Limitations of Balanced Half Sampling

Richard Valliant

U.S. Bureau of Labor Statistics

Room 4925, 2 Massachusetts Avenue NE

Washington DC 20212

June 1995
Abstract

Balanced half-sample (BHS) variance estimation is a popular technique among

survey statisticians, but it has limitations. These limits are studied theoretically through a

model-based approach and illustrated with simulations using artificial and real populations.

In the fully balanced case, under a model often used for stratified, clustered populations,

BHS produces a model-unbiased variance estimator for only one member of a broad class

of estimators of totals. Another implementation of BHS variance estimation in large,

complex surveys is to use partial balancing or grouping of strata to reduce the number of

resample estimates that must be calculated. Instead of selecting a fully balanced,

orthogonal set of half-samples, strata are combined into groups and a set of half-samples

only large enough to be balanced on the groups is selected. For two-stage cluster samples

either with or without poststratification this leads to an inconsistent variance estimator.

Key words: balanced repeated replication, inconsistent variance estimator, model-based

sampling, partial balancing, poststratification, two-stage cluster sampling.

Acknowledgment

Any opinions expressed are those of the author and do not constitute policy of the

Bureau of Labor Statistics. I am grateful to Alan Dorfman and the referees for their

careful reviews.
2

1. Introduction

Balanced half-sample (BHS) variance estimators, and resampling estimators

generally, are widely used in sample surveys because of their simplicity and flexibility.

Properly applied, they can accommodate complex survey designs and complicated

estimators without explicit derivations of variance formulae for different types of

estimators. Thoughtless application can, however, lead to problems. This paper discusses

some of the difficulties associated with BHS generally and with the shortcut method

known as partial balancing. We consider stratified clustered populations from which twostage samples are selected. Following the introduction of notation in section 2, section 3

presents a general class of estimators in which the BHS variance estimator can be designunbiased but model-biased. A subclass of estimators is noted where BHS is modelunbiased. Section 4 discusses a situation, common in practice, where the partially

balanced or grouped BHS variance estimator is inconsistent. The inconsistency result is

extended to poststratification in section 5. Simulation results using real and artificial

populations are given in section 6 in support of the theory.

2. Notation and Model

The population of units is divided into H strata with stratum h containing Nh

clusters. Cluster (hi) contains Mhi units with the total number of units in stratum h being

M M h hi i

Nh

= Â =1 and the total in the population being M Mh h

H

= Â =1. Associated with each

unit in the population is a random variable yhij whose finite population total is

T yhij j

M

i

N

h

h hi

= Â Â=1Â =1 . The working model is
3

E y M hij h d i = μ

cov ,

, ,

M hij h i j , ,

hi

hi hi y y

h hi i j j

h hi i j j ′′′ =

= ′ =′ = ′

= ′ =′ ≠ ′

R

S

|

T

| d i

σ

σ ρ

2

2

0 otherwise

(1)

A two-stage sample is selected from each stratum consisting of nh = 2 sample clusters and

a subsample of mhi sample units is selected within sample cluster (hi). The total number of

clusters in the sample is n nh h = ∑ . The set of sample clusters from stratum h is denoted

by sh and the subsample of units within sample cluster (hi) by shi. Model (1) is reasonably

general in allowing the variance and the covariance among units to be different for every

cluster in the population while specifying a common mean within each stratum.

The general estimator of the total T that we will consider in this section has the

form:

$

T Ky hi hi

h i sh

= ∈

∑∑ , (2)

where Khi is a coefficient that does not depend on the y's and y ym hi hij hi j shi= ∑ ∈ . In

order for $T to be model-unbiased under (1), we must have K M hi i s h ∑∈ h= . Each Khi may

also depend on the particular sample selected. A number of examples of estimators that

fall in the class defined by (2) were listed in Royall (1986) and Valliant (1987a, 1993).

For the ratio estimator, ( ) $T My M M R hi hi h hi h i s i s h h= ∑ ∑∈ ∑∈ , for example,

K MM M hi hi h hi i sh= ∑∈ e j, a sample dependent quantity. When clusters are selected with
4

probability proportional to Mhi and units within clusters are selected with equal

probability, the Horvitz-Thompson estimator is unbiased under (1) and has K Mn hi h h = .

The theory here will cover the situation where H is large. Lemma 1 below gives

circumstances in which the prediction variance var $ M eT T − j is asymptotically equivalent to

var $ M eT j. Although we will concentrate on the case of nh = 2, the lemma holds for other

bounded sample sizes also. First, define varM hi hi hi hi hi bg b g y mm = +− σ ρ 2 1 1 ≡ vhi. The

results in Appendix A.1 of Valliant (1993) can be easily modified to obtain

Lemma 1. If, as H → ∞,

(i) n M → 0,

(ii) max

h i,

Mhi b g, max

h i,

mhi b g, and max

h

Nh b g are Oa f1

(iii) max

h i,

K OMn hi bg b g =

(iv) nM G hh h h

2 c h∑ K VK ′ → , a positive constant,

then

var $ var $ M M

hh h h hi hi h i s

TT T

K v

h

− ≈

= ∑ ∑ ′ = ∑ ∈

e j e j

K VK 2 (3)

where Kh h hn K K h = ′ 1 d , , K i and Vh hi = diag vb g for i n = 1, , K h .

