1. Research Methods Experimental Method
only method that can establish “cause and effect”
important components include:
random sampling
random assignment
hypothesis
control and experimental groups
independent variable and dependent variable
control of subject and experimenter bias

2. Control The ability to establish cause and effect with experiments is
due to the degree of control possible in true experiments. There are three major means by which control is achieved:
Manipulation of the independent variable - altering a condition of the experiment believed to influence the dependent variable.
Constancy of conditions - maintaining conditions that may influence behavior at levels we choose or believe them to be (e.g., all Ss tested at or about the same time of day).
Elimination of extraneous variables - identifying those variables which may influence behavior and exclude them from the research situation (e.g., environmental distractions).

3. Independent Variables
An independent variable is one under the direct control of the experimenter (e.g., level of background white noise) or, if not under direct control, has been selected for inclusion in the experiment (e.g., gender) and is believed to influence behavior in a predictable manner.
Independent variables are manipulated in a variety of ways:
present different stimuli (e.g., complex vs. simple designs, drug dose, highlighter vs. no highlighter)
vary content in scenarios (e.g., three vs. five “friends”)
vary characteristics of “paper people” (e.g., male vs. female)
etc.

4. Dependent Variables
A dependent variable is a direct or indirect measure of behavior or mental process.
There a several categories of dependent variables:
frequency of response
length of response
strength of response
number of correct responses/number of errors
reaction time (RT)
rating scales
test scores

5. Threats to Internal Validity
We have seen how manipulation of the independent variable functions in research. The other two means of control are directed toward establishing internal validity - ensuring all plausible rival hypotheses are eliminated (i.e., “confounding”).
There are a number of common threats to internal validity:
history - specific events (extraneous variables) occurring between presentation of the independent variable and measurement of the dependent variable or between a first and second measurement (i.e., pretest-posttest), other than the independent variable.
maturation - processes within the Ss that change as a function of the passage of time (e.g., growing older, more hungry, tired, etc.).

6. Threats to Internal Validity
testing - the effects of taking a test upon the scores of a second test.
instrumentation - changes in calibration of measuring instruments or changes in scorers/observers.
differential selection - groups selected in such a way that may result in group biases (e.g., one group is more intelligent, more motivated, better educated, etc.).
experimental mortality - differential loss of Ss from treatment groups (e.g., illness, time constraints, death, etc.).

7. Pre-Experimental Designs
There are many instances in which true experiments are not possible. In those cases, pre-experimental or quasi-experimental designs are sometimes used. There are, however, threats to internal validity in each that must be acknowledged.
The following are three pre-experimental designs:
One-Group Pretest-
Posttest Design
O X O
Static-Group
Comparison
X O O
One-Shot Case Study
X O

8. Basic True Experimental Design
Ideally, we would like to have a small group or “sample” to study which represents the entire population. That can be accomplished if we use “random sampling” to select our sample.
Population
Random
Sampling
Random
Sample

9. Basic True Experimental Design
Unfortunately, we are seldom able to obtain such a sample. We must, therefore, often rely on using subjects who are readily available -- a “convenience sample” -- and split them into a minimum of two groups using a technique called “random assignment.”
Convenience
Sample
Random Assignment
Experimental
Group
Control Group

10. Basic True Experimental Design
Present
Independent Variable
Control Group
Experimental
Group
Measure
Dependent Variable
Compare
Groups

11. Basic Experimental Design
Have you ever wondered whether those “Highlighters” help you study? Let’s see how we could develop an experiment to test the following hypothesis:
Highlighters facilitate memory of facts read from textbooks.
All subjects will be given several pages to read. After they have done so, they will be
dismissed and asked to return to the experimental lab the next day.
Convenience
Sample:
Students in Gen. Psych. class
Random Assignment
Control Group
Experimental
Group

12. Basic Experimental Design
Present Independent Variable
Availability of Highlighter
No Highlighter
Highlighter Available
Control Group
Experimental Group
Measure
Dependent Variable
Number of correctly
recalled facts on quiz
Each subject is given five pages from an Intro Psych text and told to read the
pages carefully because they will be tested on the material. The subjects are
dismissed after they finish reading and asked to return the next day.
Compare
Groups

13. Expanding the Basic Design
The basic experimental design is limited to comparing two
groups. We can, however, increase its versatility by using
“factorial designs.”
Factorial designs use a slightly different terminology than
what we have been using:
Factor - an independent variable
Levels - different values of the independent variable
Treatment condition/group - a group exposed to one of the levels of the independent variable

14. Simple-Randomized Design
Factor A
a a a3
Levels S1 . n1 n2 n3

15. Between- vs. Within-SubjectsA
a a a3 S1 . n1 n2 n3 A
a a a3 S1 . n

16. Two-Factor Designs (completely crossed designs)
a a2
a3b1
a3b2 a3 b1 B b2
a1b1
a2b1
a1b2
a2b2
This is simply a combination of two simple-randomized designs.

17. Mixed Designs Within-subject Factor A a1 a2 a3 Between-subject Factor. n b1 B b2 S1 . n

18. Higher-Order Factorial Designs
a1b1c2 b1 B b2
a3b2c1 c2 c1 C
a a a3 A

19. Main Effects and Interactions
In addition to investigating several variables at one time to test for main effects, factorial designs permit us to investigate interactions as well.
Main effect - a significant influence of a factor on the
dependent variable.
Interaction - the simultaneous effects of two factors on the dependent variable that may be different than the effects of the factors individually.

20. Graphing Main Effects A a1 a2 b1 B b2 Main effects for Factors A and B
Main effect for Factor B
Main effect for Factor A
a a2
= b1
= b2

21. Graphing Interactions
a a2 b1 B b2
Disordinal interaction
Ordinal interaction
a a2