would now have to subtract each mean from every other mean to see if the difference between the means was larger than 1.97. Two means that differed by less than 1.97 would be considered to be equal (i.e., testing H0: 1 = 2, so retaining H0 is like saying that the two means don’t differ). After completing the post hoc analyses you would know exactly which means differed, so you would be in a position to completely interpret the outcome of the experiment. How large is the treatment effect? The measure of effect size suggested by Gravetter and Wallnau is or (eta squared). The r2 2 computation of this measure is quite simple: For the example we’ve been using, then, the effect size would be .17 (100/575). The closer is 2 to 0, the smaller the effect size. The closer 2 is to 1, the larger the effect size. A computational example RESEARCH QUESTION: Does alcohol consumption have an effect on driving ability? EXPERIMENT: Randomly select people and randomly assign them to one of three groups: No Alcohol Control Group, 1 Shot of Whiskey, 5 Shots of Whiskey. After consuming the alcohol, participants drive cars on a closed course. DV is the number of pylons knocked down as subjects drive around the course 5 times. STATISTICAL HYPOTHESES: H0: = = 0Shot 1Shot 5Shots H1: Not H0 DECISION RULE: If  Reject H0. FObt FCrit, = [depends on outcome of the FCrit(2,12) FMax test] DATA: 0 Shot 1 Shot 5 Shots 1 2 4 0 1 3 1 0 2 2 2 4 2 0 7 Sum (M) = 1.2 1.0 4.0 6.0 5.0 20.0 31.0 (G) (T) = X 10.0 9.0 94.0 113.0 = X2 2.8 4.0 14.0 20.8 SS 0.7 1.0 3.5 s2 SOURCE TABLE: Source Formula SS df MS F
would now have to subtract each mean from every other mean to see if the difference between the means was larger than 1.97. Two means that differed by less than 1.97 would be considered to be equal (i.e., testing H0: 1 = 2, so retaining H0 is like saying that the two means don’t differ). After completing the post hoc analyses you would know exactly which means differed, so you would be in a position to completely interpret the outcome of the experiment. How large is the treatment effect? The measure of effect size suggested by Gravetter and Wallnau is r2 or 2 (eta squared). The computation of this measure is quite simple: For the example we’ve been using, then, the effect size would be .17 (100/575). The closer 2 is to 0, the smaller the effect size. The closer 2 is to 1, the larger the effect size. A computational example RESEARCH QUESTION: Does alcohol consumption have an effect on driving ability? EXPERIMENT: Randomly select people and randomly assign them to one of three groups: No Alcohol Control Group, 1 Shot of Whiskey, 5 Shots of Whiskey. After consuming the alcohol, participants drive cars on a closed course. DV is the number of pylons knocked down as subjects drive around the course 5 times. STATISTICAL HYPOTHESES: H0: 0Shot = 1Shot = 5Shots H1: Not H0 DECISION RULE: If FObt  FCrit, Reject H0. FCrit(2,12) = [depends on outcome of the FMax test] DATA: 0 Shot 1 Shot 5 Shots 1 2 4 0 1 3 1 0 2 2 2 4 2 0 7 Sum (M) = 1.2 1.0 4.0 X (T) = 6.0 5.0 20.0 31.0 (G) X2 = 10.0 9.0 94.0 113.0 SS 2.8 4.0 14.0 20.8 s2 0.7 1.0 3.5 SOURCE TABLE: Source Formula SS df MS F
S.Ch12.docx