Experimental Group Designs
Group Designs
• Simple Group Designs
– one IV with 2 levels
• 2 levels can be independent groups (Exp + Cont)
• 2 levels can be repeated measurements (pre/post)
• Complex Group Designs
– one or more IVs
• factorial designs
– more than 2 levels on the IV
– more than 1 DV
• multivariate designs
Need to keep 2 things in mind
simultaneously:
• Independent Variable
– # levels
• 2 levels (bi-valent) --> SIMPLE
• > 2 levels (multi-valent) --> COMPLEX
– # variables
• 1 IV (simple OR complex group design)
• 2 or more IVs --> COMPLEX
• Groups
– Independent Groups
– Repeated Measures
These 2 things can be mix-matched to come
up with different design combinations.
Ex: 2 IVs with 2 levels each in an
independent group design
(2 x 2 independent group design)
Simple
Complex
Independent Groups
Repeated Measures
# Levels/# IVs
1 IV
2 levels
(bivalent)
# Levels/# IVs
> 1 IV
> 2 levels
(multivalent)
Simple Group Designs
• Independent Group Designs
–
–
–
–
random selection designs
random assignment designs
matched group designs
natural group designs
• Repeated Measurement Designs
• Simple Correlational Designs
Simple Group Designs
• Involve 1 IV with 2 levels and 1 DV
• the levels of the IV can be independent
groups or repeated measurements
4 Types of Independent Simple
Group Designs
•
•
•
•
random selection designs
random assignment designs
matched group designs
natural group designs
Random Selection Designs
• 2 groups are randomly selected from the
same population
• one group receives one level of the IV and
the other group receives the other level
• the effect of varying the IV is indicated by
the difference between groups on the DV
• this simple design doesn’t provide much
control of subject variables such as age,
gender, and education which researchers
generally prefer to control
Random Assignment Designs
• When only a small population of subjects is
available, they can be randomly assigned to
one group or the other.
• This is the only difference from the random
selection designs, that is, subjects are
selected from a smaller population
• subject variables are controlled by allowing
them to vary randomly across both groups
Matched Group Designs
• One or more variables that may affect the
DV is held constant between groups by
matching the groups on those variables
• Thus the problem of subject variability that
was a problem in random selection designs
is overcome with this design
Matched Group Designs
• there are 2 types of matched group designs
– groups can be matched on the DV (e.g., vocabulary
skills, test scores, etc.)
– groups can be matched on variables that might affect
the DV (e.g., age, gender, education)
• this design is more useful to CD researchers
because of the small groups that are often
available to researchers
Natural Group Designs
• 2 groups selected from two different
populations
• In this design, the IV is a difference
between the groups created by nature that
exists prior to the selection of the groups.
• It is the effect of this IV (i.e., difference
between the groups) that is studied
Repeated Measurement Designs
• This design has a single group of subjects in
which the two levels of the IV are varied
within the same group of subjects
• this design is used when there are not
enough subjects available for two
independent groups or when it is more
efficient to carry out the experimental
procedures within one group
Repeated Measurement Designs
• The DV is assessed twice in a single group of
subjects
• the difference between the two measurements
demonstrates the effect of the IV
• a problem with this design is the practice effect of
repeating a measurement.
• Another problem is the order effect of
measurements administered to subjects.
– To control for this, the researcher should use
counterbalancing of the order of measurements
to subjects.
Number of Subjects Needed for
Simple Group Designs
• 20 (10 per group for independent groups
OR 20 for repeated measures)
• In CDIS, the absolute minimum would be
10 subjects (5 per group for independent
groups OR 10 for repeated measures)
Simple Correlational Designs
• Two different measures are obtained from
each subject in a single group for
determining if a relationship exists between
the two measures
– usually the IV and DV are not defined
– it is difficult to interpret the relationships found
in these designs
Complex Group Designs
• Complex designs extend the simple group
designs
• more than 1 IV may be studied; more than 2
levels of the IV may be studied; and more
than 1 DV may be examined
• In addition, independent group designs and
repeated measurement designs may be
combined
Designs with more than 2 levels
of the IV
• Independent Group Designs: more than two levels of
the IV is examined
– for example, comparing the effects of 3 levels of
training (method a, method b, control)
• Repeated Measurement Designs: assessing more than
two things.
– Order effects are still important so must counterbalance
the order of presentation of tests, assessments, or
measurements.
