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More partial and semi-partial

More partial and semi-partial. The show. This is meant to be viewed as a slide show and not printed out. Hopefully it will help you understand partial, semi-partial (and average semi-partial) a little more. Dependent Variable The variance to account for = 16 Blocks Two potential predictors.

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More partial and semi-partial

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  1. More partial and semi-partial

  2. The show • This is meant to be viewed as a slide show and not printed out. Hopefully it will help you understand partial, semi-partial (and average semi-partial) a little more.

  3. Dependent Variable The variance to account for = 16 Blocks Two potential predictors

  4. Predictor 1 3/16 = ~19% variance accounted for by Predictor 1

  5. Predictor 2 4/16 = 25% variance accounted for by Predictor 2

  6. Predictor 1 and 2 The predictors share 2/9 = ~22% of their variance

  7. Model Variance Accounted For R2 = 6/16 = ~38% Variance Accounted for by the two predictors Note: It does not equal 25 + 19 or 44%. Why?

  8. Model Variance Accounted For The green areas represent the unique variance accounted for by each predictor

  9. Partial Correlation (squared)Variable 1 To find the partial correlation, take out all variance that is in some way explained by another variable Out of what’s left to explain, how much is the unique contribution of variable 1? 2/12 = ~.16

  10. Semi-Partial Correlation (squared)Variable 1 and 2 Semi-partial is conceptually simpler in that it is that unique variance accounted for (green) out of the total. For our first predictor it’s 2/16 or .125 For predictor 2 it’s 3/16 or ~.19.

  11. The average semi-partial (LMG) When predictor 1 is first, it’s semi-partial is: 3/16 = ~ .1875 When it is second it is as we discovered 2/16 previously .125 The average for predictor 1 is ~.16 The average semi-partial for predictor two is (.25 + .19)/2 = .22 Add them together and they equal our R2 for the model, ~.38

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