Experimental research in psychology allows researchers to test cause-and-effect relationships by manipulating independent variables and observing their impact on dependent variables. There are various types of experimental designs, including pre-experimental, true experimental, quasi-experimental, and single-subject designs. True experiments employ random assignment and control groups to establish internal validity and draw causal conclusions. Researchers must consider ethical standards to protect participants and ensure research is conducted properly.
2. Experimental Research vs. Other Methods
âť– Can test for cause/effect relationships
âť– Manipulation of independent
variable(s)
Simply put:
Decisions about the forms and values of
the IV, as
well as about which group receives which
treatment
are at the sole discretion of the researcher
Tuesday,
3. Variables in Experimental Research
âť– Independent Variable:
âť– Experimental Variable, Cause, or Treatment
âť– The activity or characteristic the researcher believes
makes a difference
âť– Dependent Variable:
âť– Criterion Variable, Effect, or Posttest
âť– Outcome of the study
âť– Difference in group(s) that occurs as a result of the
manipulation of the IV
âť– Only constraint: must represent a measurable
outcome
Tuesday.
4. Characteristics of
Experimental Research
âť– Demanding & Productive, but...
âť– Produce the soundest evidence of
hypothesized cause-effect
relationships
âť– Difference between Correlational &
Experimental
Research:
âť– Correlational can be used to predict a
specific score for a
specific individual
âť– Experimental predicts more global
results
5. Steps in Experimental Research Study
1. Select and define problem.
2. Select subjects and [measurement]
instruments.
3. Select design.
4.Execute procedures.
5. Analyze data.
6.Formulate conclusions
6. Role of the Researcher
âť– Forms or selects groups
âť– Decides what will happen to each group
âť– Attempts to control all variables and factors
âť– Observes and measures effect on the groups
Every effort is made to make sure the 2 groups have
equivalent variables—except for the independent variable.
Tuesday,
7. Two Groups
âť– Experimental Group
âť– Receives the new treatment being investigated
âť– Control Group
âť– Receives a different treatment; or
âť– Receives same treatment as usual (i.e. is left
alone)
The Control Group is needed in order to
identify/measure any
differences observed as a result of the differing
treatments
8. Group Designs
âť– Two classes of experimental designs:
âť– Single-Variable: one independent variable; IV is
manipulated
❖ Three types—
âť– Pre-experimental
âť– True experimental*
âť– Quasi-experimental
âť– Factorial: two or more independent variables; at least one
IV
is manipulated
âť– Elaborate on single-variable designs;
âť– Investigates each variable independently and in
interaction
with other variables;
❖ Sky’s the limit
9. Pre-Experimental Designs
❖ One-Shot Case Study —
âť– One group exposed to one treatment then given posttest
❖ Don’t know level of group knowledge before the treatment!
âť– Sources of invalidity are not controlled!
❖ One-Group Pretest-Posttest Design —
âť– One group pretested, exposed to one treatment, then post tested
âť– Still a number of factors affecting validity that are not controlled!
âť– Other factors may influence any differences observed between the
pretest and posttest
❖ Static-Group Comparison —
âť– At least two groups; first receives new treatment; second receives
usual
treatment; both post tested
âť– Purpose of control group is to show how the experimental (first) group
would have performed had
they not received the new treatment
âť– Effective only to the degree that the two groups are equal to each
other
10. True Experimental Designs
❖ Pretest-Posttest Control Group Design —
âť– At least two randomly-assigned groups; both pretested for dependent variable;
one group then receives the new treatment; then both groups are post tested.
âť– Internal invalidity fully controlled by: random assignment, pretesting, & inclusion
of a control group
âť– Potential risk of interaction between the pretest and the treatment*
❖ Posttest-Only Control Group Design —
âť– Same as pretest-posttest design, except there is no pretest
âť– Subjects randomly assigned; exposed to independent variable; then post tested
âť– Mortality is not controlled for (no pretest), but may not be a problem anyway
❖ Solomon Four-Group Design —
âť– Random assignment of participants to one of four groups
âť– Two groups are pretested; two groups are not pretested
âť– One pretested group & one unpretested group receive the experimental treatment
âť– All four groups are post tested
âť– Combination of the two designs (above) - eliminates both sources of internal
invalidity!
11. Quasi-Experimental Designs
❖ Nonequivalent Control Group Design —
âť– Two or more existing groups pretested; administered treatment; and posttested.