When the number of strata H is large and the other assumptions in Lemma 1 hold,

the dominant term of the prediction variance is var $ M eT j just as in the unstratified case

studied by Royall (1986) where the sample size of clusters was large and the sampling

fraction of clusters was small. The total number of sample clusters actually is large here

also, even when nh = 2 in all strata, because H → ∞. Condition (i), n M → 0, is
5

equivalent to n NM c h → 0 where M MN = is the mean number of units per cluster in

the population. Since the maximum cluster sizes are bounded in (ii), the mean M is

bounded and (i) implies that the overall sampling fraction of clusters n N is negligible.

In evaluating the performance of the BHS variance estimators, our estimation

target will be the model variance var ( ) $ M T T − or its large-sample equivalent var ( )$ M T . In

the presence of a probability sampling plan, another model-related candidate might be

EE T T p M ( ) $ − 2

 with Ep denoting design-expectation, but, after a sample is selected,

various conditionality arguments impel the use of a model, not a random selection plan or

a design/model hybrid, for inference (see, e.g. Royall 1988).

3. A Balanced Half Sample Variance Estimator and Limits of its Applicability

Balanced half-sample (BHS) variance estimators, proposed by McCarthy (1969),

are often used in complex surveys because of their generality and the ease with which they

can be programmed. Assume that the population is stratified, as in section 1, and that a

sample of nh = 2 primary units is selected from each stratum. There are generalizations of

the method to other sample sizes in Gurney and Jewett (1975), Sitter (1993), and Wu

(1991), but the nh = 2 case is so common in practice that it deserves special attention. A

set of J half-samples is defined by the indicators

ς

α

hiα

hi = R

S

T

1

0

if cluster is in half - sample 

if not

for i=1,2 and α=1,..., J. Based on the ς hiα , define
6

ς ς

α

α

α

h h α

h

h

b g = −

= R

S

T

2 1

1 1

2

1

if cluster is in half - sample 

-1 if cluster is in half - sample 

Note also that −= − ς ς α

h h α

a f 2 1 2 . A set of half-samples is said to be in full orthogonal

balance if

ς α

α h

J b g ∑ = = 1 0, for all h and (4)

ς ς α α

α h h

J bg bg′ ∑ = = 1

0 a f h h ≠ ′ (5)

with a minimal set of half-samples satisfying (4) and (5) having H JH +≤ ≤ + 1 4.

Let $Tb g α be the estimator, based on half-sample α, with the same form as the full

sample estimator $T . One of several choices of BHS variance estimators is

vT T T J B

J

$ $$ di d i a f = − =

∑ α

α

2

1

.

There are other asymptotically equivalent BHS estimators, whose large sample properties

are the same as those of vB (Krewski and Rao 1981).

The BHS variance estimator is approximately model unbiased under (1) if

Ev T MB M ( ) = var ( )$ defined by (3). As shown in section 3.1, vB meets this standard for

only one estimator $T in class (2).

3.1 Model-based Properties

Next, we can evaluate the BHS variance estimator and its expectation for the twostage case. To implement the method, entire clusters are assigned to half-samples, i.e., if a
7

particular cluster is in half-sample α, then all units subsampled from that cluster are

assigned to α also. The half-sample estimator of the total is defined as

$

T Ky Ky h hh h hh h

α

α

α

α

α ς ς bg bg bg = + ∑ e j 1 11 2 22

The form of the half-sample term Khi

b g α is dictated by the form of $

T and is computed as the

full sample coefficient would be if the sample size were nh = 1. The α superscript is

attached to Khi

b g α since the value will differ from the full sample value. Although we use a

superscript α on Khi

b g α , its value is the same for each half-sample containing unit hi. The

difference between the half-sample and full-sample estimators is

$ $ T T K Ky hi hi hi hi h i sh

α

α

α ς bg bg −= − e j ∑ ∑∈ .

Using the definitions of ς hiα and ς α

h

b g, we have ς ς α

α

h h 1 = +1 2 a f and ς ς α

α

h h 2 = −1 2 a f .

The difference $ $ T T b g α − can then be written as

$$ $ $ TT T T h h h yh h

α α αα ς bg bg bg bg −= − + {e j } ∗

∑ 1

2 ∆ (6)

where $

T Ky Ky h hh hh

bg bg bg α αα e j ∗ = + 1

2 11 22 , $

T Ky h hi hi i sh= ∑∈ , and ∆yh h h h h Ky Ky bg bg bg αα α = − 11 22 .

