1. Experimental Group Designs

2. Group Designs Simple Group Designs Complex 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

3. 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

4. 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)

5. Simple. Complex Independent Groups Repeated Measures # Levels/# IVs
Simple Complex Independent Groups Repeated Measures # Levels/# IVs # Levels/# IVs 1 IV > 1 IV 2 levels > 2 levels (bivalent) (multivalent)

6. Simple Group Designs Independent Group Designs
random selection designs
random assignment designs
matched group designs
natural group designs
Repeated Measurement Designs
Simple Correlational Designs

7. Simple Group Designs Involve 1 IV with 2 levels and 1 DV
the levels of the IV can be independent groups or repeated measurements

8. 4 Types of Independent Simple Group Designs
random selection designs
random assignment designs
matched group designs
natural group designs

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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.

16. 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)

17. 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

18. 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

19. 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)

20. 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)

21. 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

22. 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

23. 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)

24. 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

25. 2 x 2 Independent Group Design
Normal__________Disordered
male Grp 1 Grp 3
Sex
female Grp 2 Grp 4

26. 2 x 2 Independent Group Design
| N no interaction
| D
|_D__________
M F

27. 2 x 2 Independent Group Design
| N interaction
| N
|_D__________
M F

28. 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

29. 2 x 2 Repeated Measurement Design
Type of HA
HA1_______________HA2
noisy Grp 1 Grp 1
condition
quiet Grp 1 Grp 1

30. 2 x 2 Repeated Measurement Design
| 2
| 2 no interaction
| 1
|_1__________
N Q

31. 2 x 2 Repeated Measurement Design
| 1
| interaction
| 2
|_1__________
N Q

32. 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)

33. 2 x 2 Mixed Design Group Normal_______Disordered Pretest Grp 1 Grp 2
Posttest Grp 1 Grp 2

34. 2 x 2 Mixed Design Treatment Tx A_______Tx B Mild Grp 1 Grp 1
Severe Grp Grp 2

35. 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

36. 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

37. Number of Subjects Needed for Complex Group Designs
5-10 subjects per independent group or repeated measurement cell
Thus, a minimum of Ss would be needed for the 4 cells of a 2x2 factorial design and a minimum of Ss for the 60 cells of a 3x4x5 factorial design

38. 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

39. Types of Complex Correlation Designs
Partial correlation
Multiple correlation
Multiple regression
Factor analysis
Cluster analysis

40. 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

41. 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

42. 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

43. 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.

44. 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

45. Advantages of Group Designs
Provide information about interaction effects of IVs on the DV
generalizability of findings
demonstrate causal relationships

46. 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

47. 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