module 3 part 2 correlation and experimental research

correlational research

Research in which the relationship between two sets of variables is examined to determine whether they are associated, or "correlated."

replicated research

Research that is repeated, sometimes using other procedures, settings, and groups of participants, to increase confidence in prior findings. in order to be fully confident about the meaning of research studies, results need to be replicated.

experimental manipulation

The change that an experimenter deliberately produces in a situation. Experimental manipulations are used to detect relationships between different variables.

more info on random assignment to condition

The experimenter might, for instance, flip a coin for each participant and assign a participant to one group when "heads" came up and to the other group when "tails" came up. The advantage of this technique is that there is an equal chance that participant characteristics will be distributed across the various groups. When a researcher uses random assignment—which in practice is usually carried out using computer-generated random numbers—chances are that each of the groups will have approximately the same proportion of intelligent people, cooperative people, extroverted people, males and females, and so on.

experiment

The investigation of the relationship between two (or more) variables by deliberately producing a change in one variable in a situation and observing the effects of that change on other aspects of the situation. Carrying out experiments is the only way psychologists can establish cause-and-effect relationships.

treatment

The manipulation implemented by the experimenter.

Experimental research

description: Investigator produces a change in one variable to observe the effects of that change on other variables. advantage: Experiments offer the only way to determine cause-and-effect relationships. disadvantages: To be valid, experiments require random assignment of participants to conditions, well-conceptualized independent and dependent variables, and other careful controls. ​

Descriptive and correlational research

description: Researcher observes a previously existing situation but does not make a change in the situation. advantages: Offers insight into relationships between variables. disadvantages: Cannot determine causality

correlation doesn't apply causation

if we find that more study time is associated with higher grades, we might guess that more studying causes higher grades. Although this is not a bad guess, it remains just a guess—because finding that two variables are correlated does not mean that there is a causal relationship between them. The strong correlation suggests that knowing how much a person studies can help us predict how that person will do on a test, but it does not mean that the studying causes the test performance. Instead, for instance, people who are more interested in the subject matter might study more than do those who are less interested, and so the amount of interest, not the number of hours spent studying, would predict test performance. The mere fact that two variables occur together does not mean that one causes the other.

examples of variables

in a study to determine whether the amount of studying makes a difference in test scores, the variables would be study time and test scores.

positive correlation

indicates that as the value of one variable increases, we can predict that the value of the other variable will also increase. For example, if we predict that the more time students spend studying for a test, the higher their grades on the test will be and that the less they study, the lower their test scores will be, we are expecting to find a positive correlation. The correlation, then, would be indicated by a positive number, and the stronger the association was between studying and test scores, the closer the number would be to +1.0. For example, we might find a correlation of +.85 between test scores and amount of study time, indicating a strong positive association.

no correlation

it's quite possible that little or no relationship exists between two variables. For instance, we would probably not expect to find a relationship between number of study hours and height. Lack of a relationship would be indicated by a correlation close to 0. For example, if we found a correlation of −.02 or +.03, it would indicate that there is virtually no association between the two variables

control group

A group participating in an experiment that receives no treatment.

random assignment to condition

A procedure in which participants are assigned to different experimental groups or "conditions" on the basis of chance and chance alone.

experimental group

Any group participating in an experiment that receives a treatment

Variables

Behaviors, events, or other characteristics that can change, or vary, in some way.

additional info on the two groups

In some experiments, there are multiple experimental and control groups, each of which is compared with another group. consider a medical researcher who thinks he has invented a medicine that cures the common cold. To test his claim, he gives the medicine one day to a group of 20 people who have colds and finds that 10 days later all of them are cured. Eureka? Not so fast. An observer viewing this flawed study might reasonably argue that the people would have gotten better even without the medicine. What the researcher obviously needed was a control group consisting of people with colds who don't get the medicine and whose health is also checked 10 days later. Only if there is a significant difference between experimental and control groups can the effectiveness of the medicine be assessed.

experiment's key elements

Like all experiments, it includes the following set of key elements, which you should keep in mind as you consider whether a research study is truly an experiment: • An independent variable, the variable that is manipulated by the experimenter. • A dependent variable, the variable that is measured by the experimenter and that is expected to change as a result of the manipulation of the independent variable. • A procedure that randomly assigns participantsto different experimental groups, or "conditions," of the independent variable. • A hypothesis that predicts the effect the independent variable will have on the dependent variable.

significant outcome

Meaningful results that make it possible for researchers to feel confident that they have confirmed their hypotheses. that they have confirmed their hypotheses. Using statistical analysis, researchers can determine whether a numeric difference is a real difference or is due merely to chance. Only when differences between groups are large enough that statistical tests show them to be significant is it possible for researchers to confirm a hypothesis.

info on correlation

The strength and direction of the relationship between the two variables are represented by a mathematical statistic known as a correlation (or, more formally, a correlation coefficient), which can range from +1.0 to −1.0.

independent variable (IV)

The variable that is manipulated by an experimenter.

dependent variable (DV)

The variable that is measured in an experiment. It is expected to change as a result of the experimenter's manipulation of the independent variable. The dependent variable is dependent on the actions of the participants or subjects—the people taking part in the experiment.

negative correlation

as the value of one variable increases, the value of the other decreases. For instance, we might predict that as the number of hours spent studying increases, the number of hours spent partying decreases. Here we are expecting a negative correlation, ranging between 0 and −1.0. The stronger the association between studying and partying is, the closer the correlation will be to −1.0. For instance, a correlation of −.85 would indicate a strong negative association between partying and studying.