STRATIFIED TELEPHONE SURVEY DESIGNS

Robert J. Casady

Bureau of Labor Statistics

U. S. Department of Labor

and

James M. Lepkowski

Survey Research Center

University of Michigan

January 8, 1993
Abstract

Two stage random digit dialing procedures as developed by Mitofsky and

elaborated by Waksberg are widely used in telephone sampling of the U.S.

household population. Current alternative approaches have, relative to this

procedure, coverage and cost deficiencies. These deficiencies are addressed

through telephone sample designs which use listed number information to

improve the cost-efficiency of random digit dialing. The telephone number frame

is divided into a stratum in which listed number information is available at the

100-bank level and one for which no such information is available. The

efficiencies of various sampling schemes for this stratified design are compared to

simple random digit dialing and the Mitofsky-Waksberg technique. Gains in

efficiency are demonstrated for nearly all such designs. Simplifying assumptions

about the values of population parameters in each stratum are shown to have little

overall impact on the estimated efficiency.

KEY WORDS: Random digit dialing, optimal allocation, coverage, relative

efficiency
1. THE CURRENT STATUS OF TELEPHONE SURVEY DESIGNS

The two stage random digit dialing design for sampling telephone households,

first proposed by Mitofsky (1970) and more fully developed by Waksberg (1978),

has been widely employed in telephone surveys. The Mitofsky-Waksberg

technique capitalizes on a feature of the distribution of working residential

numbers (hereafter referred to as WRNs) in the U.S.: specifically, the WRNs tend

to be highly clustered within banks of consecutive telephone numbers. Currently,

only about twenty percent of the possible telephone numbers within the known

area code, three digit prefix combinations are WRNs for the United States as a

whole. However, if a bank of 100 consecutive telephone numbers can be

identified that has at least one known WRN then, on average, over 50 percent of

the numbers in the bank will be WRNs. A technique which can identify 100-

banks containing WRNs will greatly reduce the amount of screening necessary to

identify telephone numbers assigned to households.

The two-stage Mitofsky-Waksberg technique starts by obtaining a list of area

code, prefix combinations for the study area (available nationally from BellCore

Research; see Lepkowski, 1988). A frame of telephone numbers, hereafter

referred to as the BellCore Research or BCR frame, is generated by appending all

10,000 four digit suffixes (i.e., 0000 to 9999) to the area code-prefix

combinations. The telephone numbers in the frame are grouped into banks of 100

numbers using the area code, three digit prefix, and the first two digits of the

suffix to specify each bank. For example, the area code, prefix combination

313/764 will have 100 different 100-banks: 313/764-00, 313/764-01, ,

313/764-99. Next, a sample of 100-banks is selected and a single complete

telephone number is generated for each selected bank by appending a two digit,
2

randomly selected, number to the bank identifier. Each of these generated

telephone numbers is dialed in the first sampling stage and the residential status

of each number is determined and recorded. All 100-banks for which the

randomly generated number is not a WRN are discarded. A second stage sample

of WRNs is selected from all 100-banks for which the randomly generated

number is a WRN. Typically an equal number of numbers, say k, are generated

in each bank to start the second stage sampling process. When one of these

second stage numbers is found to be non-residential, it is replaced by another

randomly generated number from the same bank. This process is continued until

k WRNs are identified in each bank. The result is a two stage sample based on

selection of 100-banks with probabilities proportional to the number of residential

numbers in the bank. This methodology has proven to be an excellent technique

for identifying 100-banks with WRNs.

This technique has obvious advantages. The proportion of residential

numbers within the 100-banks retained for second stage sampling is much higher

than for the BCR frame in general, which results in a substantial improvement in

efficiency over simple random digit dialing (RDD). It only requires that the

complete set of area code, prefix combinations for the study area be known, and

that the study staff have access to a random number generator for sampling

telephone numbers. Finally, it also affords, in principle, complete coverage of all

telephone households in the study area.

The Mitofsky-Waksberg technique also has several disadvantages. For

example, not every 100-bank has the required k residential numbers so the second

stage random number generation can use all 99 remaining numbers and still not

achieve the required k WRNs. In addition, determining the residential status of
3

each generated number, especially at the first stage, can be difficult. For instance,

in many rural areas recording equipment which notifies the caller that a number is

not in service is not used. Calls to unassigned numbers are switched to a

"ringing" machine. In these areas it is difficult to distinguish unassigned numbers

from residential numbers where no one is at home during the study period. This

difficulty is more noticeable at the end of a study period due to the need to

replace non-residential numbers. Numbers generated at the end of the study

period as replacements for non-residential numbers at the second stage of

sampling have less time to be called. A small residual of unresolved numbers

accumulates at the end of the study period, and final determination of residential

status is impossible within study time constraints. Procedures for handling these

unresolved numbers have been proposed (Burkheimer and Levinsohn, 1988), but

they often detract from the simplicity of the overall method.

Many of the difficulties with the Mitofsky-Waksberg technique can be

reduced in importance through pre-screening of telephone numbers and the use of

computer assisted interviewing systems. However, these difficulties are not

eliminated unless departures are made from the basic simplicity and/or underlying

probability sampling principles of the method (see for example Potthoff, 1987

and Brick and Waksberg, 1991).

