Between-Subjects Factorial Design

Chapter Objectives Learn how to test more than one independent variable in the same experiment Learn about the main effects and interactions between variables Learn how to diagram and label factorial experiments Understand how to interpret effect from factorial experiments

More than one Independent Variable In real life, variable rarely occur alone. Experiment with more than one independent variables are efficient and provide more information than experiments with one independent variable. Factorial designs – designs with two or more independent variables Independent variables are called factors Two factor experiment – the simplest factorial design

Factorial Designs They give us information about the effects of each independent variable in the experiment – main effects They enable us to answer the question: How does the influence of one independent variable affect the influence of the other variable.

The Main Effects The action of a single independent variable in an experiment. A change in behavior associated with a change in the value of a single independent variable or factor in the experiment. There are as many main effects as there are factors

Looking for Interactions Factorial design allows us to test for the relationships between the effects of different independent variables An interaction is present if the effect of one independent variable changes across the levels of another independent variable. Alcohol alone or sleeping pill alone can reduce anxiety, together, they can be fatal The effects of one factor will change depending of the level of the other

Looking for Interactions

Looking for Interactions

Looking for Interactions

Looking of Interactions An interaction can tell us that there are limits to the effect of one or more factors Two independent variable = one interaction More than two independent variables result to more complex interactions – higher-order interactions It is possible to have interactions but no main effects Or significant effect with no interaction

Laying Out a Factorial Design Step 1: Indicate the 2 independent variables Factor 1 (Type of Name) Factor 2 (Lenght of Name)

Laying Out a Factorial Design Step 2: Indicate the levels of Factor 1 Factor 1 (Type of Name) Given Name Nickname Factor 2 (Lenght of Name)

Laying Out a Factorial Design Step 3: Indicate the levels of Factor 2 Factor 1 (Type of Name) Given Name Nickname Short Factor 2 (Lenght of Name) Long

Laying Out a Factorial Design Step 4: Indicate the 4 treatment conditions Factor 1 (Type of Name) Given Name Nickname Short Factor 2 (Lenght of Name) Long

Laying Out a Factorial Design Step 4: Indicate the 4 treatment conditions Factor 1 (Type of Name) Given Name Nickname Short given name Short Factor 2 (Lenght of Name) Long

Laying Out a Factorial Design Step 4: Indicate the 4 treatment conditions Factor 1 (Type of Name) Given Name Nickname Short given name Short nickname Short Factor 2 (Lenght of Name) Long

Laying Out a Factorial Design Step 4: Indicate the 4 treatment conditions Factor 1 (Type of Name) Given Name Nickname Short given name Short nickname Long give name Short Factor 2 (Lenght of Name) Long

Laying Out a Factorial Design Step 4: Indicate the 4 treatment conditions Factor 1 (Type of Name) Given Name Nickname Short given name Short nickname Long give name Long nickname Short Factor 2 (Lenght of Name) Long

Describing the Design 2 x 2 (two by two) factorial design Other methods of describing design variables: Factor-Labeling Method 2 x 2 (Type of Name x Length of Name) between-subjects factorial design 2 (name type) x 2 (name length) between-subjects factorial design Factor and Levels Method 2 x 2 (Type of Name: given, nickname x Length of Name: short, long) between-subjects factorial design 2 (given name or nick name) x 2 (short or long name) between-subjects factorial design

Describing the Design 2 x 3 x 2 factorial design There are 3 numbers which means there are 3 factors or independent variables Factor 1 has two levels Factor 2 has three levels Factor 3 has two levels There are 12 separate conditions

Describing the Design Factor 1: Sex of Communicator Male Communicator Female Communicator Factor 2: Communicator attractiveness Low Moderate High Factor 3: Subject sex Male Female There are 3 independent variables: Sex of the person delivering the persuasive communication, attractiveness of the communicator, and the sex of the subject listening to the communication. It is hypothesized that subjects will be more persuaded by a message delivered by a more attractive communicator, but only when the communicator is the opposite sex from the subject

Sample Experiment Women will eat less in the presence of an opposite-sex partner than in the presence of another of the same sex. Men’s eating behavior will not be influenced by their partners gender. Factor 1 (Sex of the Subject) Male Female Male Factor 2: (Sex to the subject’s Partner) Female DV: Eating behavior

Sample Experiment Significant effect for subject sex; significant interaction for females in the presence of males

Sample Experiment Findings: In general, men ate more crackers than women In general, the gender of the partner did not alter the number of crackers eaten There was a significant interaction between the sex of the subject and the type of partner.

Sample Experiment: Hypothetical Results No significant main effect or interaction

Sample Experiment: Hypothetical Results Significant main effect for partner’s sex

Sample Experiment: Hypothetical Results Significant main effect for subject sex

Sample Experiment: Hypothetical Results Two Significant main effects

Sample Experiment: Hypothetical Results Significant Subject sex by Partner sex interaction ; eating depends on both factors

Sample Experiment: Hypothetical Results Maximum significant subject sex by partner sex interaction; crossover interaction

Sample Experiment: Hypothetical Results Significant subject sex main effect. Significant partner sex main effect, and a significant subject sex by partner sex interaction

Measuring interactions in one key reason for doing factorial research. By looking at two or more variables together, we can assess whether the effect of one variable depends on the level of another variable

Choosing a Between Subjects Design Practical reasons for keeping factorial designs simple: More treatment condition means more subjects More treatment condition means more time to run the experiment More treatment condition means more time to do the statistical analysis Complicated design are virtually uninterpretable Four way interactions are practically impossible to conceptualize and explain 2 x 2 factorial design has 3 possible effects 2 x 2 x 2 factorial design has 7 possible effects

Will I need more than two levels of my independent variable to test What is my hypothesis? Does my hypothesis have more than one independent variable? YES NO I need a factorial design Will I need more than two levels of my independent variable to test my hypothesis? Now I will diagram it. Do I know of a subject variable that will have a big effect on my dependent variable? I need a multiple-group design Can I measure this variable? I will use the two-independent- groups design and assume that randomization controls for confounding from any subject variables i will use the two-matched-groups design to be sure my groups start out the same on this variable Forget it. I will have to use the two- independent=groups design and assume that randomization takes care of this variable How many treatment conditions do I have? Is one of them a control condition? Good Why not?