Topic 6 Two-way designs Randomized Complete Block and

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Topic 6 Two-way designs Randomized Complete Block and

Topic 6. Randomized Complete Block Design (RCBD)

    [ST&D sections 9.1 9.7 (except 9.6) and section 15.8]

6.1. Variability in the completely randomized design (CRD)

    In the CRD, it is assumed that all experimental units are uniform. This is not always true in practice, and it is necessary to develop methods to deal with such variability. When comparing two methods of fertilization, if one region of the field has much greater natural fertility than the others, a treatment effect might be incorrectly ascribed to the treatment applied to that part of the field, leading to a Type I error. For this reason, when conducting a CRD, it is always advocated to include as much of the native variability of the experiment as possible within each experimental

    unit (e.u.), making each e.u. as representative of the whole experiment, and the whole experiment as uniform, as possible. In actual field studies, plots are designed to be long and narrow to achieve this objective. But if the e.u.'s are more variable, experimental error (MSE) is larger, F (MST/MSE) is smaller, and the experiment is less sensitive. And if the experiment is replicated in a variety of situations to increase its scope, the variability increases even further. This additional variability needs to be removed from the analysis so that the actual effects of treatment can be detected. This is the purpose of blocking.

6.2. Randomized complete block design (RCBD)

6.2.1. Definition

    The RCBD assumes that a population of experimental units can be divided into a number of relatively homogeneous subpopulations or blocks. The treatments are then randomly assigned to

    experimental units such that each treatment occurs equally often (usually once) in each block (i.e. each block contains all treatments). Blocks usually represent levels of naturally-occurring differences or sources of variation that are unrelated to the treatments, and the characterization of

    these differences is not of interest to the researcher. In the analysis, the variation among blocks

    can be partitioned out of the experimental error (MSE), thereby reducing this quantity and increasing the power of the test.

    6.2.2. Example: Consider a field trial comparing three cultivars (A, B, and C) of sugar beet with four replications (in this case, the field is divided into 12 plots; each plot is a replication / e.u.). Suppose the native level of soil nitrogen at the field site varies from high at the north end to low at the south end (see diagram). In such a situation, yield is expected to vary from one end of the field to the other another, regardless of cultivar differences. This violates the assumption that the

    error terms are randomly distributed since the residuals will tend to be positive at the north end of the field and negative at the south end.


    North end of field Hi N

     1 2 3

    4 5 6

    7 8 9

    10 11 12

    South end of field Low N

    One strategy to minimize the impact of this variability in native soil fertility on the analysis of treatment effects is to divide the field into four east-west blocks of three plots each.

    North end of field Block Hi N

     1 2 3 1

    1 2 3 2

    1 2 3 3

    1 2 3 4

    South end of field Low N

    Because these blocks run perpendicular to the nitrogen gradient, the soil within each of these blocks will be relatively uniform. This is the basic idea of the randomized complete block design. Remember that in the completely randomized design (CRD), each e.u. in the experiment has an

    equal chance of being assigned any treatment level (i.e. a single randomization is performed for the entire experiment). This is not the case in an RCBD. In the randomized complete block

    design (RCBD), each e.u. in a given block has the same chance of being chosen for each

    treatment (i.e. a separate randomization is performed for each block). Within each block, a fixed number (often 1) of e.u.'s will be assigned to each treatment level. The term "complete" refers to the fact that all treatment levels are represented in each block (and, by symmetry, that all blocks are represented in each treatment level).

    After the four separate randomizations, one for each block, the field could look like this:

    North end of field Block Hi N

     B A C 1

    A B C 2

    A C B 3

    A C B 4

    South end of field Low N


6.2.3. The linear model

In the case of a single replication per block-treatment combination (like the example above), the

    underlying linear model that explains each observation is:

    Y = + + ( + ijijij

Here, as before, represents the effect of Treatment i (i = 1,...,t), such that the average of each i

    treatment level is T. Now, in a similar way, ( represents the effect of Block j (j = 1,...,r), jii

    such that the average of each block is . As always, are the residuals, the deviations B(ijjj

    of each observation from their expected values. The model in dot notation:


And the sum of squares:

    trtrtr2222(YY)r(YY)t(YY)(YYYY) ............ijij??????ijij,,,,111111ijijij

     TSS = SST + SSB + SSE

     2Since the variance of means of n observations is /n, the coefficients r and t (within SST and 2 SSB, respectively) ensure that all mean squares are estimates of the same when there are no block or treatment effects. This is another example of partitioning of variance, made possible because the sums of squares of blocks and treatments are orthogonal to one another. This orthogonality is a direct result of the completeness of the block design.