Alternatively, lists of published telephone numbers have been employed as a

frame. These lists of published numbers are available for the entire country from

commercial firms such as Donnelley Marketing Information Systems. A

straightforward selection of telephone numbers from such lists provides a very

high rate of WRNs (typically at least 85%) but unfortunately does not cover

households with unpublished numbers. Comparisons of telephone households
4

with and without published numbers (see, for example, Brunner and Brunner,

1971) indicates that substantial bias may result.

Lists of published numbers can be employed in a manner to provide coverage

of households with unlisted numbers as well. Groves and Lepkowski (1986)

describe a dual frame approach in which a sample of listed numbers is combined

with a random digit dialed sample through post-stratification estimation. If

coverage of the population is less important, lists of published numbers can be

used to identify 100-banks with at least one listed residential number, and

sampling can be restricted to such banks. Survey Sampling, Inc. (1986), and

previously Stock (1962) and Sudman (1973) using reverse directories, selected

samples of telephone numbers from this type of 100-bank. Clearly this approach

does not provide complete coverage of unlisted telephone households, but it can

greatly improve sampling efficiency. In fact these "truncated frame" methods

have rates of residential numbers comparable or higher than the MitofskyWaksberg technique, and the troublesome replacement of non-residential

numbers is not needed. Unfortunately, for many survey organizations, the

coverage deficiency caused by truncating the frame is considered to be

unacceptable.

The purpose of this paper is to examine stratified designs for the BCR frame

as an alternative to frame truncation and Mitofsky-Waksberg designs. As an

example of frame stratification, the BCR frame could be partitioned into two

strata: a "high density" stratum consisting of residential numbers in 100-banks

with one or more listed numbers and a "low density" stratum consisting of all the

remaining numbers in the BCR frame. The "cut-off" point between high and low

density strata is somewhat arbitrary; a cut-off of two or more listed numbers
5

could reduce the chance that 100-banks are inadvertently included due to a keying

error in a telephone number. Direct access to all listed numbers is not required

for this stratification scheme. Counts of listed numbers, or any other indicator of

the presence of listed telephone numbers in a 100-bank obtained from a reverse

directory (in metropolitan areas with such commercial services) or a commercial

list for the entire country, would be sufficient. Preliminary work indicates that

the approximately 50% of the numbers in the high density stratum are WRNs

while only about 2% of the numbers in the low density stratum are WRNs. The

obvious cost difference of sampling from the two strata can be exploited through

differential sample allocation. The telephone numbers in the low density stratum

could be further stratified by careful examination of the characteristics of the 100-

banks as determined by other data available from the BCR frame and/or the

Donnelley list which may result in even further sampling efficiency.

The next section examines the question of the appropriate allocation of

sample between the strata when simple random sampling is utilized within each

stratum. A key feature of the stratified telephone sample approach is that it

permits alternative approaches to sample selection within in the different strata.

Several alternatives are presented and discussed in Section 3. Section 4 presents a

study of the impact of "non-optimal" sample allocation on design efficiency. The

paper concludes with a general discussion contrasting the Mitofsky-Waksberg

procedure and stratified designs.
6

2. THE ALLOCATION PROBLEM FOR STRATIFIED TELEPHONE

DESIGNS

2.1 Background

We assume that the basic sampling frame is the collection of all telephone

numbers generated by appending four digit suffixes to the BCR list of area-prefix

codes. Further, we assume that each household in the target population is

"linked" to one and only one telephone number in the basic sampling frame (to

avoid complications of unequal probability of selection).

We also assume that we have access (possibly only indirect) to a directory

based, machine readable list of telephone numbers. It should be noted that

because many households choose not to list their telephone numbers in a

directory, any such directory based frame will not contain all of the WRNs.

Directory based lists are also by nature out of date so they will omit some

numbers that are currently published WRNs while including others that are no

longer WRNs.

From a survey design point of view these two frames tend to be radically

different. The BCR frame includes all WRNs so it provides complete "coverage"

of the households in the target population, but only about 20 percent of the

telephone numbers included in the BCR frame are actually WRNs. Thus, the "hit

rate" (and hence sampling efficiency) will be quite low for a simple RDD sample

design utilizing the BCR frame. In contrast, a typical directory/list frame covers

only about 70 percent of the target households, but the hit rate is 85 to 90 percent.

In general the sampling efficiency for a simple RDD design using a directory/list

frame is far better than can be attained for the BCR frame using the Mitofsky-
7

Waksberg technique. Unfortunately, the low coverage rates associated with

directory based frames preclude their use in many cases.

The basic idea of the proposed stratification approach is to utilize information

from the directory based frame to partition the BCR frame into two or more strata

with disparate hit rates and then allocate the sample to the strata so as to minimize

cost (variance) for a specified variance (cost). Hereafter the stratum with the

lowest hit rate will be referred to as the residual stratum. The truncated designs

discussed earlier can be included in this general type of design if we allow the

allocation of no sample to the residual stratum, and use mean squared error in

place of variance.