If $ $ T T b g α − is squared out and summed over half-samples, we obtain a tidy

reduction, found in McCarthy (1969) and elsewhere, if the Khi 's and Khi

b g α 's have a special

form, but not in general. In particular, suppose that

(HS-1) K K hi hi

b g α = 2

holds. This condition corresponds to the standard prescription “double the weights in

each half-sample.” Not all estimators satisfy HS-1; section 3.3 gives examples where that
8

condition is violated. When HS-1 does hold, $ $ T T h h

b g α ∗ = , ∆ ∆ yh yhb g α = 2 where

∆yh h h h h = − Ky Ky 11 2 2 , and

$ $ T T h yh h

α α ς bg bg − = ∑ ∆ . (7)

Squaring out (7) and summing over an orthogonal set of half-samples gives the BHS

estimator as

vB yh h = ∑ ∆2 .

The expectation under model (1) is then easily calculated as

E v Kv K K M B hi hi h i s hh h h h

bg b g = +− ∑ ∑ ∑∈

2 2

1 2

2

μ , (8)

which is the asymptotic variance in (3) plus a positive term. The positive term looks like a

bias squared but is present even when $T is model unbiased. Expression (8) is similar to

the result for the separate ratio estimator in single-stage sampling obtained in Valliant

(1987b). If the class of estimators is further restricted so that, in addition to HS-1,

(HS-2) K K hi h = for all i s ∈ h

holds, then ∆yh h h h = − Ky y 1 2 b g and E M yh d∆ i = 0. With both HS-1 and HS-2 holding, vB

is approximately model unbiased.

Conditions HS-1 and HS-2 substantially limit the class of estimators for which BHS

is appropriate as an estimator of the model variance (3). Because K M hi s h ∑ h

= for

model unbiasedness, HS-2 implies that K Mn h hh = = Mh 2 . In other words, the class of

model-unbiased estimators for which BHS is appropriate consists of the singleton

$

T M yn h hi h h sh= ∑ ∑ . Section 3.3 gives some examples of other estimators in class (2)

where BHS does not work because conditions HS-1 or HS-2 do not hold. In practice,
9

BHS is often applied in situations where more elaborate models for E y M hij ( ) than (1) are

appropriate. The preceding remarks do not preclude the possibility that BHS can

successfully be applied to those situations  an area of investigation that will not be

pursued further here.

3.2 Design-based Properties

With some sample designs vB may have desirable design based properties when

only HS-1 holds, despite the conditional (model) bias in (8). Define πhi to be the selection

probability of unit hi in a sample of nh = 2 . If K M hi hi hi = π , (HS-1) is satisfied when

( ) Khi

α is calculated by substituting π π ′

hi hi = 2 for πhi . In that case,

v My T n n B hi hi hi h h h ( ) [ ] ( ) h sh= −− ∑ π $ ,

2

1 and vB is design unbiased under withreplacement sampling when $

Th is design unbiased. When K M hi hi hi = π and the estimator

is a differentiable function of totals defined by (2), Krewski and Rao (1981) showed that

vB is design-consistent as H → ∞ and the sampling of clusters is done with replacement.

Condition HS-2 is not required for these results. When averaged over the design

distribution, the second, model-related term in (8) turns into a design variance component,

an example of a more general phenomenon pointed out by Smith (1994).

3.3 Examples

Some examples will show the limitations of BHS as an estimator of the largesample model variance var ( )$ M T . Examples 1-4 each concern estimators of $

T that satisfy
10

the condition K M hi i s h ∑∈ h= for unbiasedness under (1). In each case below, the halfsample coefficients all reduce to K M hi h

a f α = . Thus, the half-sample method tries to

estimate the variance of the BLU predictor, the expansion estimator, the ratio estimator,

and the Horvitz-Thompson estimator all with the same set of half-sample $( ) T α ’s  a

tactic that is obviously incorrect.

Example 1. BLU estimator: From Royall (1976) the best linear unbiased (BLU)

predictor under (1) is $ , T my BLU hi hi h sh= + ∑ ( ) M m wy w hi hi hi hi hi h [ ] ( ) h sh ∑ − +− + 1 $ , μ

Mhi h h rh

$ , ∑ μ where rh is the set of nonsample clusters, wm m hi hi hi hi hi hi = −+ ρρρ ( ) 1 ,

μ$ h hi hi s u y

h

= ∑ , and um m m m hi hi hi hi hi hi hi hi hi hi hi sh= −+ −+ σρ ρ σρ ρ ∑ 2 2 bg bg 1 1 .

Setting m m h hi i sh= ∑∈ , the coefficient in (2) is

K m M m M mwu wM m hi hi h h hi hi hi i s hi hi hi hi h

= + −− − + − ′ ′′ ∑′∈ b g bg which depends on the

particular units in the sample. The half-sample coefficient is simply K M hi h

b g α = and,

consequently, the prescription to double the full-sample weights to create half-sample

weights does not apply. Therefore, both HS-1 and HS-2 are violated.

Example 2. Expansion estimator: $T M m my h h hi hi h i sh 0 = ∑ ∑∈ b g . For the full

sample K M mm hi h h hi = b g . When the hi th b g sample cluster is assigned to half-sample α,

the number of sample units in the half-sample, mh

b g α , is equal to the number in the hi th b g

cluster, mhi . Thus, K M hi h

b g α = and neither HS-1 nor HS-2 holds. If m m hi h = , an allocation

that equalizes workload per cluster, then both HS-1 and HS-2 are satisfied.
11

Example 3. Ratio estimator: $

T M M My R h hi i s hi hi h i s h h= ∑ ∑ ∑ ∈ ∈ e j . Khi =

M MM h hi i s hi ∑∈ h

e j and K M hi h

b g α = . Again, HS-1 and HS-2 are, in general, violated.

Example 4. Horvitz-Thompson estimator when clusters are sampled with

probabilities proportional to Mhi and an equal probability subsample is selected within

each sample cluster: $

T Mn y HT h h hi h i sh

= ∑ ∑∈ b g . K Mn M hi h h h = = 2 and K M hi h

b g α = , so

that both HS-1 and HS-2 hold. In the special case of ρ ρ hi h = , σ σ hi h

2 2 = , M M hi h = , and

m m hi h = , the BLU predictor in example 1 also reduces to $THT .

It should be noted that standard survey design practices may minimize the effects

of violating HS-1 and HS-2. If clusters are stratified based on size and the sizes Mhi and

allocations mhi are about the same within a stratum, then each of the estimators in

examples 1-4 will be approximately equal to $THT , the case for which BHS works.

4. Partial Balancing

Partial balancing is often used in order to reduce the number of half-sample

estimates that must be computed for vB . Though computationally expedient, partial

balancing leads to an inconsistent variance estimator, as will be demonstrated in this

section. Suppose again that nh = 2 and that strata are assigned to groups or superstrata.

An attempt may be made to assign the same number of strata to each group, but this is not

essential. In a particular group all the sample clusters numbered 1 are associated and

assigned as a block to a half-sample. Sample clusters numbered 2 are similarly treated as a
12

block. Figure 1 illustrates the grouping of strata and treatment of clusters as blocks. If

there are g = 1, , K G groups of strata, then the estimator of the total can be written as

( ) T TT $ $$ g g

g

= + =

∑ 1 2

1

G

where $T Ky gi hi hi h Gg= ∑ ∈ , i=1,2 with Gg being the set of strata in group g. The estimator

of the total based on half-sample α is

() () () ( ) $ $$ T TT gg g g

g

α

α

α

α

α = + ς ς =

∑ 11 2 2

1

G

where ς giα =1 if the units numbered i in group g are in the half-sample and 0 if not, and

$

T Ky gi hi hi h Gg

bg bg α α = ∑ ∈ with Khib g α computed as it would be for the fully balanced case.

The difference between the grouped half-sample estimator and the full sample

estimator is

() () () ( ) $$ $$ $$ TT TT TT gg g g g g

g

α

α

α

α

α −= − + − ς ς =

∑ 11 1 2 2 2

1

G

. (9)

If K K hi hi

a f α = 2 , i.e. HS-1 holds, then $ $ T T gi gi

b g α = 2 and

() ()( ) $ $ $$ T T TT gg g

g

α α −= − ∑ς 1 2

where ςς ς α

gg g α α

b g = − =− − 2 12 1 d i 1 2 . With balancing on groups, the grouped BHS

estimator is

v TT GB g g ( ) g = − ∑ $ $ 1 2

2

.

The expectation of vGB is easily calculated as
13

E v Kv Kv K K M GB h h h h ( ) ( ) () g h G

hh h

g g g h G

= ++ −













 ∈ ∈  ∑ ∑ ∑ 1 ∑ 2

1 2

2

2 12

2

μ , (10)

which compares to (8) for the ungrouped case. When HS-2 holds, the second term in (10)

is zero and the grouped BHS estimator is asymptotically model unbiased. Note that vGB is

design unbiased if only HS-1 holds (Wolter 1985, sec. 3.6).

Even if HS-1 and HS-2 are satisfied, vGB may perform erratically when the number

of groups G is not large. Krewski (1978), in a related case, noted the large variance of a

grouped BHS estimator compared to the standard variance estimator in stratified simple

random sampling when the stratified expansion estimator is used. Lee (1972, 1973) has

studied modifications to partial balancing intended to help stabilize the variance of vGB, but

those procedures have somewhat limited applicability and have not become part of

standard practice. Rao and Shao (1993) have also proposed a repeatedly grouped

balanced half-sample (RGBHS) procedure that might be adapted to the partially balanced

case. The RGBHS method applies to a case where a large number of units are selected

within a stratum and then assigned at random to two groups for variance estimation.

If, as H → ∞, G is fixed, then vGB can be inconsistent in addition to being unstable.

To demonstrate this, we extend an argument given by Rao and Shao (1993) and Shao

(1994) for stratified single-stage sampling. Let ηg denote the number of strata assigned to

group g and suppose that ming

ηg d i → ∞. Under standard conditions,

z TT D N g gg g

d = − → $ $  , 1 2 e j b g 0 1

where D T T Kv v g Mg g hh h h Gg= −= + ∑ ∈ var $ $

1 2

2

1 2 e j b g. Since var $ M g g eTT D − ≈j ∑ ,
14

v

T T

D

D

z GB

M

g

g g

g

var g $ − ≈

′ ∑ ′

∑ e j

2 .

If D D g g g ′ ∑ ′ converges to a constant ωg , it follows that

v

T T

GB

M

g g

var g $ − → ∑ e j ω χ2 , (11)

where χg

2 is a central chi-square random variable with 1 degree of freedom. In other

words, rather than converging to 1 as would be the case for a consistent variance

estimator, the ratio in (11) converges to a weighted sum of chi-square random variables.

Note that a result similar to (11) can be obtained if ηg → ∞ in only some of the groups.

The inconsistency of vGB can manifest itself by varM GB b g v being large and by the length of

confidence intervals being excessively variable, as verified in the simulation reported in

section 6. A modification of the above formulation that might lead to consistency for vGB

would be to somehow let G → ∞ as H → ∞ .

The occurrence in practice of this phenomenon may be more frequent than one

would at first expect. In household surveys, selection of certainty clusters, i.e., selection

with probability 1, is standard practice. The first-stage units in the certainties are usually

geographically smaller clusters that are explicitly stratified or implicitly placed in strata

through systematic sampling from an ordered list. Frequently, the first-stage sample units

from a certainty are divided into two groups and vGB used for variance estimation. This

procedure can lead to the inconsistency described above.

5. Poststratification
15

Poststratification is used in household and other types of surveys to improve the

efficiency of estimators and to adjust for undercoverage of the target population due to

deficient frames and other reasons. Suppose that the population is divided into design

strata, indexed by h, and clusters within strata as in section 2. Each unit is also a member

of a class, or poststratum, denoted by c (c C =1, , K ). Each poststratum can cut across

design strata, and the set of units in poststratum c is denoted by Sc . The total number of

units in poststratum c is Mc hijc j

M

i

N

h

h hi

= ∑ ∑ = ∑ = δ

1 1 , where δhijc = 1 if unit hij is in Sc and 0 if

not. Assume that the poststratum sizes Mc are known. Consider the following working

model

E y M hij c c h= μ hij S af∈ c

cov ,

,, ,

,, , ,

,, , , M hij hij

hic c

hic hic c c

hicc c c

y y

h h i i j j hij S

h h i i j j hij S h i j S

h h i i j j hij S h i j S ′′′

′ ′

=

= ′ =′ = ′ ∈

= ′ = ′ ≠ ′ ∈ ′′′ ∈

= ′ = ′ ≠ ′ ∈ ′′′ ∈

R

S

||

T

|

|

d i

af

af a f

af a f

σ

σ ρ

τ

2

2

0 otherwise

(12)

Let mhic be the number of sample units in sample cluster hi that are part of poststratum c

and y ym hic hij hijc hic j shi= ∑ ∈ δ be the sample mean of those units. The model for the means

yhic implied by (12) is

E y M hic c b g= μ

cov ,

, ,

M hic h i c , ,

hic

hicc y y

v h hi ic c

h hi ic c ′′ ′ ′ =

= ′ = ′ = ′

= ′ =′ ≠ ′

R

S

|

T

| b g τ

0 otherwise

(13)

where v mm hic hic hic hic hic = +− σ ρ 2 1 1 b g . The poststratified estimator is defined as
16

T RT $ $$

ps c c

c

= ∑

where R MM $ $

c cc = , $ , M K c hic hi sh

= ∑ ∈ , and $ , T Ky c hic hic hi sh= ∑ ∈ with K Km m hic hi hic hi = .

A simple calculation shows that T

$

ps is unbiased under (12). Under the conditions in

Valliant (1993, Appendix A.1), var $ var $

M ps M ps eTT T − ≈j e j, similar to the nonpoststratified case in section 2.

Suppose that strata are grouped as in section 4 and that the BHS technique is used

on the groups. The estimator $Tc can be written as $ $$ T TT c cg cg g = + ∑ e1 2j with

$T Ky cgi hic hic h Gg

= ∑ ∈ (i=1,2). Similarly, M MM $ $$ c cg cg g = + ∑ e 1 2j with M K $

gci hic h Gg

= ∑ ∈ .

Let () () K Kmm hic hi hic hi

α α = and let R MM $ $

c cc

bα α g bg = be a half-sample poststratification ratio

with $ $$ , M MM c g cg g cg gh Gg

α

α

α

α

α ς ς bg= + ∑ ∈ e112 2bg bgj and define $Tc

bαg= () () ( ) ς ς α

α

α

α

g cg g cg gh G

T T g 11 2 2

$ $ , + ∑ ∈ .

$( ) Mcgi

α and $( ) Tcgiα have the obvious definitions based on ( ) Khic

α . The half-sample poststratified

estimator is T RT $ $$

ps c c c

bα αα g bg bg = ∑ .

