V. Key Terms

(you will need to get very familiar with these terms to succeed in Psychology. You can also look in the glossary of terms we have provided for these and other important terms):

1) variable - any measurable condition, event, characteristic, or behavior that can be controlled or observed in a study.

Independent Variable (IV)- the variable that is manipulated by the researcher to see how it affects the dependent variable.

Dependent Variable (DV)- the behavior or response outcome that the researcher measures, which is hoped to have been affected by the IV.

2) control - any method for dealing with extraneous variable that may affect your study.

Extraneous variable - any variable other than the IV that may influence the DV in a specific way.

Example - how quickly can rats learn a maze (2 groups). What to control?

3) Groups (of subjects/participants) in an Experiment - experimental vs control

experimental group - group exposed to the IV in an experiment.

control group - group not exposed to IV. This does not mean that this group is not exposed to anything, though. For example, in a drug study, it is wise to have an experimental group (gets the drug), a placebo control group (receives a drug exactly like the experimental drug, but without any active ingredients), and a no-placebo control group (they get no drug...nothing)

both groups must be treated EXACTLY the same except for the IV.

4) Confound - occurs when any other variable except the IV affects the DV (extraneous variable) in a systematic way. In this case, what is causing the effect on the DV? Unsure.

Example - Vitamin X vs Vitamin Y. Group 1 run in morning, group 2 in afternoon. Do you see a problem with this? (I hope so)

Many things may lead to confounds (here are just two examples):

5) Experimenter Bias - if the researcher (or anyone on the research team) acts differently towards those in one group it may influence participants' behaviors and thus alter the findings. This is usually not done on purpose, but just knowing what group a participant is in may be enough to change the way we behave toward our participants.

6) Participant Bias (Demand Characteristics) - participants may act in ways they believe correspond to what the researcher is looking for. Thus, the participant may not act in a natural way.

7). Types of Experimental Designs: true experiment, quasi-experiment, & correlation.

a) The True Experiment: Attempts to establish cause & effect

To be a True Experiment, you must have BOTH - manipulation of the IV & Random Assignment (RA) of subjects/participants to groups.

1) manipulation of the IV - manipulation of the IV occurs when the researcher has control over the variable itself and can make adjustments to that variable.

For example, if I examine the effects of Advil on headaches, I can manipulate the doses given, the strength of each pill, the time given, etc.. But if I want to determine the effect of Advil on headaches in males vs females, can I manipulate gender? Is gender a true IV?

2) Random Assignment - randomly placing participants into groups/conditions so that all participants have an equal chance of being assigned to any condition.

b) Quasi-Experimental Designs: same as the true experiment, but now there is no random assignment of subjects to groups. Still have one group which gets the IV and one that does not, but subjects are not randomly assigned to groups.

There are many types of quasi designs (actually, too many to go into detail here). What is vital to know is that in all of them, there's a lack of RA.

c) Correlation: attempts to determine how much of a relationship exists between variables. It can not establish cause & effect.

1) to show strength of a relationship we use the Correlation Coefficient (r).

The coefficient ranges from -1.0 to +1.0:

-1.0 = perfect negative/inverse correlation

+1.0 = perfect positive correlation

0.0 = no relationship

positive correlation- as one variable increases or decreases, so does the other. Example. studying & test scores.

negative correlation - as one variable increases or decreases, the other moves in the opposite direction. Example. as food intake decreases, hunger increases.

THE BETWEEN vs WITHIN SUBJECTS DESIGN

1) Between-subjects design: in this type of design, each participant participates in one and only one group. The results from each group are then compared to each other to examine differences, and thus, effective of the IV. For example, in a study examining the effect of Bayer aspirin vs Tylenol on headaches, we can have 2 groups (those getting Bayer and those getting Tylenol). Participants get either Bayer OR Tylenol, but they do NOT get both. T

2) Within-subjects design: in this design, participants get all of the treatments/conditions. For example, in the study presented above (Bayer vs Tylenol), each participant would get the Bayer, the effectiveness measured, and then each would get Tylenol, then the effectiveness measured. See the differences?

VALIDITY vs RELIABILITY

Validity - does the test measure what we want it to measure? If yes, then it is valid.

For Example - does a stress inventory/test actually measure the amount of stress in a person's life and not something else.

Reliability - is the test consistent? If we get same results over and over, then reliable.

For Example - an IQ test - probably won't change if you take it several times. Thus, if it produces the same (or very, very similar) results each time it is taken, then it is reliable.

However, a test can be reliable without being valid, so we must be careful.

For Example - the heavier your head, the smarter you are. If I weighed your head at the same time each day, once a day, for a week, it would be virtually the same weight each day. This means that the test is reliable. But, do you think this test is valid (that is indeed measures your level of "smartness")? Probably NOT, and therefore, it is not valid.