2.2 Basic Notation

Assume that the BCR frame of telephone numbers has been partitioned into H

strata based on a 100-bank attribute which can be determined from either the

BCR or the directory based frame of telephone numbers. The choice of 100-

banks is somewhat arbitrary; banks of from 10 to 500 consecutive numbers could

be considered. For the i

th stratum let

Pi = proportion of the frame included in the stratum,

hi = proportion of the telephone numbers in the stratum that are WRNs 

(i.e., the hit rate),

wi = average proportion of WRNs in the non-empty 100-banks (i.e., the 

average hit rate for non-empty banks),

zi = proportion of the target population included in the stratum, and

ti = proportion of 100-banks in the stratum that contain no WRNs.
8

The average hit rate for the frame is given by h = hiPi

i=1

H

Â and the proportion of

empty 100-banks in the frame is given by t = tiPi

i=1

H

Â .

In general only the Pi 's will be known with certainty. Data from a joint

research project involving the Bureau of Labor Statistics and the University of

Michigan were used to provide approximate values for the parameters hi and wi

for the two strata in the example. Values for the remaining parameters were

calculated using the algebraic relationships ti = 1- hi wi b g and zi = hiPi h . The

approximations for all of the frame parameters for the two stratum design are

given in Table 1 below; note that for the BCR frame h @.211 and t @.605. The

value of h is in close agreement with that given in Waksberg (1978) but the value

of t is somewhat smaller than the .65 provided by Groves (1977). At this time it

is impossible to determine which value of t is more accurate; in fact, the value

may have changed since 1977. More recently, Tucker, Casady and Lepkowski

(1992) estimated the value of t to be .616 for 10-banks which supports the lower

estimate of t for 100-banks

 Table 1. Approximate values of the frame parameters for a two stratum design

based on the BCR frame and Donnelley directory list. Stratum 1 consists of all

telephone numbers in 100-banks with at least one telephone number on the

Donnelley list frame; stratum 2 contains all remaining numbers.

Stratum

Proportion

of Frame

(Pi)

Proportion

of

Population

(zi)

Hit Rate

(hi)

Proportion

of Empty

100-Banks

(ti)

Hit Rate

Within Non-empty

Banks

(wi)

1 .3804 .9402 .5210 .0300 .5371

2 .6196 .0598 .0204 .9584 .4900
9

2.3 The Basic Estimation Problem, Sample Designs and Estimators

We assume the telephone numbers in the i

th stratum are labeled 1 through Mi .

Let

dij = 1 if the j

th telephone number in the ith stratum is a WRN,

0 otherwise.

R

S

T

The variable of interest is the household characteristic Y, and y represents the

value of Y for a particular household. The population parameter to be estimated

is the population mean m = Y./N. , where N.= dij = Ni

i=1

H

Â

j=1

Mi

Â

i=1

H

Â and

Y.= dijyij

j=1

Mi

Â

i=1

H

Â . The term Ni denotes the number of WRNs in the i

th stratum

and N. denotes the number of WRNs in the population.

Consider two sample designs: (1) simple random sampling without

replacement (i.e., simple RDD) from the telephone numbers in the BCR frame,

denoted as design D0 and (2) stratified simple random sampling from the BCR

frame (i.e., independent simple RDD samples are selected from each stratum),

denoted as design D1. Under design D0 the standard ratio estimator for m is

given Y0 = Y$

0 / N$0 where Y$0 and N$0 are the usual inflation estimators for

Y. and N. respectively. The estimator Y0 is asymptotically unbiased for m and its

variance is given by var Y0 c h @ s2 mh where m is the sample size of telephone

numbers and s2 is the population variance of the y's. For the design D1 the

standard ratio estimator of m is given by Y1 = Y$

1 / N$1 where Y$1 and N$1 are the

standard inflation estimators for Y. and N. under stratified sampling. The

estimator Y1 is also asymptotically unbiased for m and

var Y1 c h @

zi

2

si

2 1+ 1- hi b gl i c h

i=1 mi

hi

H

Â (2.1)
10

where li = mbi -mg2/ si

2

 and mi , mi , and si

2 are the stratum sample sizes,

means, and variances, respectively.

2.4 The Cost Model

There are costs associated both with determining the value of the indicator

variable d and the value of the characteristic of interest Y. The cost function for

determining the indicator variable is denoted by C1af, with

C d

c d

c d 1

1

0

1

0 af= ==

RST

if 

if 

This model allows for the possibility that the cost of determining that a telephone

number is not a WRN may be different than determining that a telephone number

is a WRN. In fact the cost of determining the status of telephone numbers that

are WRNs is usually less. The cost of determining the value of the characteristic

Y includes only the additional cost of determining the value of y after the value of

d has been determined. Letting C2a,f represent this additional cost, with

C dy

d

d 2

0 a f, = RSTif = 0

c if = 1 2

The sum c1 +c2 represents the cost of a "productive" sample selection and c0

represents the cost of an "unproductive" selection, then, following Waksberg

(1978), g = c1 +c b 2g/ c0 represents the ratio of the cost of a productive selection

to an unproductive selection.

The total cost for sample selection and the determination of the values of Y is

a random variable for both design D0 and D1 . Letting C Db0g and C(D1 )

represent the total cost of conducting a survey under the two respective designs it

is straightforward to show that
11

E C Db0g= mc0c1+ g- a 1fhh

(2.2)

and

E C Dbg1 = c0 mic1+ g- a f1 hih

i=1

H

 . (2.3)

2.5 Optimal Allocation for Y1

The stratum sample allocation that minimizes varcY1h for a fixed expected

total cost C* (or that minimizes E C Db1g for a fixed variance V*) is specified up

to a proportionality constant by

mi

zisi

hi

1+b1-higli

1+ g- a f1 hi

F

HG IKJ

1/2

(2.4)

where the proportionality constant is determined by substitution into the expected

cost equation (or the variance equation, as appropriate). The proportional

reduction in variance, relative to RDD sampling, under optimal allocation for

fixed cost C* (or the proportional reduction in cost under optimal allocation for

fixed variance V*) is given by

R Y1 c h,Y0 @1-h

zi

si

hi

c h 1+b g 1-hi li c h 1+ g- a f1 hi

12

i=1

LH

N

M O

Q

P

s2c h 1 g1 a fh

2

 . (2.5)

2.6 Practical Problems Associated With Optimal Allocation

The problem of specifying the values for the parameters in the allocation

equations is generic to optimal allocation schemes. For our particular case there

are three basic types of parameters: frame related (zi and hi), cost related

(g and c0 ) and those specific to the variable of interest (li and si

2

). Currently,

we have a fairly good working knowledge of the frame related parameters for the
12

two stratum example and certain other specific stratification schemes. In Section

5, we will discuss several active research projects which should further expand

our knowledge in this area.

It is clear that g ‡1, but the actual value can vary widely. For example, in the

case of a multipurpose survey information is collected for several variables, so the

costs of determining the status of a telephone number, c c 0 1 and , are in effect

amortized over the variables of interest, and g will probably be considerably

larger than unity. On the other hand, if the survey is intended to collect

information on only a single variable then the value of g is probably not much

larger than two or three. Waksberg (1978) considers values of g between 2 and

20.

Potentially the variable specific parameters pose the most serious problem.

Usually our knowledge regarding the values of these parameters is limited and, in

the case of multipurpose surveys, we must decide which variable(s) to use for the

purposes of allocation. Fortunately, in many practical applications, two factors

combine to somewhat lessen this problem. First, the allocation tends to be

relatively "flat" in a neighborhood of the optimum allocation so that the reduction

in variance is relatively robust with respect to allocation. Secondly, in most cases

the variables of interest will not be highly related to variables of the type we are

using for stratification. Therefore, with caution, we assume that li = 0 and

si

2 = s2

 for i =1,2,K , H. Optimal allocation is achieved by

mi

zi

hi

c h 1+ g- a f1 hi

-1/2 (2.6)

and the proportional reduction in variance is
13

R Y1 c h,Y0 @1-h

zi

1+ g- a f1 hi

hi

F

HG IKJ

12

i=1

LH

N

M

M

O

Q

P

P

2

c h 1 g1 a fh . (2.7)

In the case of the two stratum example, the allocation specified by (2.6) implies

that allocation relative to the residual stratum (i.e., m1 / m2 ) is 2.54 when g = 2

and 1.42 when g =10. In the first case the projected proportional reduction in

variance is R =.283 and in the second R =.077 . In fact, it follows from (2.7)

that as the relative cost of determining the value of the variable of interest

increases, the relative benefit of optimal allocation decreases.

The Mitofsky-Waksberg sample design, denoted by D3 , employs two stages

of sample selection (i.e., non-empty 100-banks are selected in the first stage and

WRNs are selected in the second stage). Following Waksberg (1978), we let

(k+1) be the total number of WRNs selected from each sample 100-bank. The

Mitofsky-Waksberg estimator, denoted by Y3, is unbiased for m , and its variance

is minimized when

k +1= max 1 , a f 1-r t

c h 1+ g- a f1 h -t r

F

H

G

I

K

J

1/2 R

S

|

T

|

U

V

|

W

|

(2.8)

where r is intra-bank correlation. Under this "optimal" within 100-bank sample

allocation the reduction in variance, relative to simple RDD, for the estimator Y3

is given by

R Y3 c h,Y0 @1-

c h 1+ g- a f1 h -t

1/2 a f 1- r 1/2

+ ra ft 1/2 2

1+ g - 1 a fh . (2.9)

At the national level Groves (1977) reports that r @.05 for economic or social

statistics. Using this value of r, together with the values of h and t from the
14

two stratum example, the projected proportional reduction in variance for the

Mitofsky-Waksberg procedure is R=.281 when g = 2 and R=.060 when g =10.

The two methodologies appear to produce essentially identical variance

reduction for both values of the cost ratio. However, too much should not be read

into this simple comparison as the projected reduction for each of the procedures

is based on simplifying assumptions that will not be strictly true for any

application. The only inference intended is that the two procedures appear to

highly competitive under a general set of circumstances typically encountered in

application.

3. Alternative Sample Designs

3.1 Truncated Designs

The designs presented in the previous section produce unbiased estimates of

the population mean. Incorrect assumptions regarding the various frame, cost,

and population parameters only effect the efficiency of the estimators, not their

expectations. Unfortunately an extremely high price is paid for the assurance of

unbiasedness because sampling from the residual stratum provides information on

only a small proportion of the population and at a relatively high cost. For

example, suppose we are willing to settle for an estimate of the population mean

exclusive of those households linked to telephone numbers in the residual stratum

(i.e., we "truncate" the original frame by eliminating the residual stratum and

select a stratified RDD sample from the remaining telephone numbers). For the

two stratum example the "truncated frame" would consist only of those telephone

numbers in the first stratum. The hit rate for the sample from the truncated frame
15

would be .521, in contrast to a hit rate of .211 for the entire frame. However,

only about 94% of the target population would remain in scope.

