Questions:

1. Questions: • What is the relationship between all the non-experimental and quasi-experimental designs and validity (internal and external)? • Are non-experimental and quasi-experimental strategies used very often? Are they viewed as creditable and valid? Are these experiments often repeated? • How does a researcher determine how many observations to take before and after a treatment?

2. More questions: • Time series and interrupted time-series designs seem to limit internal validity effects but will it also increase participant attrition? • How do you monitor environmental factors (cohort effects) when observing two groups in an experiment? • When you are referring to levels of the independent variable, if you have two groups of participants – one that receives a drug and the other that receives a placebo, does this mean there are two levels of the independent variable? If not, what are the levels and how many are there?

3. Factorial Designs Chapter 11 Dusana Rybarova Psyc 290B May 30 2006

4. Outline • Introduction to factorial designs • Main effects and interactions • Types of factorial designs

5. 1. Introduction to factorial designs • in real life variables rarely exist in isolation • to examine these more complex, real-life situations, researchers often design research studies that include more than one independent variable (e.g. caffeine and alcohol)

6. 1. Introduction to factorial designs • in an experiment, an independent variable is often called a factor, especially in experiments that include two or more independent variables • a research design that includes two or more factors is called a factorial design • this kind of design is often referred to by the number of its factors, as a two-factor design or a three-factor design • a research study with only one independent variable is often called a single-factor design

7. 1. Introduction to factorial designs • each factor is usually denoted by a letter (A, B, C) • factorial designs use a notation system that identifies both the number of factors and the number of values or levels that exist for each factor • e.g. caffeine (3 levels) and alcohol study (2 levels) would be described as 3 x 2 two factor design

8. 2. Main effects and interactions • the main differences among the levels of one factor are called the main effect of that factor • when the research study is represented as a matrix with one factor defining rows and the second factor defining the columns, then the mean differences among the rows define the main effect for one factor, an the mean differences among the columns define the main effect for the second factor

9. 2. Main effects and interactions • an interaction between factors occurs whenever the mean differences between individual treatment conditions, or cells, are different from what is predicted from the overall main effect of the factors • when the effects of one factor depend on the different levels of a second factor, then there is an interaction between the factors • when the results of a two-factor study are graphed, the existence of nonparallel lines (lines that cross or converge) is an indication of an interaction between the two factors

10. Main effects of caffeine and alcohol no interaction

11. Main effects of caffeine and alcohol interaction

12. 3. Types of factorial designs • between-subjects designs • there is a separate group of participants for each of the treatment conditions • large number of participants – e.g. 20 participants in each condition for a 2 x 4 design means 160 participants • within-subjects designs • single group of individuals participates in all of the separate treatment conditions • only 20 participants for 2 x 4 factorial design

13. 3. Types of factorial designs • mixed-designs (with respect to factors) • between subjects design can apply to one factor and a within-subjects design is preferable for a second factor (e.g. mood as between subjects factor and memory as within subjects factor) • a factorial study that combines two different research designs is called a mixed design • a common example of a mixed design is a factorial study with one between-subjects factor and one within-subjects factor

14. 3. Types of factorial designs • Experimental and nonexperimental or quasi-experimental research strategies • a factorial study that combines two different research strategies • a common example of a mixed design is a factorial study with one experimental factor and one nonexperimental factor (e.g. gender differences in memory tests)

15. 3. Types of factorial designs • Pretest-posttest control group designs • Quasi-experimental (two factor mixed design) • One factor between subjects – treatment type • Second factor within subjects – pre-post test O X O (treatment group) O O (control group) • Experimental version of the same design R O X O R O O Where R symbolizes random assignment of subjects into groups

16. 3. Types of factorial designs • Higher-order factorial designs • Complex designs involving three or more factors • Example of a three factor design examining two teaching methods (A), boys and girls (B) and first and second grade classes (C) • This three factor design can be summarized as 2x2x2 • 2 (two teaching methods) x 2 (boys and girls) x 2 (first and second grade)