6.2.4. ANOVA

ANOVA table for the RCBD (one replication per block-treatment combination):

    df SS MS F Source

    rt - 1 TSS Total

    t - 1 SST SST/(t-1) MST/MSE Treatments

    r - 1 SSB SSB/(r-1) Blocks

    (r-1)(t-1) TSS-SST-SSB SSE/(r-1)(t-1) Error

ANOVA table for the CRD:

    df SS MS F Source

    rt 1 TSS Total

    t - 1 SST SST/(t-1) MST/MSE Treatments


    t(r - 1) TSS-SST SSE/r(t-1) Error

    Due to the additional factor in the linear model, the ANOVA table for the RCBD has an additional row (Block) relative to that for the CRD. Notice that one consequence of this is that there are fewer degrees of freedom for error in the RCBD design than in the CRD design [(r-1)(t-1) vs. t(r-1), or (r - 1) fewer degrees of freedom]. In the RCBD, these (r 1) degrees of freedom

    have been partitioned from the error and assigned to the blocks.

Situation 1: No differences among blocks (i.e. no block effects)

    If the RCBD design were applied to an experiment in which the blocks were really no different from one another (i.e. there were no significant block effect), the MSE for the CRD would be smaller than the MSE for the RCBD simply due to the differences in error degrees of freedom. For example, if t = 3 and r = 4, MSE = SSE/9 and MSE = SSE/6. Therefore, CRDRCBD

    the F statistic for the CRD would be larger, meaning the CRD would be the more powerful (sensitive) design.

    To think of this another way, consider the general form of a confidence interval for the difference between two means (H: ): YY0AB0

    2;;CritStatisticMSEYY~ AB,dfMSEr

    If there are no block effects, the half-length of this confidence interval will be smaller for the CRD than for the RCBD for two reasons:

    1. The CRD will have a smaller critical value in the above formula due to its larger error

    degrees of freedom.

    2. MSE < MSE due to difference in error degrees of freedom. CRDRCBD

    The larger critical value and the larger MSE in the RCBD moves the threshold of rejection further from the mean than in the CRD. This change in the rejection threshold affects the Type II error (β)

    and the power of the test (1- β). Under this scenario, the probability of accepting a false null hypothesis (β) will be smaller in the CRD than in the RCBD. In other words, the CRD would in this situation be more powerful.

Situation 2: Significant difference among blocks

    Now suppose that there really are substantial differences among blocks as well as among treatments (H is false). In a CRD, this variation due to differences among blocks would remain 0

    in the error (i.e. would not be partitioned from the error). This larger MSE would make the F statistic (MST/MSE) for the CRD smaller (less significant) than the F statistic for the RCBD.

Under this scenario, the RCBD would still have a larger critical (i.e. tabular) F value because of

    the lost degrees of freedom; but this may be more than compensated by the smaller MSE. If the


    effect of the reduced MSE (increased F statistic) outweighs the effect of the larger critical value (rejection threshold further from 0), the net result will be a smaller β and thus a larger power in

    the RCBD relative to the CRD.

    Obviously, one should only use the RCBD when the variation explained by the blocks more than offsets the penalty associated with having fewer error degrees of freedom. So how can one determine when an RCBD is appropriate? This question is answered using the concept of efficiency, introduced in Section and elaborated upon in section 6.3.

6.2.5. Example (from Little and Hills)

    This experiment was conducted to investigate the effect of estrogen on weight gain in sheep.

    The four treatments in the experiment are a factorial combinations of two separate factors: Gender of sheep (male and female) and amount of estrogen (S0 and S3). Although this experiment could be analyzed as a factorial, in this example we are treating the four treatments and four levels of a single factor (gender-estrogen combination).

    Sheep from four different ranches were involved in the experiment. Anticipating that differences in herd management may affect the results, the researchers blocked by ranch. The completeness of an RCBD demanded, therefore, that each ranch volunteer four sheep to the experiment, two males and two females, providing one replication of each treatment level from each ranch.


Table 6.1 RCBD. Effect of estrogen on weight gain in sheep (lbs).

