Chapter 10 Flashcards
Complex Experimental Designs
Complex experimental designs
When you are increasing the number of levels or the number of independent variables (factorial design).
Factorial design
A design with more than one independent variable.
Number of levels
number of doses or number of sessions, this is creating more levels. (Creating a line between control and experimental groups).
Main effects of factorial design
The effect of one independent variable on the dependent variable, averaging across the levels of the other independent variable.
Interaction effects of factorial designs
seeing if the multiple independent variables interact or not.
If the two main effects are parrallel?
There is no interaction effect.
If the two main effects are not parallel?
There is an interaction effect, they are not independent of each other.
2 by 2 factorial
when there are two independent variables.
when can you use a 2-way-ANOVA on a 2 by 2 factorial?
If the two independent variable are NOT independent of each other.
interaction effects
if the lines of the two independent variables are NOT parrels it interacts.
how does a Two-Way-ANOVA work?
1) calculate the difference between the two variables.
2) input the difference, and the two variables into a code in R-studio.
3) if the p-value is <0.05 continue to post hoc test.
Repeated measures design.
You have the same ‘n’ for the whole study and just move that ‘n’ throughout all the conditions within the study.
independent design
making sure each cell has the same number of people in them ex: same number of males and females.
Mixed factorial design
A mix between independent and repeated measures. You start with the same number of people in the independent versus dependent groups and then move those two groups into the next condition.