When the number of strata H is large, R T $ $

c c

aα α f af can be expanded around the full

sample estimates R$

c and T

$

c to obtain the approximation

R T RT R T T R M M $ $ $$ $ $ $ $ $ $ c c cc c c c c c c c

αα α α μ af af af af

− ≅ −− − (14)

with μ

$ $ $ c cc = T M . With grouping of strata T T $ $

c c

bαg− is

$$ $$ $$ TT TT TT c c g cg cg g cg cg

g

α

α

α

α

α ς ς bg bg bg −= − + − e j =

∑ 11 1 2 2 2

1

G

. (15)
17

analogous to (9) and a similar expression holds for M M $c c

bαg− . If HS-1 holds, (15)

reduces to T T TT $ $ $$

c c g cg cg g

α α ς af−= − ∑ afd1 2i. We also have MM MM $$ $$

c c g cg cg g

α α ς af−= − ∑ afd 1 2i

and (14) becomes

$ $ $$ $ () () () R T RT R e c c c c c g gc

g

αα α − ≅ ς





 

 ∑  (16)

where e TT MM gc cg cg c cg cg =−− − $ $ $ $ $ d12 1 2 iμ d i. After summing (16) over c, squaring, and

using the orthogonality of the ς α

g

bg's, the grouped BHS estimator is approximately

v Re GB c gc g ( ) c ≅ ∑ ∑ $ 2

.

The expectation of egc under model (12) is 0. Thus,

E v M GB M g

g

b g≅ ∑R eR $′ var ch$

where $ $, , R $ = ′ eR R 1 K C j and eg g gC = e e ′ 1 c, , K h. By direct calculation this expectation can

be shown to be

E v M GB g ( ) g≅ ∑ ′ R R $ $ S (17)

where Sg is the C C× matrix with bccgth element K v hic hic h G i s g h

2

∑ ∈ ∑∈ and bcc′gth element

K Khic hic hicc h G i s g h ∑ ∈ ∑∈ ′ ′ τ . Expression (17) is equal to var $

M ps eT j in expression (8) of

Valliant (1993) and, consequently, the grouped BHS estimator is approximately unbiased.

Note that HS-2 was not required because the mean μc in model (12) does not depend on

the stratum h.
18

Unbiasedness notwithstanding, vGB is inconsistent here also. As in section 4,

suppose that G is fixed as H → ∞. Again, let ηg denote the number of strata assigned to

group g and suppose that ming

dηgi→ ∞. Since $

R e′ g is a linear combination of random

variables and each egc is a sum over a large number ηg of strata, we have, under

appropriate conditions,

z DN & $ & , g gg

d = ′ → R e  a0 1f

where D& $ var $

g Mg = R eR ′ ch. If D D & & & g g g ′ g ∑ ′ → ω , then

v

T T

GB

M

g g

var g $

&

− → ∑ e j ω χ2 , (18)

where χg

2 is a central chi-square random variable with 1 degree of freedom. Thus, the

grouped variance estimator is also inconsistent here.

6. Simulation Results

To illustrate the problems with the grouped BHS variance estimator, we conducted

two simulation studies. In the first, single-stage cluster sampling was used in artificial

populations. In the second study, two-stage cluster samples were selected from a

population derived from the U.S. Current Population Survey (CPS) and a poststratified

estimator used.

For the first study, two artificial populations having H = 40 and H = 160 were

generated as follows. Constant numbers of clusters per stratum and units per cluster were

assigned as Nh = 100 and M M hi h ≡ = 10 . A y variable for each unit in each stratum was

generated as yhij h hi hij =++ με ε2 where both εhi and ε hij were computed as ax − 6 12 f
19

with x a chi-square random variable with 6 degrees of freedom. The stratum means μh

were multiples of 10, assigned in blocks of 20 — μh = 10 for the first 20 strata, μh = 20

for the next 20 strata, μh = 30 for the next 20 strata (for H = 160), and so on. Assigning

the means in this way was convenient but has no particular effect on results other

choices would illustrate the same points. The population with H = 40 had a total of

M = 40 000 , units while the H = 160 population had 160,000 units. In each stratum a

sample of nh = 2 was selected by simple random sampling without replacement and both

sample clusters were completely enumerated. The estimator of the total used was

$T Myh hs h = ∑ with y yn hs hi h sh

= ∑ . $T is unbiased with respect to both the model and

the stratified simple random sampling design. When the sampling fraction of clusters is

small in each stratum, a model-unbiased and design-unbiased estimator of variance is

v My y B hh h h = − ∑ 2

1 2

2

b g4,

which also equals the BHS estimator when a set of half-samples in full orthogonal balance

is used. The sampling fraction of clusters in each stratum for both H = 40 and H = 160 is

2/100. Thus, the lack of a finite population correction (fpc) in vB has a minor effect.

