Chapter 9 WITHIN SUBJECTS DESIGN 1 Within Between

Chapter 9 WITHIN- SUBJECTS DESIGN 1

Within & Between designs Within Subjects Students A B C D E Phonics 12 13 15 14 15 Whole Word 15 14 14 15 14 Between Subjects Students Phonics Whole Word A B C D E F G H I J 12 13 15 14 15 15 14 14 15 14 2 /29

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. 3 /29

Advantages of Within- Subjects Designs 2 problems are reduced or eliminated in a within- subjects design. 1. Group differences • 2. High variance • 4 /29

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. 5 /29

Threats to internal validity for within- subjects designs 1 - Confounding from environmental variables (room difference, light difference, temperature difference) 6 /29

2 - Confounding from time- related factors. • • • History. Maturation. . Instrumentation. Testing effects. Statistical Regression 7 /29

3 - participant attrition Another potential problem for the within- subjects design is participant attrition. 8 /29

4 - Order Effects Carryover effect • Contrast effect (lighting in the cinema) • Progressive error • • Practice effect • Fatigue effect • Reduced motivation effect 9 /29

Dealing with time- related threats and order effects 10 /29

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 11 /29

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. 12 /29

3 - Counterbalancing The process of matching treatments with respect to time is called counterbalancing. 13 /29

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 14 /29

No Order effect Pop Music 20 23 25 19 26 17 14 16 Classic Music 27 29 29 26 31 22 20 24 20 26 26 -20 =6 Order effect Method A Method B 20 23 25 19 26 17 14 16 32 (27+5) 34 (29+5) 31 (26+5) 36 (31+5) 27 (22+5) 25 (20+5) 29 (24+5) 20 31 31 -20 =11 Counter Balanced Method A Method B 20 23 25 19 31 (26+5) 22 (17+5) 19(14+5) 21 (16+5) 32 (27+5) 34 (29+5) 31 (26+5) 22. 5 31 22 20 24 28. 5 -22. 5 =6 15 /29

Limitations of Counterbalancing 16 /29

Limitation 1 Counterbalancing balances the effect of ordering effect but it doesn’t eliminate it. So both means are inflated 17 /29

Limitation 2 A more serious problem is when counterbalancing adds order effect to some of the individuals but not to all. 18 /29

Limitation 3 When the order effect is not symmetrical One treatment might produce a larger order effect than the other treatment • (Math & Statistics) • • In such situations, the order effects are not symmetrical, and counterbalancing the order of treatments does not balance the order effects. 19 /29

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. 20 /29

Number of Treatments 21 /29

Partial counter-balancing One solution to this problem is to use what is known as partial counterbalancing. A simple and unbiased procedure for selecting sequences is to construct a Latin square. 22 /29

Latin square 23 /29

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. 24 /29

Random order One method for improving the Latin 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) 25 /29

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. 26 /29

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. 27 /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. 28 /29