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Lab 3

##               Df Sum Sq Mean Sq F value Pr(>F)    
## Age            2 1549.7   774.9 405.411 <2e-16 ***
## Condition      2 1198.9   599.5 313.645 <2e-16 ***
## Age:Condition  4   22.6     5.7   2.961 0.0246 *  
## Residuals     80  152.9     1.9                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Question

Does “Age” impact “Performance”? Yes

    1. What assumption must we test to include a variable as a blocking factor? In order to include a variable as a blocking factor, it is important that we perform an additional test of ‘Additivity of Interaction’. We must make sure that the blocking variable and the predictor/predictors under consideration have no interactions between them.
    1. Recognize the IV, DV, block and create a table for the following research statement. IV: Age groups 1,2,3 (60 – 69, 70 – 79, and above 80) DV: Performance_score Block : Conditions The design will be looked like:

BlockAge 1: 60-69 Age 2: 70-79 Age 3: Above 80 Condition 1 n= Condition 2 Condition 3

Results

After accounting for “Condition” (F(2,84) = 286.9, p < 0.001), there is a difference in group means among “Age” (F(2, 84) = 370.8, P < 0.001) We performed a post-hoc Tukey test to help determine pairwise significance. We found that there is a difference in all 3 combinations: 60-70, 60-80, and 70-80

  • Equal variances
  • Data Normal

Tables and Figures

Does Age impact Performance?
Age Count Mean Standard Deviation
60-69 29 32.45 3.59
70-79 31 27.94 4.36
80+ 29 22.14 3.98

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