Between- Subjects Design
Chapter 8
Review
Two types of Ex research
• Two basic research designs are used to obtain
the groups of scores that are compared in an
experiment:
• within-subjects design
• between-subjects design.
Within & Between designs
Within Subjects
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Between Subjects
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Between subjects limitations
1- More subjects required
To compare three different treatment conditions
with 30 scores in each treatment, the betweensubjects design requires 90 participants.
2-Group Difference
Individual differences, may lead to group
differences or assignment bias.
Example
• If the participants in one group are generally
older ( or smarter, or taller, or faster, etc.) than
the participants in the other group, then the
experiment has a confounding variable.
3- Larger variance
Increases variance which makes it hard to find
significant differences (explained later)
Two types of confounding variables
• Confounding from individual differences,
which is called assignment bias.
• Confounding from environmental variables.
one group may be tested in a large room and
another group in a smaller room.
Making equivalent groups
• Random Assignment ( Randomization)
• Matching Groups ( Matched Assignment)
• Holding Variables Constant or Restricting
Range of Variability
1-Random Assignment
• It is relatively easy, and does not require any
measurement or direct control of extraneous
variables.
• However, random assignment is not perfect and
cannot guarantee equivalent groups, especially
when a small sample is used. Pure chance is not a
dependable process for obtaining balanced
equivalent groups.
2-Matching Groups
• School records are used to determine the IQs of the
participants, and each student is classified as high IQ,
medium IQ, or low IQ. The high- IQ participants are
distributed equally between the two groups; half is
assigned to one group and the other half is assigned to
the second group using restricted random assignment.
• However, matching requires pre-testing to measure the
variable( s) being controlled,
• It can become difficult to match several variables
simultaneously.
3-Holding a variable constant
• For example, a researcher concerned about
potential IQ differences between groups could
restrict participants to those with IQs between
100 and 110.
• Holding a variable constant guarantees that
the variable cannot confound the research,
but this process limits the external validity of
the research results.
INDIVIDUAL DIFFERENCES AND
VARIABILITY
High variability can obscure any treatment
effects that may exist and therefore can
undermine the likelihood of a successful study.
Restricted range
40.4 50
Wide Range
39.6 49.2
Other threats to internal validity of betweensubjects designs
• Differential attrition (Mortality) (2 Dieting
Programs)
• Diffusion of treatments (communication
between groups)
• Compensatory equalization (computer lab)
• Compensation rivalry (John Henry)
• Resentful demoralization
STATISTICAL ANALYSES OF
BETWEEN- SUBJECTS DESIGNS
• single- factor /two- group design or simply the
two- group design
• a mean is computed for each group of
participants, and then an independentmeasures t-test is used to determine whether
there is a significant difference between the
means
Advantage
• It is easy to set up a two- group study,
• In addition, a two- group design provides the
best opportunity to maximize the difference
between the two treatment conditions; that
is, you may select opposite extreme values for
the independent variable.
Disadvantage of 2 groups
• The primary disadvantage of a two- group design is
that it provides relatively little information. With only
two groups, a researcher obtains only two real data
points for comparison.
Comparing Means for More Than
Two Groups
• a single- factor /multiple- group design may be
used. For example, a re-searcher may want to
compare driving performance under three
telephone conditions: while talking on a cell
phone, while texting on a cell phone, and
without using a phone.
ANOVA
• For this study, the mean is computed for each
group of participants, and a single- factor
analysis of variance ( ANOVA for independent
measures).
• When the ANOVA concludes that significant
differences exist, some form of post hoc test
or posttest is used to determine exactly which
groups are significantly different from each
other.
Advantage of ANOVA
• In addition to revealing the full functional
relationship between variables, a multiplegroup design also provides stronger evidence
for a real cause- and- effect relationship than
can be obtained from a two- group design.
Nominal and ordinal variables
• Because you cannot compute means for these
variables, you cannot use an independentmeasures t test or an ANOVA ( F test) to
compare means between groups.
• However, it is possible to compare proportions
between groups using a chi- square test for
independence
Example
Teaching methods
Traditional
Group Work
Computer Based
Math test
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