Documents
Resources
Learning Center
Upload
Plans & pricing Sign in
Sign Out

Randomized Block Design - Download as PowerPoint

VIEWS: 52 PAGES: 21

									Control of Experimental Error
  Blocking -
   – A block is a group of homogeneous experimental
     units
   – Maximize the variation among blocks in order to
     minimize the variation within blocks
  Reasons for blocking
   – To remove block to block variation from the
     experimental error (increase precision)
   – Treatment comparisons are more uniform
   – Increase the information by allowing the researcher
     to sample a wider range of conditions
Blocking
 At least one replication is grouped in a
  homogeneous area

      A B D A                 A B D C

      C D B C                 C D B A
      B A D C                 B A D C
      Just replication          Blocking
 Criteria for blocking
 Proximity or known patterns of variation in the field
  – gradients due to fertility, soil type
  – animals (experimental units) in a pen (block)
 Time
   – planting, harvesting
 Management of experimental tasks
  – individuals collecting data
  – runs in the laboratory
 Physical characteristics
  – height, maturity
 Natural groupings
  – branches (experimental units) on a tree (block)
 Randomized Block Design
 Experimental units are first classified into groups (or
  blocks) of plots that are as nearly alike as possible
 Linear Model: Yij =  + i +    j + ij
   – =     mean effect
   – βi =     ith block effect
   – j =     jth treatment effect
   – ij =    treatment x block interaction, treated as error
 Each treatment occurs in each block, the same number of
  times (usually once)
   – Also known as the Randomized Complete Block Design
   – RBD = RCB = RCBD
 Minimize the variation within blocks - Maximize the
  variation between blocks
Pretty doesn’t count here
Randomized Block Design
Other ways to minimize variation within blocks:
 Field operations should be completed in one
  block before moving to another
 If plot management or data collection is handled
  by more than one person, assign each to a
  different block
Advantages of the RBD
 Can remove site variation from experimental error and
  thus increase precision
 When an operation cannot be completed on all plots at
  one time, can be used to remove variation between runs
 By placing blocks under different conditions, it can
  broaden the scope of the trial
 Can accommodate any number of treatments and any
  number of blocks, but each treatment must be replicated
  the same number of times in each block
 Statistical analysis is fairly simple
Disadvantages of the RBD
 Missing data can cause some difficulty in the analysis
 Assignment of treatments by mistake to the wrong block
  can lead to problems in the analysis
 If there is more than one source of unwanted variation, the
  design is less efficient
 If the plots are uniform, then RBD is less efficient than
  CRD
 As treatment or entry numbers increase, more
  heterogeneous area is introduced and effective blocking
  becomes more difficult. Split plot or lattice designs may be
  better suited.
Uses of the RBD

 When you have one source of unwanted
 variation
 Estimates the amount of variation due to
 the blocking factor
Randomization in an RBD
 Each treatment occurs once in each block
 Assign treatments at random to plots within
 each block
 Use a different randomization for each block
Analysis of the RBD
 Construct a two-way table of the means and
  deviations for each block and each treatment level
 Compute the ANOVA table
 Conduct significance tests
 Calculate means and standard errors
 Compute additional statistics if appropriate:
  – Confidence intervals
  – Comparisons of means
  – CV
The RBD ANOVA
Source    df           SS                             MS                F

Total     rt-1         SSTot =

                                             
                                                  2
                         i  j Yij  Y
Block     r-1          SSB =                          MSB =            MSB/MSE

                                     
                                          2
                        t i Yi  Y                   SSB/(r-1)

Treatment t-1          SST =                          MST =            MST/MSE

                                         
                                              2
                        r j Y j  Y                  SST/(t-1)

Error     (r-1)(t-1)   SSE =                          MSE =
                       SSTot-SSB-SST                  SSE/(r-1)(t-1)


                  MSE is the divisor for all F ratios
Means and Standard Errors

Standard Error of a treatment mean               s Y  MSE r

Confidence interval estimate               L   i   Y i  t  MSE r


Standard Error of a difference               s Y  Y   2MSE r
                                                1   2




Confidence interval estimate on a difference
              L   1   2    Y1  Y 2   t  2MSE r

 t to test difference between two means                 t Y1  Y 2
                                                           2MSE r
Numerical Example
 Test the effect of different sources of nitrogen on
  the yield of barley:
   – 5 sources and a control
 Wanted to apply the results over a wide range of
  conditions so the trial was conducted on four
  types of soil
   – Soil type is the blocking factor
 Located six plots at random on each of the four
  soil types
ANOVA
  Source             df           SS          MS             F

  Total              23           492.36

  Soils (Block)      3            192.56     64.19       21.61**

  Fertilizer (Trt)   5            255.28     51.06       17.19**

  Error              15            44.52      2.97

 Source (NH4)2SO4 NH4NO3 CO(NH2)2 Ca(NO3)2 NaNO3 Control
  Mean      36.25         32.38    29.42   31.02     30.70       25.35

Standard error of a treatment mean = 0.86         CV = 5.6%
Standard error of a difference between two treatment means = 1.22
Confidence Interval Estimates
 40
 38
 36
 34
 32
 30
 28
 26
 24
 22
      (NH4)2SO4 NH4NO3   Ca(NO3)2   NaNO3   CO(NO2)2   Control

      34.41    30.54     29.19      28.86    27.59     23.51
      36.25    32.38     31.02      30.70    29.42     25.35
      38.09    34.21     32.86      32.54    31.26     27.19
 Report of Analysis
 Differences among sources of nitrogen were highly
  significant
 Ammonium sulfate (NH4)2SO4 produced the highest mean
  yield and CO(NH2)2 produced the lowest
 When no nitrogen was added, the yield was only 25.35
  kg/plot
 Blocking on soil type was effective as evidenced by:
   – large F for Soils (Blocks)
   – small coefficient of variation (5.6%) for the trial
Is This Experiment Valid?
Missing Plots
 If only one plot is missing, you
  can use the following formula:

           Yij = ( rBi + tTj - G)/[(r-1)(t-1)]
      Where:
        • Bi = sum of remaining observations in the ith block
        • Tj = sum of remaining observations in the jth treatment
        • G = grand total of the available observations
        • t, r= number of treatments, blocks, respectively

      Total and error df must be reduced by 1
      Used only to obtain a valid ANOVA
        - No change in Error SS
        - SS for treatments may be biased upwards
     Two or Three Missing Plots
            ^
            Yij = ( rBi + tTj - G)/[(r-1)(t-1)]
 Estimate all but one of the missing values and use the formula
 Use this value and all but one of the remaining guessed values
  and calculate again; continue in this manner until you have
  resolved all missing plots
 You lose one error degree of freedom for each substituted value

 Better approach: Let SAS account for missing values
   – Use a procedure that can accommodate missing values (PROC
     GLM, PROC MIXED)
   – Use adjusted means (LSMEANS) rather than MEANS
   – degrees of freedom are subtracted automatically for each missing
     observation
Relative Efficiency
 A way to measure the efficiency of RBD vs CRD
      RE = [(r-1)MSB + r(t-1)MSE]/(rt-1)MSE

                 MSECRD      Estimated Error for a CRD
            RE 
                 MSERBD      Observed Error for RBD

     r, t = number of blocks, treatments in the RBD
     MSB, MSE = block, error mean squares from the RBD
     If RE > 1, RBD was more efficient
     (RE - 1)100 = % increase in efficiency
     r(RE) = number of replications that would be required in
      the CRD to obtain the same level of precision

								
To top