In what follows we assume that the truncated frame is simply the original

BCR frame less the residual stratum which (without loss of generality) we assume

to be stratum H. Accordingly, for the truncated frame h h Ph P HH H

* =- - c hb1 is g

the hit rate, t t Pt P HH H

* =- - b gb1 is the proportion of empty 100-banks and g

mmm * =- - z z b HH H gb1 is the population mean. Let design g D4 be stratified

simple random sampling from the truncated frame, and Y4 the standard ratio

estimator of the population mean. The estimator Y4 is asymptotically unbiased

for m*, and, in general, it is biased for m . The (asymptotic) bias is given by

B Y z

z

H H

H

4 1 ch b g b g = -= -

- m m * m m

 . (3.1)

In most practical circumstances the bias tends to zero monotonically as the

proportion of the target population in the residual stratum becomes small,

although, as indicated by (3.1), this is not necessarily the case. In any event,

since the value of m m - H is never known, an upper limit on the proportion of the

population in the residual stratum is usually the key specification to be

determined when considering the use of a truncated frame. For the two stratum

example approximately 6% of the target population is excluded from the

sampling frame and, in almost all cases, this would not be tolerable for Federal

agencies.

The equations for cost, variance, allocation, and proportional reduction in

variance (or cost) are essentially the same as those presented in Section 2. In fact

the only modifications required for equation (2.1) and equations (2.3) through

(2.7) are to replace m by m*

 and, for i =1,2,K , H -1, replace zi with
16

zz z ii H

* = - /b1 g, and replace li with li

* = mi -m c *

h

2

si

2 . Obviously all sums

are only over the remaining H-1 strata. For the special case where only one

stratum remains after truncation the proportional reduction in variance (cost)

reduces to

R Y4 c h,Y0 =1-

h 1+h* c h a f g-1

h *

c h 1 + h g-1 a f

 . (3.2)

Thus for the two stratum design, the proportional reduction in variance (cost) is

approximately R =.492 when g = 2 and R =.206 when g =10. In both cases the

reduction is substantially greater than achieved by the two methods in the

previous section. However, nearly 6% of the population is not covered by the

frame.

In an attempt to retain the relative efficiency of truncation while reducing the

magnitude of the coverage problem, BLS and the University of Michigan are

investigating several alternative stratification plans in an effort to reduce the

proportion of the population in the residual stratum. One promising approach

calls for the partition of the residual stratum into two or more residual strata. For

example, the partitioning could create a residual stratum 3 consisting of telephone

numbers in 100-banks thought to be primarily assigned to commercial

establishments or not yet activated for either residential or commercial use.

Residual stratum 2 will now contain all other telephone numbers in the residual

stratum from the two stratum design D2 . Estimated frame parameters for the

resulting three stratum design are given in Table 2.
17

Table 2. Estimated frame parameters for a proposed three stratum design based

on the BCR frame and the Donnelley list frame.

Stratum

Proportion

of

Frame

(Pi)

Proportion

of

Population

(zi)

Hit Rate

(hi)

Proportion

of Empty

100-Banks

(ti)

Hit Rate Within

Non-empty Banks

(wi)

1 .3804 .9402 .5210 .0300 .5371

2 .2000 .0399 .0420 .9143 .4900

3 .4196 .0199 .0100 .9796 .4900

These data were used to compute the projected proportional reduction in variance

for both a three stratum design and a truncated three stratum design in which

Stratum 3 is excluded. These results, together with a summary of the results for

the two stratum designs and the Mitofsky-Waksberg design, are presented in

Table 3 below. (Although not discussed in the text, Table 3 also includes the

projected reduction in variance for a cost ratio of 20.)

Table 3. Projected proportional reduction in variance (or cost) relative to simple

RDD sampling for five alternative telephone sample designs.

Sample Design

Proportional Reduction

in Variance or Cost

Proportion of

Frame

g = 2 g =10 g = 20 Not in Scope

Two Stratum .2829 .0766 .0320 .0000

Two Stratum (Truncated) .4917 .2055 .1189 .0598

Mitofsky-Waksberg .2811 .0597 .0135 .0000

Three Stratum .3001 .0866 .0389 .0000

Three Stratum (Truncated) .4095 .1574 .0879 .0199

The proposed partitioning strategy successfully reduces the percent of the

population out of scope from nearly 6% to approximately 2%. The projected

proportional reduction in variance for the truncated three stratum design is

approximately R =.410 when g = 2 and R =.157 when g =10. From an
18

efficiency point of view, it occupies the middle ground between the highly

efficient truncated two stratum design and unbiased designs.

Of course the issue to be faced when considering such a design is the

coverage problem. The design is already subject to non-coverage of the nontelephone household population. Truncating the frame may add to any noncoverage bias already due to this source. For any particular application the risk

inherent in sampling from a frame that does not include all of the target

population must be weighed against the potential gain in efficiency. As expected,

the standard three stratum design is slightly more efficient than the two stratum

design. However, the increase in efficiency is so small that it is doubtful that the

added cost of partitioning the BCR frame into an additional stratum is justified

except for the purpose of truncation.

3.2 Designs Using Optimal Allocation and the Mitofsky-Waksberg Procedure

The final design to be considered is based on the stratified BCR frame.

