Download presentation

Presentation is loading. Please wait.

Published byArianna Paver Modified over 2 years ago

1
Ch 10: Basic Logic of Factorial Designs & Interaction Effects Part 1: Oct. 28, 2014

2
Note: we’ll only cover p. 377-402 in Ch 10 (skip the calculation sections of this chapter “Advanced Topic: Figuring 2-Way ANOVA”) A 2-way ANOVA uses a factorial research design –Effect of two or more independent (group) variables examined at once –Efficient research design –Interaction of the 2 independent variables are possible Interaction effect: –Combination of variables has a special effect such that the effect of one variable depends on the level of another variable

3
Interaction Effects Example: Lambert et al study –Manipulated job description for flight attendant to give stereotype-appropriate or inappropriate info (1 factor); and manipulated mood (sad v. neutral – 2 nd factor) –A Factorial design – 2-way ANOVA (indicates 2 IV’s)

4
Basic Logic of Interaction Effects 2 way ANOVA includes a focus on: –2 possible main effects: Stereotype- appropriateness; Mood That is, regardless of mood, does stereotype appropriateness affect hiring decisions? And, regardless of stereotype-appropriateness, does mood affect hiring decisions? –1 possible interaction effect – does the impact of mood on hiring depend on stereotype appropriateness?

5
Cont. In 2-way ANOVA, with 2x2 table, each group is called a “Cell” Notice 4 cell means and 4 marginal means –Cell mean is each group’s mean –Marginal mean is overall mean for 1 var, regardless of group

6
2X2 Table (2-way ANOVA) Cell Mean 1 7.73 Cell Mean 2 5.80 Cell Mean 3 5.83 Cell Mean 4 6.75 Mood SadNeutral Stereotype Appropriate Inappropriate Marginal Mean 3 = 6.78 Marginal Mean 4 = 6.28 Marginal Mean 1= 6.77 Marginal Mean 4 = 6.29 Note: group sizes were equal

7
Basic Logic of the Two-Way ANOVA We calculate 3 F ratios: –Column main effect (for variable 1) –Row main effect (for variable 2) –Interaction effect (of variable 1 x variable 2) F ratios for the row and column main effects –Based on deviations from marginal means F ratio for the interaction effect –Based on deviations from cell means

8
Two-Way ANOVA Design a) Column between-gp variance based on diff between mean of column 1 (shaded) & column 2 (unshaded) b) Row between-gp variance based on diff between mean of row 1 (shaded) & row 2 (unshaded) c) Within-gps variance estimate is based on variation among scores in each cell

9
Cont. To examine main effects, focus on the marginal means –Main effect of Mood: what is compared ? –Main effect of Stereotype: what is compared? To examine the interaction, focus on pattern of cell means 7.735.80 5.836.75 SadNeutral Appropriate Inappropriate 6.786.28 6.77 6.29 Stereotype Mood

10
Interpreting Interactions: Examining 2x2 Tables –Is the difference in cell means across the 1 st row the same (direction and magnitude) as the difference in cell means in 2 nd row? –If yes (same direction AND magnitude) no interaction, –If no (different direction OR magnitude) interaction –Here, for stereotype-appropriate row, difference is 7.73-5.80= 1.93 –For stereotype-inappropriate row, difference is 5.83-6.75 = -.92 –So, in this example…does it ‘look’ like an interaction? –Examples on board of combinations of main effects and interactions

Similar presentations

Presentation is loading. Please wait....

OK

Chapter Fourteen The Two-Way Analysis of Variance.

Chapter Fourteen The Two-Way Analysis of Variance.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Burn ppt on dvd Ppt on current account deficit usa Download ppt on rotation and revolution of earth Ppt on group development stages Ppt on buddhism in india Ppt on 14 principles of henri fayol principles Ppt on instrument landing system suppliers Ppt on revolution and rotation of the earth Ppt on advanced construction materials and techniques Ppt on rocks soil and minerals