would then be randomly assigned to complete different conditions, and so on until the two least healthy participants.
This method would ensure that participants in the traumatic experiences writing condition are matched to participants in the neutral writing condition with respect to health at the beginning of the study.
If at the end of the experiment, a difference in health was detected across the two conditions, then we would know that it is due to the writing manipulation and not to pre-existing differences in health. In a within-subjects experiment , each participant is tested under all conditions.
Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive and an unattractive defendant.
The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. We will look more closely at this idea later in the book.
However, not all experiments can use a within-subjects design nor would it be desirable to do so. One disadvantage of within-subjects experiments is that they make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This knowledge could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do.
The primary disadvantage of within-subjects designs is that they can result in order effects. One type of order effect is a carryover effect.
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