{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

10_2way_2

# 10_2way_2 - Detailed Analyses of Interaction Effect When...

This preview shows pages 1–4. Sign up to view the full content.

1 Detailed Analyses of Interaction Effect When the interaction effect is significant, we might be interested in what exactly causes the interaction effect. There are several different ways to look at the interaction effect. For example: 1. Simple Main Effect 2. Simple Effect Comparisons 3. Partial Interaction Etc. # 1. Simple Main Effect • Compare cell means at a specific treatment level. (e.g.) • Simple main effect of B (length of course) at a 3 H 0 : β k at α 3 = 0 for all k or H 0 : µ 31 = µ 32 = µ 33 • Simple main effect of A (type of beat) at b 1 H 0 : α k at β 1 = 0 for all j or H 0 : µ 11 = µ 21 = µ 31 Estimated Marginal Means of Y Length of Course 15 10 5 Mean Attitude 60 50 40 30 20 10 Type of Beat 1.00 2.00 3.00 Estimated Marginal Means of Y Type of Beat 3.00 2.00 1.00 Estimated Marginal Means 60 50 40 30 20 10 Length 1.00 2.00 3.00 B at a 3 A at b 1

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
2 MANOVA attitude BY a(1,3) b(1,3) /error=within /design=b within a(1), b within a(2), b within a(3). (SPSS syntax) • Dependent variable = “attitude” • First factor = “a”, with levels from 1 to 3, and • Second factor = “b” with levels from 1 to 3. Error term = “withincell” Specifies the models to be analyzed. B at a 1 B at a 2 B at a 3 * * * * * * A n a l y s i s o f V a r i a n c e -- design 1 * * * * * * Tests of Significance for ATTUDE using UNIQUE sums of squares Source of Variation SS DF MS F Sig of F WITHIN CELLS 2250.00 36 62.50 B WITHIN A(1) 63.33 2 31.67 .51 .607 B WITHIN A(2) 103.33 2 51.67 .83 .446 B WITHIN A(3) 2613.33 2 1306.67 20.91 .000 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - (Output) SSB at a 1 + SSB at a 2 + SSB at a 3 = SSB + SSAB = 2780 • Effect of (time) is significant at only a 3 (inner city). • Partitioning sum of squares SSA b SSA SSAB k k q at = = + 1 1 at p j j SSB a SSB SSAB = = + Exactly the same MS error as the omnibus 2-way ANOVA
3 2. Simple Effect Comparisons • Contrast of cell means at a specific treatment level. (e.g.) • A contrast of factor B (time) → ψ Β 1 = (0.5, 0.5, -1) • Then, look at the contrast at a specific level of factor A (beat). • For example, ψ Β 1 at a 1 , ψ Β 1 at a 2 , and ψ Β 1 at a 3 . Estimated Marginal Means of Y Length of Course 15 10 5 Mean Attitude 60 50 40 30 20 10 Type of Beat 1.00 2.00 3.00 Estimated Marginal Means of Y Type of Beat 3.00 2.00 1.00 Estimated Marginal Means 60 50 40 30 20 10 Length 1.00 2.00 3.00 MANOVA attitude BY a(1,3) b(1,3) /error=within /contrast (b) = special (1 1 1, 0.5 0.5 -1,-1 1 0) /partition (b) = (1,1) /design=b(1) within a(1), b(1) within a(2), b(1) within a(3).

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern