Group Discussion

Identify two time-related factors that can threaten the internal validity of a within-subjects research study.  In each case, explain how the factor could be a confounding variable.

Describe the process of counterbalancing and explain how this process reduces the risk of problems from order effects.

Chapter 9

Within & Between designs

Within Subjects

Students Phonics Whole Word

A 12 15

B 13 14

C 15 14

D 14 15

E 15 14

Between Subjects

Students Phonics Whole Word

A 12  

B 13  

C 15  

D 14  

E 15  

F   15

G   14

H   14

I   15

J   14

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.

Advantages of Within- Subjects Designs2 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.

Examples

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.

Threats to internal validity for within- subjects designs1- Confounding from environmental

variables (room difference, light difference, temperature difference)

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

3- participant attrition

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

4- Order Effects

• Carryover effect• Contrast effect (lighting, boring teacher)

• Progressive error• Practice effect• Fatigue effect• Reduced motivation effect

Dealing with time- related threats and order effects

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

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.

3- Counterbalancing

The process of matching treatments with respect to time is called counterbalancing.

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

Order effect

Pop Music Classic Music Generic Brand Generic Brand

20 27   20 32 (27+5)   20 32 (27+5)23 29   23 34 (29+5)   23 34 (29+5)25 29   25 34 (29+5)   25 34 (29+5)19 26   19 31 (26+5)   19 31 (26+5)26 31   26 36 (31+5)   31 (25+5) 3117 22   17 27 (22+5)   22 (17+5) 2214 20   14 25 (20+5)   19(14+5) 2016 24   16 29 (24+5)   21 (16+5) 24

20 26 20 31 22.5 28.5

No Order effect

Counter Balanced

Limitations of Counterbalancing

Limitation 1

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

Counterbalancing

26-20 =6 28.5-22.5 =6

Pop music Classic Music Generic Brand

Limitation 2

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

Limitation 3When 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.

Limitation 4Number 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.

Number of Treatments

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.

Latin square

The Latin square is not a particularly good 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.

Limitation of Latin square

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)

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.

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.

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.