Transcript Randomized Block Designs
Experimental Design Concepts: Blocking (Chapters 14 & 15)
• Experimental Design Basics. • Completely randomized design revisited.
• Accounting for more than one factor.
• Blocking (and randomized block designs) to remove confounding.
ExpDes-1
Why Experimental Design?
Experimental design
(for an experimental study): the manner in which the experimental units are arranged or grouped, and how the treatments are assigned to them.
To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
Sir R.A. Fisher (1938)
Fisher compared a dataset to a sample of gold ore. The
finest analysis
could only extract the proportion of gold contained in the ore.
But a good design could yield a sample with more gold!
[ data = ore ; information = gold.] ExpDes-2
Experimental Error
Experimental error is the
variation
in the responses among experimental units (e.u.’s) which are assigned the same treatment, and are observed under the
same
experimental conditions. It is measured by SSE (or MSE).
Ideally, we would like experimental error to be
zero
! This is impossible because of (at least) one or more of the following reasons: 1.
2.
3.
4.
There are inherent differences in the e.u.’s before they receive treatments.
There is variation in the devices that record the measurements.
There is variation in applying or setting the treatments.
There are extraneous factors other than the treatments which affect the response.
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Experimental Error Control
Control over the magnitude of experimental error can be achieved by: • • • • • Careful choice of e.u.’s.
Homogeneity of e.u.’s decreases MSE.
Taking care with experimental procedures and recording of measurements.
Smaller errors result in smaller MSE.
Blocking of e.u.’s.
Blocking decreases MSE.
Choice of experimental design used.
Designs with more factors generally lead to smaller MSE.
Using
explanatory variables
or
covariates
(variables that are thought to affect the response; related to it).
Models with more variables generally lead to smaller MSE.
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Review: Completely Randomized Design
(one-factor design)
• CRD in one-way AOV (
test
(pooled variances). • Experimental units are • Treatments are assigned to experimental units at
random
• Each treatment is replicated the same number of times
balanced design
• No accommodation made for (extraneous sources of variation).
• High probability that a large fraction of the experimental units set out at the beginning of the study may be .
appropriate time.
).
generalizes the two-sample t relatively homogeneous
• Experiment will use very
few replicates
.
lost or unavailable for measurement
.
disturbing variables
at the ExpDes-5
Completely Randomized Design
• Experimental Design - Completely randomized design (CRD) • Sampling Design - One-way classification design • • •
Assumptions:
Independent random samples (results of one sample do not effect other samples).
Samples from normal population(s).
Mean and variance for population i are respectively, m i and s 2 .
Model:
y ij
m
i
ij
E ( y ij ) m i
AOV model
overall mean random error ~ N(0, s 2 ) effect due to population i
H
0 :
H a
: 1
At
2
least
one
t of
0
the
differ from
0 Requirement for m to be the overall mean:
i t
1
i
0 ExpDes-6
Reference Group Model
Model:
y tj y ij
m
t
m
t
tj
,
i
i
ij
,
i
t
1 , 2 , ,
t
1 random error ~ N(0, s 2 ) reference group mean effect due to population i Mean for the last group (i=t) is m t .
Mean for the first group (i=1) is m t Thus, 1 + b 1 is the difference between the mean of the reference group (cell) and the target group mean. Any group can be the reference group.
H
0
H a
: 1 2 : At least one of
t
1 the 0 0 This is the model SAS , SPSS and most other packages use.
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CRD Practical Problems/Limitations
• • • In many situations, the researcher: Does not have sufficient
homogeneous
experimental material in one group (location, batch, etc.) to effectively use the CRD (i.e.
resource constraints
) The study objectives require examining treatments over a broad range of experimental units in order that results can be extended to more situations (i.e.
breadth of study objectives
).
The experimental material must be grouped for administrative or implementation purposes (i.e.
implementation constraints
).
If the researcher knows something about the characteristics of the experimental material, it is often possible to
group
experimental units into
sets of relatively homogenous material
(
blocks
), and then
compare treatment level means within these groups
.
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Example
A scientist was interested in the use of three chemicals and water on their effectiveness in extracting sulfur from Florida soils. The chemicals of interest are: • Calcium Chloride • Ammonium Acetate • Mono Calcium Phosphate • Water CaCl NH Ca(H H 4 2 2 O 2 OAc PO 4 ) 3 Five soils were chosen for this experiment: • Troup Jackson Co. Paleudults soil • Lakeland • Leon • Chipley • Norfolk Walton Co. Duval Co. Quartzipsamments soil Haplaquads soil Jackson Co. Quartzipsamments soil Alachua Co. Paleudults soil ExpDes-9
Blocking and Control of Extraneous Variation
The main interest in the experiment is the comparison of the four extraction methods.
• The variation imposed on the extraction procedure by the five different soil types represents a source of
extraneous variation
. • Unless
controlled
for in the experiment, this variation has the potential to “swamp” or overwhelm the differences among the extraction procedure.
• High probability of concluding there are no treatment effects when treatment effects are in fact present.
Fair comparisons only occur among extractions within a soil type.
We wish to use the combined experience across soil types to make a stronger statement about the extraction procedures.
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Graphical View
Note: The pattern of responses to treatments is consistent within a given soil type (a block); but responses vary across soil types.
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Randomized Block Design (RBD)
Any experimental design in which the randomization of treatments is restricted to groups of experimental units within a predefined block of units assumed to be internally homogeneous is called a
randomized block design
. Blocks of units are created to control known sources of variation in expected (mean) response among experimental units.
There are two
classifications
treatment “effects”.
or
factors
in an RBD: block “effects” and • • Rules for blocking: Carefully examine the situation at hand and identify those factors which are known to affect the proposed response.
Choose one or two of these factors as the basis for creating blocks.
Blocking factors are sometimes referred to as
disturbing factors
.
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Examples of Typical Blocking Factors
Disturbing Variable
Nutrient gradient Water moisture gradient Slope differences Soil composition Orientation to sun Flow of air Distribution of heat Age Local density Gender Age Socio-demographics
Experimental Unit
Field Plot Location in Greenhouse Tree Person ExpDes-13
Blocking Importance
How blocks are formed is critical to the effectiveness of the analysis
.
• With field plots, blocks are laid out so that they are perpendicular to the maximum direction of change in the • • disturbing factor to be controlled. Wide border (discard) areas are used to overcome interference between neighboring plots (i.e. to maintain independence of responses) within blocks and between blocks.
Time blocks may need discard times between “replications”.
This approach maximizes
within block homogeneity
while simultaneously maximizing
among block heterogeneity
.
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Blocking Example
T2 T1 T5 T3 T4 T1 T2 T3 T5 T4 T1 T5 T2 T4 T3 T3 T4 T1 T2 T5
Treatment effects confounded with moisture effect!
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Blocking Example
T1 T5 T3 T4 T3 T2 T5 T4 T3 T1 T2 T5 T4 T1 T5 T2 T2 T4 T1 T3 Block effect now removes moisture effect, fair comparisons among treatments.
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Advantages and Disadvantages
• • •
Advantages of a Blocked Design
Controls a
single
extraneous source of variation and removes its effect from the estimate of experimental error.
Allows more
flexibility in experimental layout
.
Allows more
flexibility in experimental implementation
and administration.
• •
Disadvantages of a Blocked Design
Generally unsuited when there is a large number of treatments because of possible
loss of within block homogeneity
.
Serious problem with the analysis if a
block factor by treatment interaction effect
actually exists and no replication within blocks has been included. (solution: use replication within blocks when possible).
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Complete or Incomplete Designs
Can all treatments be accommodated in each block?
Complete Block Design
: Every treatment occurs in each block.
Incomplete Block Design:
Not every treatment occurs in each block.
Complete Incomplete
A B C D A B C B D C D C A A B B D D C A A ExpDes-18
Balance in Designs
Balancing
refers to the specific assignment of treatments to experimental units such that
comparisons of treatment effects are done with equal precision
. This is usually accomplished by equally replicating each treatment.
Balanced Block Design:
The variance of the difference between two treatment means is the same regardless of which two treatments are compared. This usually implies that the overall replication (disregarding which blocks they are in) for the comparison of two treatments is the same for all pairs of treatments.
Partially Balanced Design
: The variance of the difference between two treatments depends on which two treatments are being considered. This usually implies different replication for different treatments.
Unbalanced Designs:
Unequal replication in each block - usually what one ends up with.
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