Textbook Notes (362,870)
Psychology (3,261)
PSYC 2360 (93)
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Chapter 11

# Chapter 11-Factorial Designs

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School
University of Guelph
Department
Psychology
Course
PSYC 2360
Professor
N/ A
Semester
Fall

Description
Chapter 11: Factorial Designs • factorial experimental design: experimental designs with more than one independent (manipulated) variable • factor: each of the manipulated independent variables • factorial research designs are described with the number of factors that the experiment has and how many levels each factor has separated by an X eg. 2 x 2 x 3. you can find out the number of conditions by multiplying the number of levels in each factor • advantages: 1) more information can be gained 2) cheaper because less participants are needed 3) help researches draw conclusions about effect of IV on DV • conditions are arranged so that each IV occurs with each different IV aka crossing factors • conditions need to be equated before manipulations occur, which is done through random assignment or repeated-measures • research hypothesis indicates what the pattern of means is expected to be Main Effects and Simple Effects • marginal means: the means of the DV within the levels of any one factor, which are combined across the levels of one or more other factors in the design • main effect: differences on the dependent measure across the levels of any one factor, controlling for all other factors in the experiment • interaction: a pattern of means that may occur when the influence of one IV on the DV is different at different levels of another IV(s) • simple effect: the effect of one factor within a level of another factor eg. the effect of viewing violent versus nonviolent cartoons for frustrated children • interactions can be statistically tested to determine whether they're significant or not • F values and significance tests are displayed in anANOVAtable Sum of Squares df Mean square F Sig. Cartoon viewed 23.56 1 23.56 4.56 0.04 (significant ) Prior state 11.33 1 11.33 2 0.17 Cartoon viewed 29.45 1 29.45 5.87 0.03 x prior state (significant) Residual 41.33 36 5.17 (within groups) Total 94.67 39 59.51 • Numerator df is printed on line of variable, denominator df is printed in residual eg. F(1,36)=4.56 for cartoon viewed • an effect size statistic can be computed for each main effect and interaction, n, which indicates the size of the relationship between the manipulated IV (or the interaction) and the DV Reporting Results • determine which test statistics are significant viaANOVAtable • study condition means to see if they are in direction predicted in hypothesis • record means in chart (usually bar) or table Understanding Interactions
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