Depending on the proportion of empty 100-banks in the stratum, we use simple

RDD sampling in some strata and Mitofsky-Waksberg sampling in others. The

motivation for this type of design is based on the following two considerations:

(a) Mitofsky-Waksberg sampling tends to be "administratively complex", and

if the gain in efficiency is small, simple RDD is preferred.

(b) It follows from (2.9), applied at the stratum level, that if the proportion of

empty banks in a stratum is "small" then Mitofsky-Waksberg sampling offers

little, if any, increase in efficiency.

Thus, we propose to utilize simple RDD sampling in strata with a "small"

proportion of empty hundred banks and Mitofsky-Waksberg sampling in the
19

remaining strata. The criterion for determining the type of sampling to be utilized

is based on equation (2.8) applied at the stratum level. Specifically, if the

"optimal" total number of WRNs, as determined by equation (2.8), to be selected

from sample 100-banks in a particular stratum is equal to one, then the stratum is

designated a simple RDD stratum; otherwise it is designated a MitofskyWaksberg stratum. In terms of the proportion of empty hundred banks, the i

th

stratum will be an RDD stratum if

ti £

2.25rc1+hi

a f g-1 h

a f 1+1.25r (3.3)

and a Mitofsky-Waksberg stratum otherwise. For the two stratum example, the

first stratum is a RDD stratum, and the second is a Mitofsky-Waksberg stratum

for g equal either 2 or 10.

Formally the proposed sample design is as follows. The BCR frame has been

partitioned into H strata and, according to the criteria given in (3.3), simple RDD

sampling is specified for the first H1 strata and Mitofsky-Waksberg sampling is

specified for the remaining strata. Let

mi= the number of telephone numbers selected from the i

th RDD stratum,

mi

¢= the number of WRNs in the sample from the i

th RDD stratum,

m%i= the number of 100-banks selected from the i

th Mitofsky-Waksberg 

stratum,

m%i

¢= the number of retained 100-banks in the i

th Mitofsky-Waksberg 

stratum,

ki = number of additional WRNs selected from each retained 100-bank,

and

yi = aggregate of y values for the sample WRNs from the i

thstratum.
20

The combined ratio estimator Y5 = Y$

5 N$

5 , where Y$

5 = Mi

i=1mi

H1

å yi×+

Mi

i=H1+1m%i

H

å yi×

ki+1

F

H

G

I

K

J

and N$

5 = Mi

i=1mi

H1

å mi

¢+

Mi

i=H1+1m%i

H

å m%i

¢ , is utilized to estimate the population mean m

and the values of mi ,m%i and ki are to be chosen to minimize varcY5h or the

expected cost as specified.

The estimator Y5 is asymptotically unbiased for m and it is straightforward to

show that

varchY5 @ zi

2si2

i=1mihi

H1 c h 11h b gili zi

2si2

i=H11mihi

H 1 1h b gilikia f 1rb g ki1 e 1j

(3.4)

and

E C Db g5 =c0 mic h 1hia f g1 mic h 1kib g 1ti hib g ki1a f g -1

i=H1+1

H

i=1

RH1

S

T|

U

V

W|

 .

(3.5)

The optimal values of mi and m%i

 , specified up to a proportionality constant,

are given by

mi zisi

1+b g 1- hili

hic h 1+hia f g-1

F

H

G

I

K

J

1/2

 (3.6)

for i H =1 1 , , L and,

m%i zisi

lib g 1- hi +r

hiti

F

H

G

I

K

J

1/2

 , (3.7)

for iH H = +1 1, , L . The optimal value of (ki +1), for iH H = +1 1, , L , is given

by

ki +1 = max 1 , tia f 1- r

1+hia f- t g-1 i c hl ib g+r 1- hi c h

F

H

G

I

K

J

1/2 R

S

|

T

|

U

V

|

W|

 . (3.8)
21

The proportionality constant for (3.6) and (3.7) is found by substitution into the

expected cost equation or the variance equation as appropriate.

Under optimal allocation the reduction in variance (or cost) relative to simple

RDD, is given by

R Y5 c h,Y0 =1 hF2

s2c1 g1 a fhh (3.9)

where

F= + - + -

+ +- +-+ - -

=

= +

z

h

h h

z

h

ht t h

i i

i

ii i

i

H

i i

i H i

H

ii i i i

s

l g

s

rl g r

1 2

1 2 1 2

1

1 2

1

1 2 1 2 1 2 1 2

11 1 1

1 1 11

1

1

/

/ /

/

/ / / /

c h b g c h a f

 . c b gh c a fha f

 (3.10)

Under the simplifying assumptions li = 0 and si

2 = s 2

 fo r i = 1, 2 ,L , H ,

F= + -

L

N

M O

Q

P

+ + -+ - -

L

N

M O

Q

P

=

= +

s g

 . s r gr

 

i

H

i

i

i

i

i

i ii

i H

H

z

h

h

z

h

t th

1

1 2

1 2

1 2

1 2 1 2

1

1

1

1 1

1 11

/

/

/

/ /

c h a f

e j b g d i c h a f a f (3.11)

When applied to the two stratum frame, this combined sampling strategy

yields a proportional reduction in variance of approximately R =.440 for g = 2

and R =.157 for g =10. For both of the cost ratios, the reduction in variance is

considerably larger than achieved by any of the unbiased procedures considered

previously. In fact, the variance reduction is essentially equivalent to that

attained by the three stratum truncated design (which is subject to a bias of

unknown magnitude). Thus, on first consideration, this combined sampling

strategy appears to be superior to all of the other methods.

