Consider if there were only two conditions: one student involved in the discussion or two. Even though we may see a decrease in helping by adding another person, it may not be a clear demonstration of diffusion of responsibility, just merely the presence of others.
The construct validity would be lower. However, had there been five conditions, perhaps we would see the decrease continue with more people in the discussion or perhaps it would plateau after a certain number of people. In that situation, we may not necessarily be learning more about diffusion of responsibility or it may become a different phenomenon. By adding more conditions, the construct validity may not get higher.
When designing your own experiment, consider how well the research question is operationalized your study. A common critique of experiments is that a study did not have enough participants. The main reason for this criticism is that it is difficult to generalize about a population from a small sample. At the outset, it seems as though this critique is about external validity but there are studies where small sample sizes are not a problem Chapter 10 will discuss how small samples, even of only 1 person, are still very illuminating for psychology research.
Therefore, small sample sizes are actually a critique of statistical validity. The statistical validity speaks to whether the statistics conducted in the study support the conclusions that are made. Proper statistical analysis should be conducted on the data to determine whether the difference or relationship that was predicted was found. The number of conditions and the number of total participants will determine the overall size of the effect.
With this information, a power analysis can be conducted to ascertain whether you are likely to find a real difference. When designing a study, it is best to think about the power analysis so that the appropriate number of participants can be recruited and tested more on effect sizes in Chapter To design a statistically valid experiment, thinking about the statistical tests at the beginning of the design will help ensure the results can be believed.
These four big validities—internal, external, construct, and statistical—are useful to keep in mind when both reading about other experiments and designing your own.
However, researchers must prioritize and often it is not possible to have high validity in all four areas. Morling points out that most psychology studies have high internal and construct validity but sometimes sacrifice external validity. Again, to manipulate an independent variable means to change its level systematically so that 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.
As discussed earlier in this chapter, 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. 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, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore not conducted an experiment. This distinction is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too.
For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals.
Thus the active manipulation of the independent variable is crucial for eliminating the third-variable problem. 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, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches.
We will discuss this type of methodology in detail later in the book. In many experiments, the independent variable is a construct that can only be manipulated indirectly. In such situations, researchers often include a manipulation check in their procedure. A manipulation check is a separate measure of the construct the researcher is trying to manipulate.
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. Saul McLeod , published What is a controlled experiment? Experimental Group. The group being treated, or otherwise manipulated for the sake of the experiment. Control Group. 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. 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. The two leftmost columns of Table 5. 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 in the two rightmost columns of Table 5.
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 5.
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 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. Measure content performance. Develop and improve products. List of Partners vendors. Share Flipboard Email. Todd Helmenstine. Todd Helmenstine is a science writer and illustrator who has taught physics and math at the college level.
He holds bachelor's degrees in both physics and mathematics. Updated January 13, Key Takeaways: Control vs. Experimental Group The control group and experimental group are compared against each other in an experiment. The only difference between the two groups is that the independent variable is changed in the experimental group. The independent variable is "controlled" or held constant in the control group.
A single experiment may include multiple experimental groups, which may all be compared against the control group. The purpose of having a control is to rule out other factors which may influence the results of an experiment. Not all experiments include a control group, but those that do are called "controlled experiments. A placebo isn't a substitute for a control group because subjects exposed to a placebo may experience effects from the belief they are being tested.
Featured Video. Cite this Article Format.
0コメント