Bonferroni Adjustment

Statistics Psychology. How to do a Bonferroni Adjustment for Planned Comparisons with Grouped Data

Psychology
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Bonferroni Adjustment

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Statistics Psychology. How to do a Bonferroni Adjustment for Planned Comparisons with Grouped Data
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Tags: Psychology
  1. Bonferroni Adjustment

    Slide 1 - Bonferroni Adjustment

    • A technique to manage Type I error in planned comparisons
  2. What is it?

    Slide 2 - What is it?

    • The often overlooked statistic to control for Type I error for PLANNED comparisons
    • If conduct several comparisons with same dataset then chances of Type I error increase (similar to running lots of T tests)
    • Change the acceptable alpha (p) value for a result to be significant
    • Follow a formula
    • There are actually a few formulas all called “Bonferroni Correction” if you look on the internet. Some of that is the type of application of stats (math, sociology, psychology, etc.) has different standards based on the types of analyses conducted in those fields.
    • For our class I will focus on a single formula…wait for it….
  3. Not for post hoc tests

    Slide 3 - Not for post hoc tests

    • Not for Scheffe, tukey, Bonferroni post-hocs (what we’ve been doing in labs)
    • Post hoc ASSUMES a lower bar of stringency in the data analysis
  4. Steps

    Slide 4 - Steps

    • Determine if your study needs to modify the p (alpha) value to control for Type I error for PLANNED Comparisons
    • If running more comparisons than levels of the IV/s
    • If one-way ANOVA it’s fairly easy. How many levels of the IV?
    • If 3 levels (groups). So can do (3-1) = 2 comparisons without adjustment
    • If 4 levels. (4-1) = 3 comparisons without adjustment
    • So if a 2 X 2 ANOVA, that’s 4 mean scores, can only do (4-1) 3 comparisons without adjustment
    • If a 2 X 3 ANOVA, can only do (6-1)= 5 comparisons without adjustment
    • 3 X 3 ANOVA, (?-1)
    • Determine how many comparisons you will be doing
  5. Example of Steps to conduct

    Slide 5 - Example of Steps to conduct

    • If Dr. Williams had 6 different conditions in experiment
    • Can do 5 comparisons without needing to correct alpha (stick with p<.05)
    • Otherwise alpha = (# conditions -1) (.05) / number of comparisons you want to do
    • If Dr. Williams wants to do 10 comparisons
    • (6-1) (.05)/10 = .025. Should use .025 as alpha test for significance (from Keppel Text)
    • Sorry, have to do this one by hand.
  6. Another example

    Slide 6 - Another example

    • Dr Taylor examining a 2 X 3 ANCOVA
    • Wants to do 7 planned comparisons
    • What is the alpha level?
    • Answer: (6-1)(.05)/7 = .04 (rounded)