Unfortunately there are practical problems which may preclude the use of this

sampling design in certain situations. For example, the hit rate in the Mitofsky-
22

Waksberg stratum is very low (only .02) so the number of first stage sample 100-

banks must be fairly large in order that the expected number of retained 100-

banks is not too small. On the other hand, the relative number of first stage

sample units allocated to the RDD stratum is considerably larger than allocated to

the Mitofsky-Waksberg stratum, therefore a large overall sample size is required

(see Table 4). Also, from Table 4, the number of WRNs required from each of

the retained 100-banks is relatively large and may actually exceed the number of

WRNs in some banks. Clearly both of these problems are more acute for g = 2

than for g =10. Therefore, the use of this design is restricted to situations where

resources can support a "large" sample, and the cost ratio is moderate to large.

Table 4. First stage allocation ratios and second stage sample sizes for the

combined RDD/Mitofsky-Waksberg sample design applied to the two stratum

BCR frame.

g = 2 g =10

Stratum m m1 2 % Sample Size

Second Stage

m m1 2 % Sample Size

Second Stage

1 28.17 N.A. 14.56 N.A.

2 N.A. 17.00 N.A. 9.00

4. SAMPLE ALLOCATION AND DESIGN EFFICIENCY

In Section 2.6 the problem of specifying the parameters required to optimally

allocate the sample to the various strata was considered. It was noted that the

variable specific parameters (i.e. the li

 and si

2) tend to pose the most serious

problem since we usually have little information regarding their values. For most

cases the variables of analytic interest will not be very highly related to the

variables used for stratification. Thus it is reasonable to assume that

li = 0 and s i

2= s 2

 for i = 1,2,K , H. Under these assumptions the optimal
23

allocation is given by (2.6) and the proportional reduction in variance is given by

(2.7).

It is obvious that for any particular application these assumptions will never

be strictly true, so when we allocate according to (2.6) the actual proportional

reduction in variance will not be that given exactly by (2.7). Furthermore,

allocating according to (2.6) will not provide the maximum reduction in variance

which is achieved under the optimal allocation specified by (2.4). Assuming that

we plan to allocate according to (2.6) two questions need to be addressed: (1)

does (2.7) give a reasonable approximation to the actual reduction in variance,

and (2) is the actual reduction in variance reasonably close to the maximum

possible reduction in variance? A single simple answer is not possible for either

question because the outcome depends on exactly how and to what extent the

assumptions failed. In the following we address these question for the two

stratum design under three specific cases of model failure which are typical of

situations encountered in the "real world". In all three cases the results indicate

strongly affirmative answers for both questions.

In the first case we assume that s1

2 = s2

2” W 2 but l1 „ l2. The projected,

the actual, and the maximum reduction in variance were computed for selected

values of b= l1 - l 2 = m 1 - m 2 W between 0.00 to 0.50 and the results are

presented in Table 5 below. Based on our previous discussion regarding the weak

relationship between the analytic and stratification variables it would seem highly

unlikely that b will ever be larger than 0.50. The results in Table 5 indicate that

for both cost ratios and for all selected values of b the actual reduction in

variance achieved by allocation under the simplifying assumptions is essentially

equivalent to that which would be attained under "optimal" allocation. For both
24

cost ratios the projected reduction in variance is always larger than the reduction

actually attained and the difference increases as b becomes larger. However, it

should be noted that for b £ .35 the percentage difference between the projected

reduction and the actual reduction is less than 10% when g =10, and less than

4% when g=2.

Table 5. The projected, the actual, and the maximum proportional reduction in

variance for cost ratios of 2 and 10 and values of b between 0.00 and 0.50.

b

g = 2 g =10

Projected

Reduction

Actual

Reduction

Maximum

Reduction

Projected

Reduction

Actual

Reduction

Maximum

Reduction

0.00 .2829 .2829 .2829 .0766 .0766 .0766

0.10 .2829 .2820 .2820 .0766 .0761 .0761

0.20 .2829 .2793 .2794 .0766 .0745 .0746

0.30 .2829 .2748 .2750 .0766 .0720 .0721

0.40 .2829 .2686 .2692 .0766 .0684 .0689

0.50 .2829 .2607 .2619 .0766 .0639 .0649

The second general case considered assumes that the analytic variable is

Bernoulli, where p1 and p 2 represent the proportion of the population with the

attribute of interest in stratum 1 and stratum 2, respectively. The projected, the

actual, and the maximum proportional reduction in variance were computed for

two specific cases of assumption failure, namely p2 =.90 p 1 and p 2 = 1.1 0 p 1 ; p1

was allowed to vary from .05 to .50 and cost ratios of 2 and 10 were considered.

As discussed before it is probably reasonable to assume that p2 will be within

10% of p1 in most "real world" situations so these results can be considered

general for Bernoulli type analytic variables. The actual reduction in variance

was virtually identical to that attained under optimal allocation in all cases ; thus,
25

allocation under (2.6) can be considered (near) optimal. The projected reduction

in variance was also very close to the actual reduction. When p2 was smaller

than p1 the actual reduction was always larger than the predicted reduction, and

the converse was true when p2 was larger than p1. In both cases the maximum

difference (which was only about 3.5% of the actual reduction when g=2 and

8.3% of the actual reduction when g =10) occurred when p1=0.05 and

monotonically decreased as p1 increased.

