In Box, Hunter, and Hunter's book "Statistics for Experimenters" the authors show an intriguing plot of the human population in Oldenburg on the y-axis versus the stork population on the x-axis during the years from The plot shows that as the stork population increases, the human population is increasing.
However, we know this is not the case. Instead, what may be happening, is that as there are more humans, there are more rooftops for storks to roost. If you truly believed that the storks were causing the human population to increase, then your experiment might be to take out your rifle and start shooting storks to see if the human population decreases as well.
Because we understand this system, however, we predict that it is highly unlikely that shooting storks will have any effect on the human population unless the shooter is Vice President Cheney.
Levine and Norenzayan described their sampling process as follows:. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds.
To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed.
Thirty-five men and 35 women were timed in most cities. Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. The second issue is measurement. What specific behaviours will be observed? They simply measured out a foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.
Often, however, the behaviours of interest are not so obvious or objective. The observers committed this list to memory and then practised by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed.
Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.
Coding generally requires clearly defining a set of target behaviours. The observers then categorize participants individually in terms of which behaviour they have engaged in and the number of times they engaged in each behaviour.
The observers might even record the duration of each behaviour. The target behaviours must be defined in such a way that different observers code them in the same way.
This difficulty with coding is the issue of interrater reliability, as mentioned in Chapter 5. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviours independently and then showing that the different observers are in close agreement.
Another approach to correlational research is the use of archival data , which are data that have already been collected for some other purpose. As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward.
Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home Figure 3.
Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other Figure 3. Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable also known as a third variable.
A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them.
Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline Figure 3. In this case, television viewing and aggressive play would be positively correlated as indicated by the curved arrow between them , even though neither one caused the other but they were both caused by the discipline style of the parents the straight arrows.
When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious. If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher.
Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately.
Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.
In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated.
Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session.
But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments. The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs.
In an experimental research design, the variables of interest are called the independent variable or variables and the dependent variable. The independent variable in an experiment is the causing variable that is created manipulated by the experimenter. The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation.
The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction.
This demonstrates the expected direction of causality Figure 3. Consider an experiment conducted by Anderson and Dill The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game Wolfenstein 3D or a nonviolent video game Myst.
During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable aggressive behaviour was the level and duration of noise delivered to the opponent.
The design of the experiment is shown in Figure 3. Two advantages of the experimental research design are a the assurance that the independent variable also known as the experimental manipulation occurs prior to the measured dependent variable, and b the creation of initial equivalence between the conditions of the experiment in this case by using random assignment to conditions.
Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation.
Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs. The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table.
Anderson and Dill first randomly assigned about participants to each of their two groups Group A and Group B. Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.
Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable the white noise blasts between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.
Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable and not some other variable that caused these differences.
The idea is that the only thing that was different between the students in the two groups was the video game they had played. Despite the advantage of determining causation, experiments do have limitations. Even if there is no third variable, it is impossible to tell which factor is influencing the other. Only experimental research can determine causation. In the above example, while a research could predict the likelihood of an alcoholic father having an alcoholic son, they could not describe why this was the case.
Of course, using contraception does not induce you to buy electrical appliances or vice versa. Instead, the third variable of education level affects both.
Another popular example is that there is a strong positive correlation between ice cream sales and murder rates in the summer. As ice cream sales rise, so do murder rates. Is this because eating ice cream makes us want to murder people?
The actual explanation is that when the weather is hot, more people buy ice cream, but they also go out more, drink more, and socialize more, leading to an increase in murder rates. Extreme temperatures observed in the summer also have been shown to increase aggression.
In this case, there are many other variables at play that feed the correlation between murder rates and ice cream sales. Experimental research tests a hypothesis and establishes causation by using independent and dependent variables in a controlled environment. Experimental research in psychology applies the scientific method to achieve the four goals of psychology: describing, explaining, predicting, and controlling behavior and mental processes. A psychologist can use experimental research to test a specific hypothesis by measuring and manipulating variables.
By creating a controlled environment, researchers can test the effects of an independent variable on a dependent variable or variables. The psychologist randomly assigns some children to play a violent video game for 1 hour and other children to play a non-violent video game for 1 hour.
In this example, the independent variable is video game group. Our independent variable has two levels: violent video games and non-violent video games.
The dependent variable is the thing that we want to measure—in this case, aggressive behavior. In an experimental study, the independent variable is the factor that the experimenter controls and manipulates. This variable is hypothesized to be the cause of a particular outcome of interest. The dependent variable, on the other hand, depends on the independent variable, and will change or not because of the independent variable. The dependent variable is the variable that we want to measure as opposed to manipulate.
In a simple experiment, a researcher might hypothesize that cookies will make individuals complete a task quicker. In one condition, participants will be offered cookies if they complete a task, while in another condition they will not be offered cookies. In this case the presence of a reward receiving cookies or not is the independent variable, and the time taken to complete the task is the dependent variable. Effect of a Reward : Effects of receiving a cookie as a reward independent variable on time taken to complete task dependent variable.
As shown in the figure, participants who received a cookie took much less time to complete the task than participants who did not receive a cookie. An experiment can have more than one independent variable. A researcher might decide to test the hypothesis that cookies will make individuals work harder only if the task is easy to begin with.
In this case, both the presence of a reward and the difficulty of the task would be independent variables. The purpose of an experiment is to investigate the relationship between two variables to test a hypothesis. By using the scientific method, a psychologist can plan and design an experiment that will answer the research question. The basic steps of experimental design are:.
The Scientific Method : The scientific method is the process by which new scientific knowledge is gained and verified. First you must identify a question and, after some preliminary research, form a hypothesis to answer that question. After designing an experiment to test the hypothesis and collecting data from the experiment, a scientist will draw a conclusion. The conclusion will either support the hypothesis or refute it.
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