❖ Participants’ assignment to groups is not random; assignment of treatments to
groups is random
âť– Invalidity sources include: regression, selection-treatment interactions (maturation,
history, and testing)
❖ Time-Series Design —
âť– One group repeatedly pretested; administered treatment; repeatedly posttested.
âť– Elaboration of the one-group pretest-posttest design; involves testing (pre- and
post-) more than once
âť– Advantage lies in confidence gained through significant improvement of group
scores between pretests and posttests
❖ Counterbalanced Designs —
âť– All groups received all treatments; each group receives treatment in a different
order than others.
âť– Any number of groups can be involved; limited only by the number of treatments;
# of groups = # of treatments
❖ Order of each groups’ receipt of treatment is determined randomly; each group is
posttested following each treatment
âť– Pretest usually not possible and/or feasible; often used on existing groups
âť– Weakness lies in potential for multiple-treatment interference; thus, should only be
used when this is not a concern
12. Factorial Designs
âť– Two or more independent variables; at least one is
manipulated by researcher
❖ Term “factorial” comes from the use of multiple
variables
with multiple levels
âť– 2 x 2 factorial design*
âť– Can get very complicated (2 x 3, 3 x 2, etc.)!
âť– Often employed after using a single-variable design;
❖ “Variables do not operate in isolation”
âť– Studies how variables behave at different levels**
13. Single-Subject Experimental Designs
❖ Also referred to as “single-case experimental designs”
âť– Used when sample size = 1; or for multiple individuals
considered as 1 group
âť– Variation of the time-series design
âť– Typically used as a study of behavioral change in an
individual
âť– Participant is own control; exposed to both non-
treatment &
treatment phases;
❖ Individual’s performance measured repeatedly during
all phases
âť– Non-treatment phase = A; Treatment phase = B
14. Validity in Single-Subject Experiments
âť– External Validity
âť– Frequent criticism due to lack of generalizability
âť– Can be counteracted through replication
âť– Internal Validity
âť– Repeated and Reliable Measurement
âť– If results are to be trusted, treatment must follow exact
same procedures every time
âť– Baseline Stability
âť– Provides basis for assessing the effectiveness of the
treatment; must do enough
baseline measurements to establish a pattern
âť– The Single Variable Rule
âť– Only one variable should be manipulated at any one time!
15. Types of Single-Subject Designs
âť– A-B-A Withdrawal Designs --
âť– The A-B* Design
âť– Establishment of baseline stability; treatment given
âť– Improvement during treatment = effectiveness of treatment
âť– The A-B-A Design
âť– Adds a second baseline measurement to the A-B design
âť– Improves validity IF behavior improves during the B phase, and
subsequently
deteriorates during the second A phase
âť– The A-B-A-B Design
âť– Adds a second treatment phase to the A-B-A design
âť– Could add strength to experiment IF behavior improves during
treatment twice!
âť– Eliminates ethical concerns from A-B-A design (ending with participant
not
receiving potentially effective treatment)
16. Types of Single-Subject Designs (cont’d)
âť– Multiple-Baseline Designs
âť– Alternative to the A-B design
âť– Used when unable to withdraw the treatment, or when it would be
unethical to do so
âť– Three basic types: across behaviors, across subjects, and across settings*
âť– Alternating Treatments Design
âť– Only valid design for assessing effectiveness of 2+ treatments in a single-
subject
context
âť– Rapid alternation of treatments for a single subject
âť– Treatments are alternated randomly
âť– Notice: no withdrawal phase, no baseline phase.
âť– Allows for the study of multiple treatments quickly and efficiently
âť– Could introduce multiple-treatment interference
17. Data Analysis/Interpretation
âť– Typically involves graphically-represented
results
âť– Design must be evaluated for adequacy;
then
treatment effectiveness is assessed
âť– Clinical Significance vs. Statistical
Significance
âť– t and F tests can be used to test for
statistical
significance
18. Infamous Cases of Unethical Research
âť– Tuskegee Syphilis Study (1932-1972)
âť– Nearly 400 African-American men were infected with syphilis
âť– Study conducted by Public Health Service
âť– Milgram Obedience to Authority Study (began 1961;
made public 1963)
âť– Residents of New Haven, CT recruited to participate in a study of
“memory and
learning”
âť– Participants asked to inflict electric shocks in increasing voltages
based on
“learner’s” incorrect answers (maximum voltage of 450 volts)
âť– Stanford Prison Experiment (1971)
❖ 24 students chosen as “prisoners,” while 9 “guards” were assigned to
3 shifts
âť– Shut down after 6 days (originally intended to take 2 weeks) due to a
deterioration of the experiment’s conditions and structure