1. Within- Subjects Design
Chapter 9
Within- Subjects Design

2. Within & Between designs
Within Subjects
Students
Phonics
Whole Word A 12 15 B 13 14 C D E
Between Subjects
Students
Phonics
Whole Word A 12 B 13 C 15 D 14 E F G H I J

3. repeated- measures
A within- subjects experimental design, also known as a repeated- measures experimental design, compares two or more different treatment conditions ( or compares a treatment and a control) by observing or measuring the same group of individuals in all of the treatment conditions being compared.

4. Advantages of Within- Subjects Designs
2 problems are reduced or eliminated in a within- subjects design.
1. Individual differences between groups can become a confounding variable. If the individuals in one treatment condition are noticeably different from the individuals in another treatment ( for example, smarter, faster, bigger, or older), the individual differences, rather than the treatments, may explain any observed differences.
2. The individual differences within each treatment condition can create high variance, which can obscure any differences between treatments.

5. Examples

6. Within subjects
a within- subjects design is generally more powerful than a between- subjects design; that is, a within- subjects design is more likely to detect a treatment effect than a between- subjects design.

7. Threats to internal validity for within- subjects designs
1- Confounding from environmental variables (room difference, light difference, temperature difference)

8. 2- Confounding from time- related factors.
History.
Maturation..
Instrumentation.
Testing effects.
Statistical Regression

9. 3- participant attrition
Another potential problem for the within- subjects design is participant attrition.

10. 4- Order Effects Carryover effect
Contrast effect (lighting in the cinema)
Progressive error
Practice effect
Fatigue effect
Reduced motivation effect

11. Dealing with time- related threats and order effects

12. 1- Controlling Time
if the different treatment conditions are scheduled over a period of months, the chances greatly increase that an outside event ( history, maturation, or change in the measurement instrument) will have an influence on the results.
However if the time between treatments is too short other factors such as fatigue or reduced motivation may change the results

13. 2- Switch to a Between- Subjects Design
In some situations, order effects are so strong and so obvious that a researcher probably would not even consider using a within- subjects design. For example, a within-subjects design is a poor choice for a study comparing two methods of teaching reading to first- grade children. After the children have been taught with method I, they are permanently changed.

14. 3- Counterbalancing
The process of matching treatments with respect to time is called counterbalancing.

15. Easy Case
order effects evenly distributed between the treatment conditions
it doesn’t matter which treatment comes first
there is a constant (e.g., d=5 points) change due to order effect

16. No Order effect Order effect Counter Balanced 22.5 28.5 20 27
Pop Music
Classic Music
Generic
Brand 20 27
32 (27+5) 23 29
34 (29+5) 25 19 26
31 (26+5) 31
36 (31+5)
31 (25+5) 17 22
27 (22+5)
22 (17+5) 14
25 (20+5)
19(14+5) 16 24
29 (24+5)
21 (16+5)
22.5
28.5
26-20 =6
31-20 =11 =6

17. Limitations of Counterbalancing

18. Limitation 1
Counterbalancing balances the effect of ordering effect but it doesn’t eliminate it. So both means are inflated

19. Limitation 2
A more serious problem is when counterbalancing adds order effect to some of the individuals but not to all.

20. Limitation 3 When the order effect is not symmetrical
One treatment might produce a larger order effect than the other treatment
Example? (Math & Statistics)
In such situations, the order effects are not symmetrical, and counterbalancing the order of treatments does not balance the order effects.

21. Limitation 4 Number of treatments
With only two treatment conditions, complete counterbalancing is easy: There are only two possible sequences. However, as the number of treatments increases, complete counterbalancing becomes more complex.

22. Number of Treatments

23. Partial counter-balancing
One solution to this problem is to use what is known as partial counter-balancing.
A simple and unbiased procedure for selecting sequences is to construct a Latin square.

24. Latin square

25. Limitation of Latin square
The Latin square is not a perfect example of partial counterbalancing because it does not balance every possible sequence of treatment conditions. For example, the first three groups all receive treatment A followed immediately by treatment B.

26. Random order
One method for improving the square is to use a random process to rearrange the columns ( for example, a coin toss to decide whether or not each column is moved)

27. Statistical analyses With two treatment conditions,
a repeated- measures t test
For more than 2 treatments a
single-factor ANOVA ( repeated measures)
can be used to evaluate the statistical significance of the mean difference.

28. Ordinal & nominal scale
If the data are measured on an ordinal scale ( or can be rank ordered), a Wilcoxon test can be used to evaluate significant differences.
If the data includes only positive and negative (nominal) effects then we use a sign test.

29. Matched- Subjects Designs
In a matched- subjects design, each individual in one group is matched with a participant in each of the other groups.
The goal of a matched- subjects design is to duplicate all the advantages of within- and between- subjects designs without the disadvantages of either one.