1 / 26

One-Way and Factorial ANOVA

One-Way and Factorial ANOVA. SPSS Lab #3. One-Way ANOVA. Two ways to run a one-way ANOVA Analyze  Compare Means  One-Way ANOVA Use if you have multiple DV’s, but only one IV Analyze  General Linear Model  Univariate Use if you have only one DV bc/ can provide effect size statistics

yanka
Download Presentation

One-Way and Factorial ANOVA

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. One-Way and Factorial ANOVA SPSS Lab #3

  2. One-Way ANOVA • Two ways to run a one-way ANOVA • Analyze  Compare Means  One-Way ANOVA • Use if you have multiple DV’s, but only one IV • Analyze  General Linear Model  Univariate • Use if you have only one DV bc/ can provide effect size statistics • More on this later (factorial ANOVA section)

  3. Method #1: Compare Means • First we have to test if we meet the assumptions of ANOVA: • Independence of Observations • Cannot be tested statistically, is determined by research methodology only • Normally Distributed Data • Shapiro-Wilk’s W statistic, if significant, indicates significant non-normality in data • Analyze  Descriptive Statistics  Explore • Click on “Plots”, make sure “Normality Plots w/Tests” is checked

  4. Testing Assumptions

  5. Testing Assumptions • Homogeneity of Variances (Homoscedasticity) • Tested at the same time you test ANOVA • Analyze  Compare Means  One-Way ANOVA • Click on “Options” and make sure “Homogeneity of variance test” is checked • If violated, use Brown-Forsythe or Welch statistics, which do not assume homoscedasticity

  6. Method #1: Compare Means • One-Way ANOVA • Analyze  Compare Means  One-Way ANOVA • “Dependent List” = DV’s; “Factor” = IV • Options • Descriptive • Fixed and random effects • Homogeneity of variance test • Levene’s Test: Significant result  Non-homogenous variances • Brown-Forsythe • Welch • Means plot

  7. Method #1: Compare Means

  8. Method #1: Compare Means

  9. Method #1: Compare Means • One-Way ANOVA • Post-Hoc • Can only be done if your IV has 3+ levels • Pointless if only 2 levels, just look @ the means • Click the test you want, either with equal variances assumed or not assumed • DON’T just click all of them and see which one gives what you want (that’s cheating), select the test you want priori

  10. Method #1: Compare Means • Contrasts • Click “Polynomial”, Leave “Degree” at default (“Linear”) • Enter in your coefficients • # of coefficients should equal # of levels of your IV • Doesn’t count missing cells, so if you have 3 levels, but no one in one of the levels, you should have 2 coefficients • Coefficients need to sum to 0

  11. Method #1: Compare Means • Contrasts • Enter in your coefficients • IV = Race – 1=Caucasian, 2=African American, 3=Asian American, 4=Hispanic, 5=Native American, 6=Other, BUT there were no Native Americans in the sample • If you want to compare Caucasians to “Other”, coefficients = 1, 0, 0, 0, -1 • Caucasians vs. everyone else = -1, .25, .25, .25, .25

  12. Method #1: Compare Means

  13. Method #2: Univariate • Univariate works for both one-way (1 IV) and factorial ANOVA’s (2+ IV’s) • Allows for specification of both fixed and random factors (IV’s) • Assumptions • Independence of Observations • Normally Distributed Data • Both same as one-way ANOVA

  14. Factorial ANOVA • Assumptions: • Homoscedasticity • Tested at the same time you test ANOVA • Click on Analyze  General Linear Model  Univariate • Click on “Options” and make sure “Homogeneity tests” is checked

  15. Factorial ANOVA • Options • Estimated Marginal Means • Displays means, SD’s, & CI’s for each level of each IV selected • If “Compare main effects” is checked, works as one-way ANOVA on each IV selected • “Confidence interval adjustments” allows you to correct for inflation of alpha using Bonferroni or Sidak method • Descriptive statistics • Estimates of effect size • Observed power • Pointless, adds nothing to interpretation of p-value and e.s. • Homogeneity tests • Levene’s test

  16. Factorial ANOVA

  17. Factorial ANOVA • Save • Don’t worry about this for now • Post Hoc • Select the IV for which you wish to compare all levels against all other levels (i.e. that you don’t plan to do planned comparisons on) • Click on the right arrow button so the IV is in the box labeled “Post Hoc Tests for” • Check the post hoc tests you want done, either with equal variances assumed or not assumed • Click “Continue”

  18. Factorial ANOVA • Plots • Horizontal Axis • What IV is on the x-axis • Separate Lines • Separate Plots

  19. Factorial ANOVA • The following graph has the IV “Race” on the horizontal axis and separate lines by the IV “Gender”

  20. Factorial ANOVA • Model • Allows you to: • Denote which main effects and interactions you are interested in testing (default is to test ALL of them) • Specify which type of sum of squares to use • Usually you won’t be tinkering with this

  21. Factorial ANOVA • Contrasts • Tests all levels within one IV • Concern yourself with Simple only for now • “Reference category” = What level all others are compared to (either first or last, with this referring to how they were numbered) • Can test specific levels within one IV with specific levels in another IV, but requires knowledge of syntax

  22. Factorial ANOVA

  23. Factorial ANOVA • Interpreting interactions • See graphs

More Related