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This 3 page Class Notes was uploaded by Melanie Maino on Thursday March 31, 2016. The Class Notes belongs to PSYC314 at Towson University taught by Brianna Stinebaugh in Spring 2016. Since its upload, it has received 12 views. For similar materials see Introduction to Research Methods in Psychology in Psychlogy at Towson University.
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Date Created: 03/31/16
Chapter 10: Between Subject Design: Factorial Designs • Review of Between Subjects: Each participants is only receiving one level of the independent level; different participants for each treatment condition • Factorial Designs: 2 or more independent variables and each independent variable has at least two levels o Independent variables and factors are used interchangeably o Simplest factorial design is called 2 Factor Experiment because you are only dealing with two factors (or 2 IV) ▯ 2 kinds of information • 1-‐ Main Effect: information about the effects of each independent variable/ factor • 2-‐ Interaction: seeing if one independent variable has an influence on another independent variable /factor • Main Effects: the effects or actions of one independent variable o As we see a change in behavior we will see that there will be a change associated in value of independent variable o Number of independent variables= the number of possible significant main effects o ANOVA= analysis of variance (more than two variables) • Interactions: specifically looking at relationships that may exist between variables/factors o if effects of one IV depend on the effects of another IV= interaction o main effect of 1 factor (IV) altered by another factor= interaction o Number of possible main interactions depends of the number of IV ▯ Ex. 2 IV =1 possible interaction o More variables = the number of possible interactions increases o If you have more than two independent variables then you can get a higher order interaction (the number of possible interactions will increase) o 3 factor experiment ▯ interaction between all three variables ▯ also possible to have an interaction between two variables and not the third ▯ ex. success in the class room (DV) what influences it?-‐-‐> pick three variables (personality of prof., work load, & teaching style) o ANOVA will be run for significant interactions • Short hand notation helps us describe the actual design that we are using. o 1-‐ number of IV o 2-‐ number of levels per IV • 2 factor Experiment o 2x2 (2 by 2 factorial design) o numbers represent the factors present within the study o numerical value represent the actual number of levels for each IV • 2 factors o 1st factor= two levels o 2nd factor= two levels o 4 treatment conditions present • 2x3 o 2 factors (2,3) o treat. condition= 6 • 5x4x2x3 o 4 factors (5,4,2,3,) o treat. condition= 120 • 2x3x2 o 3 factors (2,3,2) o treat. condition= 12 April 6 Chapter 10… • Factorial designs should be as simple as possible: o 1-‐ a lot of participants; the more variables/ levels the more treatment conditions the more participants you will need o 2-‐ more treatment conditions the more the # of possible main effects and interactions increase o 3-‐ hard to identify all the specific interaction ▯ so your statistical analysis becomes harder Chapter 11 Within Subjects Designs • Within Subjects: when each participants receives all treatment conditions • NEED to measure each dependent variable after each treatment is given o Ex noise effects on concentration…DV= concentration IV= noise (quiet like the library, noisy like a café) • More than 1 independent variable (factor)▯ factorial design • Within subject design needs less participants than between subjects • Example…how quickly it takes to identify facial expressions from a picture? o IV= expressions in pictures (happy, sad, angry, embarrassed) o IV= sex (females, males) o 4X2= 8 treatment conditions ▯ female, happy ▯ female, sad ▯ female, angry ▯ female, embarrassed ▯ male, happy, ▯ male, sad ▯ male, angry ▯ male, embarrassed • Mixed Factorial Design: factorial design that combines one factor that is manipulated as a within subjects design and a second factor that is manipulated as a within subjects design and a second factor that is manipulated as a between subjects design (pre determined factor like age or gender) o Example: caffeine effecting men & women; 2 groups one women one men; 6 groups differentiated on two separate dimensions (caffeine and gender); 3X2 factorial design (6 treatment cond.)