     Ranch (i.e. block) Treatment

    Treatment I II III IV Total Mean

    47 52 62 51 212 53 F-S0

    50 54 67 57 228 57 M-S0

    57 53 69 57 236 59 F-S3

    54 65 74 59 252 63 M-S3

    208 224 272 224 928 Block Total

    52 56 68 56 58 Block Mean

Table 6.2 RCBD ANOVA

    Source df SS MS F

    15 854 Total

    3 576 192.00 24.69** Blocks

    3 208 69.33 8.91** Treatment

    9 70 7.78 Error

Table 6.3 CRD ANOVA

    Source df SS MS F

    15 854 Totals

    3 208 69.33 1.29 NS Treatment

    12 646 53.83 Error

    Since each treatment is present at the same level of replication within each block, differences among blocks are not the result of treatment effects. Differences among blocks are entirely independent of treatment effects and are due only to differences associated with the four ranches. Therefore, this component (SSB) can be perfectly partitioned from the total SS. Ultimately, this reduces the experimental error. To see this, compare the two tables above (Tables 6.2 and 6.3), paying close attention to the degrees of freedom and the SS in each analysis. SAS Program

The linear model of an RCBD contains two classification variables, treatment and block. For this

    experiment, we will call the treatment factor "Sex_Est" because its levels are comprised of various combinations of gender and estrogen supplements. The block variable is "Ranch." The response variable is "Gain." SAS does not know the scientific interpretation of the effects in the model, so it will simply compute F statistics for both Sex_Est and Ranch, as shown in Table 6.2 above. See the code below:


Data Lambs;

    Input Sex_Est $ @@;

     Do Ranch = 1 to 4;

     Input Gain @@;




    F0 47 52 62 51

    M0 50 54 67 57

    F3 57 53 69 57

    M3 54 65 74 59


    Proc GLM;

     Class Sex_Est Ranch;

     Model Gain = Ranch Sex_Est;



6.3. Relative efficiency [ST&D 221, and Topic 1 section]

    We saw earlier that if the variation among blocks is large then we can expect the RCBD to be more sensitive to treatment effects than the CRD; conversely, if this variation is small, the CRD may be more sensitive (i.e. more powerful). The concept of relative efficiency formalizes the

    comparison between two experimental methods by quantifying this balance between loss of degrees of freedom and reduction in experimental error.

    Recall that the F statistic = MST/MSE. The experimental design primarily affects the MSE since the degrees of freedom for treatments is always (t 1) and the variation due to treatments is

    independent of (i.e. orthogonal to) the variation due to blocks and the experimental error. The information per replication in a given design is:

    1 I2

Therefore, the relative efficiency of one design another is


    22I121 RE1:221I2122

    2In reality, we never know the true experimental error (); we only have an estimate of it (MSE).

    To pay for this lack of knowledge, a correction factor is introduced into the expressions for information (I) and relative efficiency (RE) (Cochram and Cox, 1957). The following formulas include this correction factor and give an estimate of the relative amount of information provided by two designs:


    ?)df111MSE?? I?2??dfMSE3MSE(

    ?)df11MSE1????df3MSE(df1)(df3)MSEIMSE11(MSE1MSE221 RE?1:2I(df1)(df3)MSE?)df112MSE2MSE11MSE2????df3MSEMSE22(

where MSE is the mean square error from experimental design i. If this ratio is greater than 1, it i

    means that Design 1 provides more information per replication and is therefore more efficient than Design 2. If RE = 2, for example, each replication in Design 1 provides twice as much 1:2

    information as each replication in Design 2. Design 1 is twice as efficient.

    The main problem with the approach is how to estimate MSE for the alternative design. Suppose an experiment is conducted as an RCBD. The MSE for this design is simply given by the analysis (MSE). But now we wish to ask the question: What would have been the value of the MSE RCBD

    if the experiment had been conducted as a CRD? In fact, it was not conducted as a CRD. The treatments were not randomized according to a CRD. Because of this, one cannot just re-analyze the data as though it were a CRD and use the MSE from the analysis as a valid estimate of MSE. CRD

MSE can be estimated, however, by the following formula (ST&D 222): CRD


    where MSB and MSE are the block and error mean squares in the original design (RCBD), and df, df, and df are the block, treatment, and error degrees of freedom in the original design. To BTe

    obtain this formula, the total SS of the two designs are assumed equal. This equation is then expanded such that the SS are rewritten in terms of the underlying variance components of the expected MS. Simplification of the terms generates the above estimate (for a complete derivation, see Sokal & Rohlf 1995, Biometry 838-839).

