Complex Experimental Designs
So far, we;re focused on the simplest possible experimental design
- one IV, with 2 levels, is manipulated
- one DV is measured
Complex Experimental Designs
- factorial design with 3 or more IVs
- - assignment to condition in a factorial design
- - More independent variables
- - possible outcomes of a 2x2 factorial design
More Independent Variables
- factorial designs= testing more than one IV in a single experiment
- you would do this is you if think it’s possible;le that the effect of one of the IVs on the DV
depends on the levels of the other IV (interaction)
- you would do this is you if think it’s possible;le that the effect of one of the IVs on the DV
- some independent variables’ in a factorial design are not actually manipulated
- these are called participant variables
- main effect: the effect each variable has by itself
- interaction: when the effect of one IV is different at different levels of the other IV
Possible Outcomes of a 2x2 Factorial design
- There may or may not be a significant main effect for independent variable A
2.There may or may not be a significant main effect for independent variable B - There may or may not be a “significant interaction between IV A and B” analysis of variance is the statistical procedure used to assess the statistical signifiicance of the main effects and interaction in a Factorial design
Possible Outcomes of a 2X2 Factorial Design
- There may or may not be a significant main effect for independent variable A
- There may or may not be significant main effect for independent variable B
- There may or may not be a siginificant interaction between IV A and B
- analysis of variance (AVOVA) is the statistical procedure used to assess the statistical significance of the main effects and interaction in a Factorial design
Assignment to condition In a Factorial design
- review of two basic options
- independent group design (random assignment of different people to each group) “within subjects design” - in a Factorial design
- can be completely independent groups, completely repeated measures, or a mixed Factorial design
Why should you not just design your experiment with as many different IVs as you can think of
- you will need a huge number of participants, or have to run a huge number of trials
- the statistical analysis gets really hard to understand
- you should be looking for IV combinations and interactions that make sense in the real world, not just any possible combinations
Factorial Designs with Manipulated and no manipulated variables
- IV*PV designs
- Used when testing how different types of individuals (ie introverts vs extroverts) react to the same manipulated variable
What is a 2x2 Factorial Design
2 independent variables each with 2 levels
simple main effects
analysis examines mean differences at each level of the independent variable.
Simple main effects, also known as simple effects, are the differences in cell means within a design. They are calculated by determining the effect of one independent variable at a specific level of another independent variable
independent groups (between Subjects design)
Different groups assigned to each condition
repeated measures (within subjects design)
Same individuals in all conditions
What is a curvilinear relationship and why is a complex experimental design necessary to identify this pattern
Curvilinear
Complex experimental design is necessary because it requires at least 3 levels of the independentt variable to show curvilinear relationships
How do you identify a main effect in a factorial design?
The direct effect of an independent variable on a dependent
If one is bigger than the other there is a mean affect effect
Explain what an interaction is in a factorial design. Use a example.
If there is an interaction the effect of one independent variable depends on the particular level of the other to find graph
no interaction
interaction
How are factorial designs labeled (ie what does a 2x3 factorial design mean)
Factorial designs are labeled based on the number of independent variables and the levels of each
When given a verbal description of an experiment with more than two independent variables be able to identify the design, What does each # signify?
The amount of numbers shown represents how many indpendent variables there are whale the number themselves represent the levels of each variable
2x3x3
3 independent variabels
How do you calculate the number of conditions in a factorial design (ex. how many conditions are there in a 2x3x3 design?
mulitiply
there would be 18
Imagine conducting the same experiment as an indepnendent groups, repeated measures, or mixed factorial design. How does the decision to use a certain design affected the number of participants you will need?
Independent groups of participants are gathered based on the number of conditions different groups for each
Repeated: same individuals for all conditions
Mixed: one group for 2 conditions in a 4 condition experiment
