Then, from the results, we collect we can draw conclusions. These are the fundamentals of scientific study - investigations allow us to advance scientific knowledge and better our understanding of the world and its workings. Children are taught as early as Year 1 that we must make sure any experiments are a fair test. For example, if we conduct an experiment looking at whether boys run faster than girls in a race, we must make the test fair.
We must make sure the distance they run is the same, the conditions are the same i. This understanding of fairness is our foundation for learning about variables, which we shall look at now. The elements that change in an experiment are called variables. A variable is any factor, trait, or condition that can exist in differing amounts or types. An experiment usually has three kinds of variables: independent , dependent , and controlled.
Let's use a basic experiment as an example: A group of students want to find out whether temperature affects how quickly sugar dissolves. They set up an experiment with four beakers of water, each at a different temperature. They add a spoonful of sugar to each, sir each beaker once only, and timed how long it took for the sugar to disappear. There is only ever one independent variable. There is only one dependent variable.
There can be multiple control variables. Any change to a controlled variable would invalidate the results, so it's really important that they are kept the same throughout. An easy way to think of independent and dependent variables is, when you're conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that.
You can also think of the independent variable as the cause and the dependent variable as the effect. Imagine you want to see which type of fertiliser helps plants grow fastest, so you add a different brand of fertiliser to each plant and see how tall they grow. And all other conditions kept the same between each plant e. Why not try executing your own investigation? You could look at how the mass of a toy attached to a parachute affects how long it takes to fall.
This will give you an opportunity to make a parachute perhaps using a piece of scrap material and some string, tried to various toys such as a toy car, a Playmobil person, a cuddly toy.
You will also need a set of scales to measure the mass of each toy. Remember to use the same parachute each time! Ok, now its time to see whether all this information is sinking in. Answer the following questions to test your understanding of variables. Sally is performing a test in which she is trying to see if plants can grow when given fizzy drinks instead of water.
She gives one plant water and a second identical plant the same amount of fizzy drink for two weeks. What is the independent variable? 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 shoe size.
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. 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.
Extraneous variables make it difficult to detect the effect of the independent variable in two ways. Imagine a simple experiment on the effect of mood happy vs. Participants are put into a negative or positive mood by showing them a happy or sad video clip and then asked to recall as many happy childhood events as they can. Table 6. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three.
The effect of mood here is quite obvious. In reality, however, the data would probably look more like those Table 6. Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated.
And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated.
Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in Table 6.
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.
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 to the data.
In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as year-old, heterosexual, female, right-handed psychology 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 heterosexual women would apply to older homosexual men. In many situations, the advantages of a diverse sample 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. But as long as there are participants with lower and higher IQs at each level of the independent variable so that the average IQ is roughly equal, then this variation is probably acceptable and may even be desirable.
What would be bad, however, would be for participants at one level of the independent variable to have substantially lower IQs on average and participants at another level 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 across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Figure 6. 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 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. Anything that varies in the context of a study other than the independent and dependent variables.
When the way an experiment was conducted supports the conclusion that the independent variable caused observed differences in the dependent variable. These studies provide strong support for causal conclusions. When the way a study is conducted supports generalizing the results to people and situations beyond those actually studied. The participants and the situation studied are similar to those that the researchers want to generalize to and participants encounter everyday.
Skip to content Chapter 6: Experimental Research. Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments. Explain what internal validity is and why experiments are considered to be high in internal validity. Explain what external validity is and evaluate studies in terms of their external validity. Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
Recognize examples of confounding variables and explain how they affect the internal validity of a study. An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables. Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable.
Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables. In many cases, the control is the unmanipulated version of the experiment, or the "normal" condition of the subject of the experiment. If experimenting to determine the effect of salt on freezing point of water, the control version of the experiment would be freezing water without any salt.
If experimenting to determine if plants grow faster in red light, the control version would be plants grown in full-spectrum light. Unfortunately, experimental terminology can get a little confusing. The control in an experiment isn't the same as the controlled variables.
The controlled variable definition science uses essentially states that controlled variables include all the variables the experimenter controls or keeps constant to prevent interference with the experimental results. For example, in the water-and-salt freezing experiment controlling the variables would mean using the same type of water for all experiments, using the same amount of water, the same size and shape of container to freeze the water, the same freezer, and the same measurement tool and technique.
Every factor of the control plain water and the experiment water with salt would be exactly the same except the salt. The manipulated variable in an experiment is the one variable of the experiment that the scientist decides will change.
The manipulated variable may also be called the independent variable. In a properly designed experiment, there will be only one manipulated variable. In the salt and water experiment, for example, the manipulated variable is the amount of salt added to the water.
In the plant experiment, the manipulated variable is the light. Every other aspect of the experiment should be exactly the same between experimental groups and between test or trial runs. One responding variable definition says the responding variable is what will be measured in the experiment.
The responding variable, also called the dependent variable, is what the scientist measures as the experiment progresses.
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