11_RBF

11_RBF - Randomized Block Factorial Design(RBF-pq(Kirk...

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Randomized Block Factorial Design (RBF- pq ) (Kirk, pp.456-461) • CR- p could be improved by RB- p . • Similarly, CRF- pq can be improved by RBF- pq by blocking effect. • Assume n blocks n homogeneous groups of people, or n people with p x q repeated measures (model – additive model) () , ijk j k jk i ijk Y =µ+α +β + αβ +π +ε π i … the effect of block i . All other terms are the same as in CRF- pq . (example) • Police officer data • Assume that, prior to the experiment , officers were grouped by their pretest attitude scores in to 5 groups, where 9 officers in each group. • Then the experimenter randomly assigned 9 officers in each group to one of the 9 treatment levels 5 blocks, and 9 people in each block.
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49 34 10 26 27 21 28 25 24 s5 42 36 18 27 22 26 38 27 29 s4 53 39 20 45 31 34 29 36 33 s3 52 50 21 36 40 30 47 44 42 s2 64 41 31 46 35 39 48 43 37 s1 a3b3 a3b2 a3b1 a2b3 a2b2 a2b1 a1b3 a1b2 a1b1 (Table 10.6-1 in Kirk) (Data layout) (SPSS procedure) • Use “GLM-Univariate” procedure. • Exactly the same as the RB- p case, except • Specify 2 fixed factors, instead of 1 • Specify 3 main effects ( α j , β k , and π i ), and an interaction between the two fixed factors ( αβ ) jk
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Tests of Between-Subjects Effects Dependent Variable: SCORE 55125.000 1 55125.000 136.523 .000 1615.111 4 403.778 190.000 2 95.000 4.788 .015 634.889 32 19.840 1543.333 2 771.667 38.894 .000 634.889 32 19.840 1615.111 4 403.778 20.351 .000 634.889 32 19.840 1236.667 4 309.167 15.583 .000 634.889 32 19.840 Source Hypothesis Error Intercept
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11_RBF - Randomized Block Factorial Design(RBF-pq(Kirk...

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