If it is something that varies in response to changes in another variable, it is a dependent variable. In many psychology experiments and studies, the dependent variable is a measure of a certain aspect of a participant's behavior. In an experiment looking at how sleep impacts test performance, the dependent variable would test performance because it's a measure of the participants' behavior. The independent variable is deemed independent because the experimenters are free to vary it as they need.
This might mean changing the amount, duration, or type of independent variable that the participants in the study receive as a treatment or condition.
One way to help identify the dependent variable is to remember that it depends on the independent variable. When researchers make changes to the independent variable, they then measure any resulting changes to the dependent variable.
How do researchers determine what a good dependent variable will be? There are a few key features that a scientist might consider:. Stability is often a good sign of a quality dependent variable. If the same experiment is repeated with the same participants, conditions, and experimental manipulations, the effects on the dependent variable should be very close to what they were the first time around. A researcher might also choose dependent variables based on the complexity of their study.
While some studies may only have one dependent variable and one independent variable, it is also possible to have several of each type of variable. Researchers might want to learn how changes in a single independent variable affect several distinct dependent variables. For example, imagine an experiment where a researcher wants to learn how the messiness of a room influences people's creativity levels. However, the research might also want to see how the messiness of a room might influence a person's mood.
The messiness of a room would be the independent variable, but the study would have two dependent variables: levels of creativity and mood. As you are learning to identify the dependent variables in an experiment, it can be helpful to look at examples. Here are just a few examples of psychology research using dependent and independent variables.
Understanding what a dependent variable is and how it is used can be helpful for interpreting different types of research that you encounter in different settings. When you are trying to determine which variables are which, remember that the independent variables are the cause while the dependent variables are the effect.
Ever wonder what your personality type means? Sign up to find out more in our Healthy Mind newsletter. National Library of Medicine. Dependent and independent variables. Steingrimsdottir HS, Arntzen E. It is called the "dependent" variable because we are trying to figure out whether its value depends on the value of the independent variable. If there is a direct link between the two types of variables independent and dependent then you may be uncovering a cause and effect relationship.
The number of dependent variables in an experiment varies, but there can be more than one. Experiments also have controlled variables. Controlled variables are quantities that a scientist wants to remain constant, and she or he must observe them as carefully as the dependent variables. For example, in the dog experiment example, you would need to control how hungry the dogs are at the start of the experiment, the type of food you are feeding them, and whether the food was a type that they liked.
If you did not, then other explanations could be given for differences you observe in how much they eat. For instance, maybe the little dog eats more because it is hungrier that day, maybe the big dog does not like the dog food offered, or maybe all dogs will eat more wet dog food than dry dog food.
So, you should keep all the other variables the same you control them so that you can see only the effect of the one variable the independent variable that you are trying to test. Similar to our example, most experiments have more than one controlled variable.
Some people refer to controlled variables as "constant variables. In the best experiments, the scientist must be able to measure the values for each variable. Weight or mass is an example of a variable that is very easy to measure. However, imagine trying to do an experiment where one of the variables is love. There is no such thing as a "love-meter.
So, love is not measurable in a scientific sense; therefore, it would be a poor variable to use in an experiment. In some experiments, time is what causes the dependent variable to change. The scientist simply starts the process, then observes and records data at regular intervals. When a scientist performs a test or survey on different groups of people or things, those groups define the independent variable.
For example:. For example, something might be either present or not present during an experiment. Here is a sample containing the variables and hypothesis. Menu Science Projects. Project Guides. View Site Map. Notice that although the words manipulation and control have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate the independent variable by systematically changing its levels and control other variables by holding them constant.
We will explore each validity in depth. Recall that two variables being statistically related does not necessarily mean that one causes the other. It could mean instead that greater happiness causes people to exercise the directionality problem or that something like better physical health causes people to exercise and be happier the third-variable problem. The purpose of an experiment, however, is to show that two variables are statistically related and to do so in a way that supports the conclusion that the independent variable caused any observed differences in the dependent variable.
The logic is based on this assumption : If the researcher creates two or more highly similar conditions and then manipulates the independent variable to produce just one difference between them, then any later difference between the conditions must have been caused by the independent variable.
An empirical study is said to be high in internal validity if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables—provides strong support for causal conclusions.
At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism.
In many psychology experiments, the participants are all undergraduate students and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. At first, this manipulation might seem silly. When will undergraduate students ever have to complete math tests in their swimsuits outside of this experiment?
The issue we are confronting is that of external validity. An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied.
As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to and participants encounter everyday, often described as mundane realism.
Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes.
Their study would be high in external validity and have high mundane realism if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store.
If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this increase would be true for other shoppers in other stores.
Their study would be relatively low in external validity, however, if they studied a sample of undergraduate students in a laboratory at a selective university who merely judged the appeal of various colours presented on a computer screen; however, this study would have high psychological realism where the same mental process is used in both the laboratory and in the real world.
We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Or consider field experiments that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy Cialdini, [5].
These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels.
The result was that guests who received the message that most hotel guests choose to reuse their towels reused their own towels substantially more often than guests receiving either of the other two messages.
Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels. A second reason not to draw the blanket conclusion that experiments are low in external validity is that they are often conducted to learn about psychological processes that are likely to operate in a variety of people and situations.
Let us return to the experiment by Fredrickson and colleagues. They found that the women in their study, but not the men, performed worse on the math test when they were wearing swimsuits. They argued, furthermore, that this process of self-objectification and its effect on attention is likely to operate in a variety of women and situations—even if none of them ever finds herself taking a math test in her swimsuit.
This conversion from research question to experiment design is called operationalization see Chapter 2 for more information about the operational definition. 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.
0コメント