In summary; the two cases considered seem to indicate that so long as the

assumptions which yield the allocation specified by (2.6) are not radically

violated the variance will be very near that attained under optimal allocation.

Furthermore, the proportional reduction in variance given by (2.7) provides an

approximation for the actual reduction in variance which is at least accurate

enough for the purposes of survey design.

5. CONCLUDING REMARKS

The strengths of the Mitofsky-Waksberg technique for generating telephone

samples are clear: high hit rates in the second stage of selection, an efficient

method for screening empty banks of telephone numbers, and a conceptually

ingenious approach to sample generation. It is a remarkable testimony to the

strength of the technique that it is widely considered to be the standard method of

random digit dialing with few serious competitors after many years. The

weakness of technique (first stage screening and replacement of non-residential

numbers during the data collection) does not, on the surface, seem to be important

relative to its general strength. However, these features can cause substantial

difficulty, especially in short time-period telephone survey operations.
26

In this paper stratified designs, based on commercial lists of telephone

numbers, are proposed as alternatives to the Mitofsky-Waksberg technique. Both

two and three stratum designs are studied in detail. In addition to simple random

sampling within each stratum, two general alternatives are considered

(1) simple random sampling from all strata except the low density stratum 

frame where the Mitofsky-Waksberg method is used, and

(2) simple random sampling from all strata except the low density stratum 

which is not sampled at all.

The basic thesis of this paper is that stratified sampling methods, using strata

based on counts of listed telephone numbers, are at least as efficient as the

Mitofsky-Waksberg technique. Furthermore, these designs can eliminate the need

for the troublesome replacement of non-residential numbers at the second stage,

since the only telephone numbers that must be dialed in the high density stratum

are those that are generated at the beginning of the study. Specific conclusions

include the following;

• For low cost ratios, the two and three stratum designs are as efficient as the 

 Mitofsky-Waksberg approach,

• When numbers can be dropped from the low density stratum, these 

 alternatives designs are much more efficient, but at the price of unknown 

 bias due to excluding part of the target population.

• When cost ratios are high, the two and three stratum approaches are clearly 

 superior.

A critical issue is the magnitude of the bias introduced by dropping the low

density stratum. As noted previously, approximately 7% of U.S. households do

not have a telephone and truncating the frame may add to the non-coverage bias.
27

As less than 5% of the U.S. household population is expected to be contained in

the low density stratum it is likely that the additional coverage bias will not be

substantial for many characteristics of the total population. On the other hand, for

some characteristics, and for some subgroups of the population, the magnitude of

the additional bias may be large enough to be of concern. Further empirical

investigations of this population must be conducted.

There are two costs associated with the use of stratified designs that may

detract from their use: the cost of the commercial list used to stratify the BCR

frame and the overall lower hit rate. The cost of stratifying the frame into high

and low density strata is not addressed in this investigation because the requisite

information was derived from a specialized research file. The cost of

stratification is a fixed cost and therefore will reduce the resources available for

data collection. It is not known what the fixed cost will be in the future as

arrangements are made with commercial vendors to routinely provide such data.

Furthermore, this fixed stratification cost can be amortized over multiple studies

to greatly reduce its impact on any single sample. It is unlikely that data

collection for one time surveys will find either the Mitofsky-Waksberg or the

stratification method described here to be as cost-effective as indicated. Further

investigation is needed into the frame costs before a complete answers can be

found.

The second cost issue concerns the lower hit rates presented in this paper.

Given the relative competitive efficiencies of the alternatives considered here, it

appears that the lower hit rates do not seriously detract from the efficiency of the

alternatives. It may be possible to improve the hit rates in the high density

stratum if smaller banks of numbers are used. For example, in another
28

investigation we have found that 10-banks will have hit rates in the neighborhood

of .57 compared to the .52 reported here for 100-banks. Of course, working with

10-banks substantially increases the size of files and processing operations that

must be used to generate samples and the cost of a 10-bank frame is likely to be

much higher than the 100-bank frame.

The cost models as shown in (2.2) and (2.3) are relatively simple, ignoring

many cost differences in the telephone survey process that may be important for

comparisons of relative efficiencies of the designs. These cost models allow the

allocations to be expressed in a straightforward way, but they do not specifically

address the cost components associated with two features of the MitofskyWaksberg technique that the alternative designs address; replacement of

nonworking numbers and weighting to compensate for exhausted clusters. Thus,

the cost models ignore structural cost differences between the Mitofsky-Waksberg

approach and the proposed alternatives that, if properly taken into account, could

effect the relative efficiency of the two methods.

Clearly the results presented here are insufficient to draw final conclusions

about the overall value of these alternative designs. Further cost data and

empirical evidence on the size of the bias caused by eliminating the numbers from

the low density stratum is required before a final conclusion can be reached.

ACKNOWLEDGMENTS

The support and assistance of Clyde Tucker and Bob Groves is gratefully

acknowledged. The findings and opinions expressed in this article are those of

the authors and do not necessarily reflect those of the U. S. Bureau of Labor

Statistics or the University of Michigan.
29

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