For both artificial populations vGB was computed using G = 20 groups and a set

of 24 half-samples in full orthogonal balance. When H = 40, strata were paired to form

the groups. Strata 1 and 2 were paired, strata 3 and 4 were paired and so on. When

H = 160, strata 1-8 were grouped, strata 9-16, and so on. Note that this type of

purposive, as opposed to random, grouping reflects what is typically done in practice.

The second study used a population of 10,841 persons included in the September

1988 CPS. The y variable was weekly wages for each person. The study population
20

contained 2,826 geographic clusters, each composed of about 4 neighboring households.

Eight poststrata were formed based on age, race, and sex. Valliant (1993) gives more

details about the population and the poststrata definitions. A two-stage sample design was

used with clusters as first-stage units and persons as second-stage units. Two sets of

1,000 samples were selected with 100 sample clusters in the first set and 200 sample

clusters in the second. In both sets, clusters were selected with probabilities proportional

to the number of persons in each cluster. Strata were created in both cases to have about

the same total number of households, and nh = 2 sample clusters selected in each stratum

using the systematic method described in Hansen, Hurwitz, and Madow (1953, p.343). In

each sample cluster, a simple random sample of 4 persons was selected without

replacement in clusters with Mhi > 4; otherwise, the cluster was enumerated completely.

From each sample from the CPS population, the poststratified estimate $Tps , the

BHS variance estimator vB based on a set of half-samples in full orthogonal balance, and

the grouped BHS estimator were calculated. The poststratified estimate $Tps used

K Mn hi h h = so that HS-1 and HS-2 were satisfied. For both sample sizes (n=100 and

n=200), 25 groups of strata were formed in order to compute vGB. For both vB and vGB,

the half-sample totals $Tc

b g α incorporated the factor 1− n N h h , as described in Valliant

(1993), to approximately reflect the effect of a non-negligible fpc.

Table 1 summarizes results on square root mean square errors (rmse's) and

standard error estimates across 1,000 samples from each of the populations. The rmse in

each simulation was computed as rmse T T T S s s

S $ $ ej e j = − L

NM O ∑ = QP

2

1

1 2

 where S=1000 and $Ts is

an estimate of the population total T from sample s. The average of the square roots of
21

each variance estimate was calculated as v vS s s

12 12 = ∑ where vs is the grouped or

ungrouped BHS variance estimate from sample s. As the ratios, v rmse 1 2 , of average

root variance estimate to rmse show, neither the grouped BHS estimator nor the fully

balanced choices have any serious biases in either the artificial or CPS populations.

Table 2 gives coverage percentages over the 1,000 samples of 95% confidence

intervals computed using the different variance estimates. Again, no particular defects are

observed for the grouped BHS estimator. All choices cover at about the nominal level.

Calculations were also performed for 90% and 99% intervals with similar results.

Table 3 lists the averages of the half-widths of 95% confidence intervals, i.e. the

average over the samples of 1 96 . v for each variance estimator v. The table also shows

the variances of those half-widths. Although, for a given simulation, the average length is

about the same for both variance estimators, the variances of the half-widths are vastly

different. In the (artificial/H=40) case, the variance of the vGB half-widths is 1.9 times the

variance of the vB half-widths (3,040/1,591). In the (artificial/H=160) case, the ratio is

6.2. The ratios of variances for the (CPS/H=50) and (CPS/H=100) cases are 2.1 and 4.4.

The relative instability of vGB is further illustrated by Figure 2 which gives density

estimates for the two variance estimates from the CPS simulations. The density for the

grouped BHS estimate is much more heavy-tailed than that of vB at either sample size.

Figure 3 makes related points on confidence interval coverage and length. The standard

error estimate v (v = vB or vGB ) for each sample is plotted versus the estimation error

$

T T − for 500 of the samples for (Artificial/H=160) and (CPS/H=100). Reference lines

are drawn at v TT = −$ 196 . . Points that fall between the two lines correspond to
22

samples where the 95% confidence interval covered the true value. Points outside the

reference lines are samples where the confidence intervals did not cover. Circles denote

vB and dots vGB . In both panels vB has a more narrow range for almost all values of

$

T T − than does vGB . The width of confidence intervals based on vGB is erratic in the

region where intervals cover T. Near $

T T − = 0 in (CPS/H=100), for example, vGB

ranges from about 60 to 160 (in thousands), but the range of vB is about 75 to 120.

These results raise the interesting point that despite the sameness of coverage and

mean interval length, the fully balanced and partially balanced BHS estimators are not

equally good. The price paid for partial balancing is wildly fluctuating confidence interval

lengths.

7. Conclusion

Though balanced half-sampling can be a flexible and powerful tool in complex

sample surveys, the shortcut method of partial balancing should be avoided unless a large

number of groups can be formed. The grouped BHS variance estimator is at best unstable

compared to a fully balanced estimator and at worst inconsistent. Continuing surveys that

use partial balancing are likely to observe erratic point estimates of variance over time that

do not accurately reflect the precision of estimated means and totals.

