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Good Afternoon !

Good Afternoon !. Please turn in HW Today… Short Review of Between & Within Design ANOVA Two-Way ANOVA One together- Open helping3-student version.sav file One by yourself. Reminders. Last “new material” before lab quiz 3

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Good Afternoon !

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  1. Good Afternoon! • Please turn in HW • Today… • Short Review of Between & Within Design ANOVA • Two-Way ANOVA • One together- • Open helping3-student version.sav file • One by yourself

  2. Reminders • Last “new material” before lab quiz 3 • The week after will be the Makeup lab quiz week-if you do not need to make up a quiz, you can skip that week!

  3. Between Design ANOVA Give values (ex.-treatment 1 =Drug therapy), enter scores down column Drug Therapy (treatment 1)-15, 13, 12, 11, 9, 8, 7 Psychoanalysis (treatment 2)- 13, 9, 10, 13, 5, 5, 5 Behavioral Therapy (treatment 3)- 11, 10, 9, 12, 7, 4, 2

  4. Repeated Measures ANOVA Last week we looked at the difference between the length of days a person spent at a clinic for three different treatments. Data was entered down the columns.(i.e. each row represents a person’s score at different points in time) Treatment 1- 15, 13, 12, 11, 9, 8, 7 Treatment 2- 13, 9,10,13, 5 ,5, 5, Treatment 3- 11, 10, 9, 12, 7, 4, 2

  5. Two-Way ANOVA • Two-way ANOVAs consider the effects of two variables that divide groups. These variables are called grouping variables. • Each grouping variable is a possible main effect. • If the result for a grouping variable, averaging across the other grouping variable(s) is significant, it is amain effect. • This is different from aninteraction effect, which is based on thecombination of grouping variables. • The means of one grouping variable alone are called marginal means. • There are three F ratios: one for the grouping variable spread across the columns (the column main effect), one for the grouping variable spread across rows (the row main effect) and one for the interaction effect.

  6. Helping3-studentversion.savWe will be doing a two-way ANOVA with gender and type of problem for the IVtohelp (Combined Help Measure-Quantity and Quality for the DV)

  7. We can highlight and cut/delete the columns between gender and problem to see gender and problem side by side

  8. Can you guess what 1 and 2 under gender mean?Go to variable view…

  9. Click on space under values to see what they are, ok, go back to data view

  10. Now, what do the values under problem mean? Back to variable view…

  11. Click on the space under values for problem to see what they are…ok, go back to data view

  12. Analyze-General Linear Model-Univariate

  13. Fixed Factors: Gender & Type of ProblemDependent Variable: Combined Help Measure-Quantity and Quality (tohelp)

  14. Fixed Factors (remember fixed factors are the IV): Gender & Type of ProblemDependent Variable: Combined Help Measure-Quantity and Quality (tohelp)Click on Post Hoc

  15. Post Hocs: Remember, post hocs tell us where the difference is so we don’t need to do a post hoc for gender because it only has two levels, so just move problem over

  16. Move problem over and click on LSD, continue

  17. To see what it looks like on a graph, Click on plots

  18. Gender on Separate LinesProblem on Horizontal Axis The different (separate) lines represent the levels/conditions/groups of one of your IVs, while the points on the X-axis represent the levels/conditions/groups of the other IV

  19. Gender on Separate LinesProblem on Horizontal Axis

  20. Gender on Separate LinesProblem on Horizontal AxisClick Add..continue

  21. What are we missing? Descriptives…click on options

  22. Move all variables over, select Descriptive Statistics…continue

  23. continue

  24. Here, we see that there are the assigned values of 1 for female, 2 for male. In addition we can see that there are 325 females in our sample and 212 males. We can also see the breakdown of type of problem experienced Descriptives box gives us our means and standard deviations as well as our N-the amount in our sample, so in our sample we have 119 females that help

  25. Tests of Between-Subjects Effects Check significance levels, if sig. (aka pvalue) level is below .05 (p<.05) then we have an effect. From the sig column we can see that gender has a sig value of .019. Since .019 is < .05, we can say that there is a main effect for gender; p<.05 F(1,529)=5.54, p<.05 Since we did find main effects, we need to look at the post hoc test to see where the differences are For problem, since .023 is <.05, then we have a main effect for problem too. p<.05…… F(3,529)=3.198, p<.05 Now, we look at the sig. value for gender and problem combined. Since .848 is greater than .05, not less than .05, there is nointeraction effect. If the value was below .05, we would say that there was an interaction effect. F(3, 529)=.269, n.s.

  26. A two way ANOVA was conducted to evaluate the effects of gender on quantity and quality of combined help measure. A main effect for gender was found F(1,529)=5.54, p<.05, and a main effect for type of problem was found F(3,529)=3.198, p<.05. However, there was no interaction effect between gender and type of problem found F(3, 529)=.269, n.s. Post Hoc analysis revealed…need to go to post hoc results

  27. Look to see which sig (aka pvalue) is less than .05. If sig. value less than .05, it is significant A two way ANOVA was conducted to evaluate the effects of gender on quantity and quality of combined help measure. A main effect for gender was foundF(1,529)=5.54, p<.05, and a main effect for problem was found F(3,529)=3.198, p<.05. However, there was no interaction effect found F(3, 529)=.269, n.s. Post Hoc LSD analyses revealed …

  28. . If the two (or more) lines are parallel there is no interaction between them (no interaction effect) – they do not need to cross over to have an interaction. The different (separate) lines represent the levels/conditions/groups of one of your IVs, while the points on the X-axis represent the levels/conditions/groups of the other IV

  29. Two-Way ANOVA on your own • Label variables and the levels of each as follows: • Gender (as name and label) • Men as 1 • Women as 2 • Therapy as the name of the second IV with “Therapy Type” as the label • Drug Therapy-value 1 • Talk Therapy-value 2 • Run the ANOVA with Gender and Therapy Type for the IV and SE (labeled Self-Esteem) for the DV. • Analyze-General Linear Model-Univariate • Fixed Factors • Dependent Variable: • Post Hocs: don’t need for gender because it is only 2 levels • Plots • Options: select Descriptives

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