Researchers can influence the results of an experimental design by

In order to determine what effect something has on people, you subject some people to that "something" and then you compare them with people who were not subjected to the "something. In the same way that random selection makes your sample representative and eliminates bias, randomly assigning subjects to conditions automatically matches the. Random assignment controls for all possible kinds of differences between people.

Random assignment to conditions gives you groups that do not differ from one another in any systematic way. Strengths: The experimental method makes it possible to determine whether changes in the independent variable cause subsequent changes in the dependent variable. While other types of research make it possible to determine whether or not there is a correlation between a pair of variables, only the experiment can tell whether there is a causal relationship.

Experiments, by their very nature, require a particular sequence of events to take place in a particular temporal order. They focus on change over time, while survey methods are much better at identifying static correlational patterns. Weaknesses: The main weakness of the experimental method is their dependence on what many see as an "artificial" environment. People may behave differently in the experimental setting than they would under more ordinary conditions.

While the artificial environment of the experiment allows the unpredictable complexities of ordinary life to be "controlled," it tends to remove the participants from the ordinary context in which they live and place them instead in an artificial environment that has little or no resemblance to the "real world.

Researchers generally want to see changes in their dependent variables. They want to see differences between their experimental and control groups. Because of this, the apparent changes seen in the dependent variable may be caused by the experimenter's subtle cues and not by the independent variable. Due to the costs involved or the structure of the experimental situation, it is often difficult to obtain experimental samples large enough to obtain results that are stable enough to allow generalizations to larger populations.

Experimental methods require the researcher to identify and control all relevant variables that might distort the apparent relation between independent and dependent variables. In the natural sciences where all the complexities of the social world do not have to be considered, this may be accomplished. But in the social sciences where the subject of inquiry is a social situation that takes place in a social context, it may not be possible to identify all relevant variables or potential "disturbances.

Compare the two-group pretest-posttest design to the two-group posttest only design. What additional information does the pretest give you in the former? The second 10 plants will go into what we call our control group —these are the plants that do not get plant food.

This method of assigning things to groups is called randomization , and it makes sure that every plant involved in our study has an equal chance of being picked for either group. This is the best way to make sure that the groups are as equal as possible. Then, for a month, we give the plant food to the treatment group but not to the control group. All the other variables are kept exactly the same—the plants get the same amount of sunlight, the same environment, and the same water.

We measure the size of the plants our dependent variable before and after the month of treatment and compare measurements. If the plants in the treatment group have grown more than those in the control group, we know that it was the plant food that caused the extra growth and not something else, because everything else was the same between the two groups.

In order to examine if sports fans can actually cause judges to change their opinion on who should win, and also to see if this could also be applied to other judges too, we did an experiment looking at the effect of a noisy home crowd on judges scoring Muay Thai fights [ 1 ] Figure 1.

Muay Thai fights are held in a standard boxing ring, with two competitors fighting for five, 3-min rounds. Muay Thai fighters, kick, punch, knee, elbow, and grapple with their opponent in an attempt to gain a points victory or get the referee to stop the fight. In our experiment, judges scored Muay Thai fights the dependent variable while either listening to the cheering of the actual crowd at ringside, or while using noise-canceling headphones to judge in complete silence noise is the independent variable Figure 2.

When designing experiments, the first thing scientists do is look at the research conducted by other people on the topic they plan to investigate. So, to set the scene for the experiment that we are going to look at in this article, let us look at what other researchers have found about the influence of crowds on sports.

Sports teams and individual athletes tend to win a higher number of games when playing at their home stadium or venue than when they play away games. This is so common it has a name—it is known as home advantage. Home advantage is found in both team sports and individual sports where a judge, referee, or umpire plays a major role in deciding who wins—sports such as basketball, soccer, and boxing. It seems that fans cheering for their favorite team might actually influence who wins a game or competition, by influencing referees, umpires, and judges.

One previous experiment looked at the influence of crowd noise on whether soccer referees decided to award a foul or not [ 2 ]. The researchers were able to make sure that differences in the decisions made by referees were influenced by the crowd noise conditions only crowd noise or no crowd noise , rather than differences in the referees themselves.

The researchers did this by dividing the group of referees randomly into two groups. One group watched a video of soccer tackles with crowd noise, and the other group watched the same video but in total silence. When research teams find differences using this type of study design, they can be confident it was crowd noise that made the difference—this is something known as internal validity.

However, the researchers cannot have confidence that their findings will hold true for other officials outside of a laboratory without considering some additional things. To decide how likely it is that research findings can be applied to real-life situations outside of the laboratory, researchers use an idea called external validity. A laboratory study can have high internal validity but low external validity.

For example, a researcher can be confident that one thing has caused another to change within in the experiment, but they can be less confident these changes will happen outside of the experiment, in the real world. Researchers can do particular things to help improve the external validity of their experiments. These things include selecting participants who are similar to the wider group being researched; using a series of different settings that reflect the diversity found outside the lab; using a range of participants who might respond differently to the experiment; exploring the cause and effect relationship across more than a single point in time; and making sure the settings and tasks the participants take part in are realistic [ 3 ].

Psychologist and researcher Egon Brunswik [ 4 ] proposed something similar to external validity, which he called representative design. He suggested that when researchers want to investigate how individuals respond to different things, it is important to do the study in a location where these things would normally happen and not an artificial environment.

The idea is that if sports officials make decisions in a laboratory, where there is no pressure from actual fans or players, it is not quite the same as making decisions at a live event. In our crowd noise study, we attempted to improve external validity and representative design in a number of ways. 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. Confounding variables.