Chapter 13 Complete Block Designs

Randomized Block Design (RBD) g > 2 Treatments (groups) to be compared r Blocks of homogeneous units are sampled. Blocks can be individual subjects. Blocks are made up of t subunits Subunits within a block receive one treatment. When subjects are blocks, receive treatments in random order. Outcome when Treatment i is assigned to Block j is labeled Y ij Effect of Trt i is labeled  i (Typically Fixed) Effect of Block j is labeled  j (Typically Random) Random error term is labeled  ij Efficiency gain from removing block-to-block variability from experimental error

Randomized Complete Block Designs Model: Test for differences among treatment effects: H 0 :  1  g  0 (  1  g ) H A : Not all  i = 0 (Not all  g are equal) Typically not interested in measuring block effects (although sometimes wish to estimate their variance in the population of blocks). Using Block designs increases efficiency in making inferences on treatment effects

RBD - ANOVA F-Test (Normal Data) Data Structure: (g Treatments, r Subjects (Blocks)) Mean for Treatment i: Mean for Subject (Block) j: Overall Mean: Overall sample size: N = rg ANOVA:Treatment, Block, and Error Sums of Squares

RBD - ANOVA F-Test (Normal Data) ANOVA Table: H 0 :  1  g  0 (  1  g ) H A : Not all  i = 0 (Not all  i are equal)

Pairwise Comparison of Treatment Means Tukey’s Method- with  = (r-1)(g-1) Bonferroni’s Method - with = (r-1)(g-1), C=g(g-1)/2

Expected Mean Squares / Relative Efficiency Expected Mean Squares: As with CRD, the Expected Mean Squares for Treatment and Error are functions of the sample sizes (r, the number of blocks), the true treatment effects (  1,…,  g ) and the variance of the random error terms (  2 ) By assigning all treatments to units within blocks, error variance is (much) smaller for RBD than CRD (which combines block variation&random error into error term) Relative Efficiency of RBD to CRD (how many times as many replicates would be needed for CRD to have as precise of estimates of treatment means as RBD does):

Example - Caffeine and Endurance Treatments: g=4 Doses of Caffeine: 0, 5, 9, 13 mg Blocks: r=9 Well-conditioned cyclists Response: y ij =Minutes to exhaustion for cyclist dose i Data:

Example - Caffeine and Endurance

Would have needed 3.79 times as many cyclists per dose to have the same precision on the estimates of mean endurance time. 9(3.79)  35 cyclists per dose 4(35) = 140 total cyclists

Latin Square Design Design used to compare g treatments when there are two sources of extraneous variation (types of blocks), each observed at g levels Best suited for analyses when g  10 Classic Example: Car Tire Comparison –Treatments: 4 Brands of tires (A,B,C,D) –Extraneous Source 1: Car (1,2,3,4) –Extraneous Source 2: Position (Driver Front, Passenger Front, Driver Rear, Passenger Rear)

Latin Square Design - Model Model (g treatments, rows, columns, N=g 2 ) :

Latin Square Design - ANOVA & F-Test H 0 :  1 = … =  g = 0 H a : Not all  i = 0 TS: F obs = MST/MSE = (SST/(g-1))/(SSE/((g-1)(g-2))) RR: F obs  F , g-1, (g-1)(g-2)

Pairwise Comparison of Treatment Means Tukey’s Method- with  = (g-1)(g-2) Bonferroni’s Method - with = (g-1)(g-2), C=g(g-1)/2

Expected Mean Squares / Relative Efficiency Expected Mean Squares: As with CRD, the Expected Mean Squares for Treatment and Error are functions of the sample sizes (g, the number of blocks), the true treatment effects (  1,…,  g ) and the variance of the random error terms (  2 ) By assigning all treatments to units within blocks, error variance is (much) smaller for LS than CRD (which combines block variation&random error into error term) Relative Efficiency of LS to CRD (how many times as many replicates would be needed for CRD to have as precise of estimates of treatment means as LS does):

Replicated Latin Squares To Increase Power (and Error degrees of freedom), experimenters often will use multiple (m>1) gxg latin squares for their design. There are 3 possible model structures: Model 1: Separate Row and Column blocks used in each square Model 2: Common Row, but separate Column blocks used in each square Model 3: Common Row and Column blocks used in each square

Model 1 – Separate Row and Column Blocks

Model 2 – Common Row, Separate Column Blocks

Model 3 – Common Row and Column Blocks

Designs Balanced for Carry-Over Effects Subjects receive g treatments, one in each of g time periods Treatments are balanced with equal number of replicates per time period (across subjects) Design balanced such that each treatment follows each other treatment equal number of times and appears in the first position equal number of times. Carryover effect that observation in Period 1 receives is 0

Example – Milk Yield

Example 13.2 – Factor/Carryover Coding Note: Trt1,Trt2 and Res1,Res2 are coded so thatTrt and Res Effects sum to Zero (“Trt3 = -Trt1-Trt2” and “Res3 = -Res1-Res2”, with no Res effects in Period 1)