REFERENCES

Gurney, M., and Jewett, R.S. (1975). Constructing orthogonal replications for variance

estimation. Journal of the American Statistical Association, 71, 819-821.
23

Hansen, M., Hurwitz, W., and Madow, W. (1953), Sample Survey Methods and Theory

(Vol.1), New York: John Wiley.

Krewski, D. (1978). On the stability of some replication variance estimators in the linear

case. Journal of Statistical Planning and Inference, 2, 45-51.

Krewski, D., and Rao, J.N.K. (1981). Inference from stratified samples: properties of the

linearization, jackknife, and balanced repeated replication methods. Annals of

Statistics, 9, 1010-1019.

Lee, K.-H. (1972). Partially balanced designs for half sample replication method of

variance estimation. Journal of the American Statistical Association, 67, 324-334.

Lee, K.-H. (1973). Using partially balanced designs for the half sample replication method

of variance estimation. Journal of the American Statistical Association, 68, 612-614.

McCarthy, P.J. (1969). Pseudo-replication: half-samples. Review of the International

Statistical Institute, 37, 239-264.

Rao, J.N.K., and Shao, J. (1993). On balanced half-sample variance estimation in stratified

sampling. Carleton University preprint.

Royall, R. (1976). The linear least squares prediction approach to two-stage sampling.

Journal of the American Statistical Association, 71, 657-664.

Royall, R. (1986). The prediction approach to robust variance estimation in two-stage

cluster sampling. Journal of the American Statistical Association, 81, 119-123.

Royall, R. (1988). The prediction approach to sampling theory. Handbook of Statistics,

Vol. 6, eds. P.R. Krishnaiah and C.R. Rao, 399-413, Amsterdam: Elsevier Science

Publishers.
24

Shao, J. (1994). Resampling methods in sample surveys. Statistics, to appear.

Sitter, R. (1993). Balanced repeated replications based on orthogonal arrays. Biometrika,

80, 211-221.

Smith, T.M.F. (1994). Sample surveys 1975-1990; an age of reconciliation?”

International Statistical Review, 62, 3-34.

Valliant, R. (1987a). Generalized variance functions in stratified two-stage sampling.

Journal of the American Statistical Association, 82, 499-508.

Valliant, R. (1987b). Some prediction properties of balanced half-sample variance

estimators in single-stage sampling. Journal of the Royal Statistical Society B, 49, 68-

81.

Valliant, R. (1993). Poststratification and conditional variance estimation. Journal of the

American Statistical Association, 88, 89-96.

Wolter, K. (1985), Introduction to Variance Estimation, Springer-Verlag: New York.

Wu, C.J.F. (1991). Balanced repeated replications based on mixed orthogonal arrays.

Biometrika, 78, 181-188.
25

Table 1. Empirical root mean square errors (mse) of estimators of totals and ratios of

average standard error estimates to the rmse in 1,000 samples.

Population rmse (000s) v rmse B

1 2 v rmse GB1 2

Artificial populations $T0

H = 40 5.2 1.002 .997

H = 160 9.9 1.051 1.040

CPS population $Tps

H = 50 133.0 1.055 1.049

H = 100 97.4 .977 1.022

Table 2. Empirical coverage percentages in 1,000 samples of 95% confidence intervals.

L, M, and U are percentages of samples with $ d i TT v − <−1 96 . ,

$ d i TT v − ≤1 96 . , and $ d i TT v − > 1 96 . , respectively.

Population vB vGB

Artificial populations L MU L MU

H = 40 3.2 94.2 2.6 3.9 93.5 2.6

H = 160 2.7 95.7 1.6 3.0 95.0 2.0

CPS population

H = 50 2.5 95.5 2.0 3.2 94.4 2.4

H = 100 4.2 93.2 2.6 3.6 94.1 2.3
26

Table 3. Empirical results for average half-width length and variance of half-width length

for 95% confidence intervals over 1,000 samples.

Population Average half-width

(000s)

Variance of half-width

(000s)

Ratio of half-width

variances

(v v GB B )

Artificial vB vGB vB vGB

H = 40 10.2 10.2 1,591 3,040 1.9

H = 160 20.3 20.1 1,618 10,076 6.2

CPS

H = 50 275.1 273.6 963,597 2,015,957 2.1

H = 100 186.5 195.1 234,752 1,035,377 4.4

Figure 1. An example of grouping strata and treating sample clusters as blocks when

partial balancing is used. Circled units are assigned as a block to a half-sample.

h Sample clusters

11 2

21 2

31 2

41 2

51 2

61 2

71 2

g=1

g=2
27

Figure Titles

Figure 1. An example of grouping strata and treating sample clusters as blocks when

partial balancing is used. Circled units are assigned as a block to a half-sample.

Figure 2. Nonparametric density estimates for vB and vGB in the CPS population

simulations.

Figure 3. Standard error estimates ( vB and vGB ) plotted versus estimation errors

( ) $

T T − for 500 samples from the artificial population (H=160) and the CPS

population (H=100). o = vB ; • = vGB .