– EX: IV - type of hearing aid;
Levels - HA-1, HA-2, HA-3
same subjects are tested on all three levels (or HAs)
Designs with more than 1 IV
(Factorial Designs)
• Designs that vary two or more IV at same
time can provide detailed information
related to the complexity of the processes
and disorders of communication
• factorial designs can involve independent
groups, repeated measures, or both (mixed
factorial designs)
Designs with more than 1 IV
(Factorial Designs)
• The more complex the design, the greater
the number of experimental conditions (or
cells) in the factorial design
– two IV with 2 levels each is a 2 x 2 (4 cells)
– three IV with 2 levels each is a 2x2x2 (8 cells)
• with factorial designs, you can determine if
there are main effects of each of the IVs as
well as an interaction effect between the IVs
Designs with more than 1 IV
(Factorial Designs)
• Remember that factorial designs are
COMPLEX DESIGNS
• But, can have simple factorial designs and
complex factorial designs
Simple Factorial Designs
• The simplest factorial design has 2 IVs with
2 levels each
• the 2 IVs can be:
– both independent groups
– both be repeated measures
– one independent group and one repeated
measure (mixed factorial design)
2 x 2 Independent Group Design
• Two groups that differ with respect to 2
different IVs, e.g.,
– normal vs disordered; AND
– male vs female
2 x 2 Independent Group Design
Group
Normal__________Disordered
male Grp 1
Grp 3
Sex
female Grp 2
Grp 4
2 x 2 Independent Group Design
|
N
|N
|
D
|_D__________
M
F
no interaction
2 x 2 Independent Group Design
|
D
|N
|
N
|_D__________
M
F
interaction
2 x 2 Repeated Measurement
Design
• One group that received two different
measurements, e.g., tested HA 1 vs HA 2 in
noisy vs quiet conditions
– must control for order effects
2 x 2 Repeated Measurement
Design
Type of HA
HA1_______________HA2
noisy Grp 1
Grp 1
condition
quiet Grp 1
Grp 1
2 x 2 Repeated Measurement
Design
|
2
|2
|
1
|_1__________
N
Q
no interaction
2 x 2 Repeated Measurement
Design
|
1
|2
|
2
|_1__________
N
Q
interaction
2 x 2 Mixed Design
• Two groups that receive some assessment,
e.g., normal vs disordered (independent
group design) AND pretest vs posttest
(repeated measure design)
2 x 2 Mixed Design
Pretest
Group
Normal_______Disordered
Grp 1
Grp 2
Posttest
Grp 1
Grp 2
2 x 2 Mixed Design
Mild
Treatment
Tx A_______Tx B
Grp 1
Grp 1
Severe
Grp 2
Grp 2
Complex Factorial Designs
• Factorial designs can be made more
complex by increasing the number of IVs,
the number of levels of the IVs, or both
• there can be 3 or more IVs and 3 or more
levels of each IV
• these designs are interpreted the same way
as simple factorial designs, but there are
many more possible outcomes
Complex Factorial Designs
• The complex factorial design can provide
more information about the complex
interactions
• the limitation of complex factorial designs
are the number of subjects and the number
of experimental conditions required by the
design
Number of Subjects Needed for
Complex Group Designs
• 5-10 subjects per independent group or
repeated measurement cell
• Thus, a minimum of 20-40 Ss would be
needed for the 4 cells of a 2x2 factorial
design and a minimum of 300-600 Ss for
the 60 cells of a 3x4x5 factorial design
Complex Correlation Designs
• Simple correlation designs provide
information about the relationship between
2 variables
• complex correlation designs provide more
information about relationships
• these designs are usually considered
statistical techniques rather than designs
Types of Complex Correlation
Designs
•
•
•
•
•
Partial correlation
Multiple correlation
Multiple regression
Factor analysis
Cluster analysis
Partial Correlation
• Controls the effects that other variables may
have on the relationship between 2 variables
being examined.
• Partial correlation adjusts the correlation
between two variables that are being
examined by eliminating the effects of their
correlation with another variable
Complex Correlation Designs (con’t)
• Multiple Correlation
– Determines the relationship between criterion variables
and the predictor variable
• Multiple Regression
– Determines the relationship between each criterion
variable and the predictor variable
• Factor Analysis
– Measures that are highly correlated with each other are
grouped together with measures that are independent of
each other
• Cluster Analysis
– a method for grouping subjects on the basis of patterns
of deficit
Combined Correlation and Group
Designs
• Covariance designs
– statistical procedures used to control the effects
of variables that might influence the IVs
– similar in function to partial correlation
• Multivariate designs
– has more than 1 DV; it controls for the
correlations between DVs
Combined Correlation and Group Design
• Discriminant analysis designs
– a statistical procedure used to obtain a measure that
will best differentiate two or more disordered
groups from a normal group with regard to a
number of variables on which the groups have been
measured.
– A weighted score is calculated for all the measures
that best differentiate the groups
– the proportion that each measure contributes to the
total weighted score is varied until the weighted
score that best differentiates the two groups is
found.
Advantages of Group Designs
• Isolate the effects of the IVs by
systematically varying the levels of the IVs
to determine the effects on the DVs
– the IVs can be independent groups, repeated
measures, or both
• control the effects of other variables by
allowing them to vary randomly in random
selection designs, by holding them constant
in matched group designs, or by
systematically varying them in factorial
designs
Advantages of Group Designs
• Provide information about interaction
effects of IVs on the DV
• generalizability of findings
• demonstrate causal relationships
Disadvantages of Group Designs
• Difficulty obtaining large numbers of
subjects
– thus, groups may be small and the number and
levels of the IVs may be restricted
– such restrictions limit the interpretations of
results obtained with group designs and
decrease the knowledge that can be obtained
with these findings
Disadvantages of Group Designs
• Group averages may not adequately
represent the characteristics of individuals
• quantified measures of the DV may not
provide enough information
• may not apply to natural settings