From the sheep experiment, MSE = 7.78 and MSB = 192.0. Therefore: RCBDRCBD

    dfMSBdfdfMSE()3(192)(39)7.78BRCBDTeRCBDˆMSE 44.62CRDdfdfdf339BTe


    ˆdfdfMSE(1)(3)(91)(123)44.62MSE1MSE2CRD RE5.51RCBD:CRDdfdfMSE(1)(3)(121)(93)7.78MSE2MSE1RCBD


    Interpretation: It takes 5.51 replications in the CRD to produce the same amount of information as one replication in the RCBD. Or, the RCBD is 5.51 time more efficient than the CRD in this case. It was a very good idea to block by ranch.

6.4. Assumptions of the model

    Again, the model for the RCBD with a single replication per block-treatment combination is:

    Y = + + ( + ijijij

As in the CRD, it is assumed that the residuals ( are independent, homogeneous, and normally )ij

    distributed. Also as in the CRD, it is assumed that the variance within each treatment levels is homogeneous across all treatment levels. But now, in an RCBD without replication (i.e. with a single replication per block-treatment combination), there is a third assumption of the model: Additivity of main effects.

Recall that experimental error is defined as the variation among experimental units that are

    treated alike. With that in mind, consider the following schematic of our sheep experiment:


    Trtmt 1 2 3 4

    M Est 0

    M Est 3

    F Est 0

    F Est 3

    In this experiment, while there are four reps of each level of treatment and four reps of each block, there is no true replication vis-à-vis calculation of experimental error. For example, there is only one male sheep at Ranch 1 that received no estrogen. Normally, our estimate of the experimental error would come from looking at the variation among two or more sheep treated alike (e.g. two or more sheep of the same gender, at the same ranch, receiving the same estrogen treatment). So if we have no ability to calculate the experimental error, what is the in our linear model? ij

There is an expected value for each of the 16 cells in the above diagram, given by:

    Expected Y = + + ( ijij

    In this design, we use the deviation of the observed values from their expected value as estimates of the experimental error. Technically, though, these deviations are the combined effects of


    experimental error and any nonzero block*treatment interaction for that cell. With only one replication per cell, we are unable to separate these two effects. So when we use these deviations (observed expected) as an estimate of the experimental error, we are assuming that there are no

    significant block*treatment interactions (i.e. no significant non-additive effects).

Said another way, in this model:

    Y = + + ( + ijijij

    The residuals are the results of experimental error and any non-additive treatment*block interactions:

     = *( + error ijijij

Thus, when we use as estimates of the true experimental error, we are assuming that *( = 0. ijij

    This assumption of no interaction in a two-way ANOVA is referred to as the assumption of additivity of the main effects. If this assumption is violated, all F-tests will be very inefficient and possibly misleading, particularly if the interaction effect is very large.

    Example: A significant interaction term will result if the effect of the two factors A and B on the response variable Y is multiplicative rather than additive. This is one form of non-additivity.

     Factor A

     Factor B = +1 = +2 = +3 123

    2 3 4 Additive effects

    1 2 3 Multiplicative effects (= +1 1

    0 0.30 0.48 Log of multiplicative effects

    6 7 8 Additive effects

    5 10 15 Multiplicative effects (= +5 2

    0.70 1.00 1.18 Log of multiplicative effects

    In the above table, additive and multiplicative treatment effects are shown in a hypothetical two-way ANOVA. Let us assume that the population mean is = 0. Then the mean of the e.u.'s

    subjected to level 1 of factor A and level one of factor B should be 2 by the conventional additive model. Similarly, the expected subgroup mean subjected to level 3 of factor A and level 2 of factor B is 8, since the respective contributions to the mean are 3 and 5. If the process is multiplicative rather than additive, however, as occurs in a variety of physicochemical and biological phenomena, the expected values are quite different. For treatment AB, the expected 32

    value is 15, the product of 3 and 5.

    If multiplicative data of this sort are analyzed by a conventional ANOVA, the interaction SS will be large due to the nonadditivity of the treatment effects. If this SS is embedded in the SSE, as in the case of an RCBD with one e.u. per block-treatment combination, the estimate of the experimental error will be artificially large, thereby making all F tests artificially insensitive.


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