NASA Contractor Report 178062

AN EVALUATION OF STUDY DESIGNS FOR

ESTIMATING A TIME-OF-DAY NOISE WEIGHTING

James M. Fields

THE BIONETICS CORPORATION

Hampton, Virginia

Contract NAS1-16978

March 1986

(NASA-CB-178062) AN EVALUATION OF STUDY N86-22306

DESIGN FOR ESTIMATING A TIME-OF-DAY NOISE

WEIGHTING {Bionetics Corp.) 30 p

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TABLE OF CONTENTS

SUMMARY 1

INTRODUCTION 2

SYMBOLS 2

ALTERNATIVE APPROACHES FOR REDUCING CORRELATIONS BETWEEN

DAYTIME AND NIGHTTIME NOISE LEVELS IN STUDY DESIGNS .... 3

VALIDITY OF ALTERNATIVE APPROACHES 4

STATISTICAL METHODS FOR PREDICTING THE ACCURACY OF NEW

STUDIES 6

Theory for Predicting the Accuracy of Estimates of

Nighttime Weights 6

Values of Regression Coefficients and Residual Error

Variances from Prevous Studies 8

Design Effects from Cluster Sample Designs Used in

Noise Surveys 9

PREDICTING THE ACCURACY OF TIME-OF-DAY STUDY DESIGNS ... . 10

CONCLUSIONS: THE FEASIBILITY OF STUDYING TIME-OF-DAY

EFFECTS IN A COMMUNITY SETTING 14

APPENDIX A: SOURCE OF EQUATION TO PREDICT SAMPLING VARIANCES

OF NONLINEAR REGRESSION COEFFICIENTS 16

APPENDIX B: JACKKNIFE VARIANCE ESTIMATES 18

REFERENCES 20

TABLES 22
SUMMARY

The relative importance of daytime and nighttime noise of

the same noise level is represented by a time-of-day weight in

noise annoyance models. The high correlations between daytime and

nighttime noise have been regarded as a major reason that previous

social surveys of noise annoyance could not accurately estimate

the value of the time-of-day weight. This report describes study

designs which could reduce this correlation between daytime and

nighttime noise.

Most alternatives which would reduce this correlation are dependent upon studying short-term variations in noise environments.

The evidence suggests that designs based on short-term variations

in nighttime noise levels would not be able to provide valid measures of response to nighttime noise, because it is likely that

people can not form accurate evaluations of the long-term effects

of nighttime noise over short time spans.

The accuracy of the estimate of the time-of-day weight is

predicted for designs which are based on long-term variations in

nighttime noise levels. An examination of various alternative

long-term designs, including contrasts of areas with and without

nighttime noise, suggests that accurate estimates can not be

formed with cross-sectional surveys based on noise environments

found around United States airports. The difficulty in creating

accurate estimates occurs even if the correlation between daytime

and nighttime values of LEQ can be eliminated.

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INTRODUCTION

Information about the variation of noise impact with the

time of day is needed to establish the effects of nighttime noise

and to determine the relative impact of daytime and nighttime

noise. While controlled laboratory experiments can provide some

information about these issues there is also a need for field

studies which would provide information about people's reactions

to familiar noise sources in a residential environment.

Attempts have sometimes been made to obtain information

about time-of-day issues with general purpose social surveys of

noise annoyance (Langdon and Buller, 1977; Second Survey. . .,

1971; Wilson, 1963). Two major problems have limited the amount

of information which can be drawn from these surveys. The first

problem is that high correlations between daytime and nighttime

noise levels have made it impossible to establish the independent

effect of nighttime noise (Fields, 1985c). The statistical estimates are thus highly inaccurate and have large standard errors.

The second problem is that the relationships between some nighttime human response measures and nighttime noise levels have been

weak (Fields, 1985a). There is thus uncertainty about the validity of the nighttime human reaction measures (ie., their ability

to measure nighttime noise impact).

This report first outlines some alternative study designs

which might be expected to reduce the correlations between daytime and nighttime noise levels. The likely validity of these

study designs is then discussed in the following section. Statistical methods for predicting the accuracy of alternative study

designs are described in the following section. The predicted

accuracy for several alternative designs is then presented.

Finally conclusions are presented about the feasibility of obtaining information about time-of-day effects in a community

setting.

SYMBOLS

a,c Constants used in time-of-day models

A Annoyance

B Partial regression coefficient for time period (j) or

noise index (I)

E Error term (The part of annoyance scores which is not

explained by variables in a model)

LEQj Equivalent continuous sound level for period j, dB

LI Noise level for noise index I, dB

-2-
m Sample size

tj Number of hours in period j

wj Weight to be multiplied by number of events (N) or

relative sound pressure squared

Additional Subscripts

d Daytime period

i A single noise event

I Noise index I

j A time period

n Nighttime period

ALTERNATIVE APPROACHES FOR REDUCING CORRELATIONS BETWEEN DAYTIME

AND NIGHTTIME NOISE LEVELS IN STUDY DESIGNS

If daytime and nighttime noise levels are too hightly correlated in a study design, then it is not possible to estimate the

separate effects of daytime and nighttime noise. One of the requirments for an adequate study design is thus that it include a

suitable combination of daytime and nighttime noise levels.

Strategies are presented which could reduce correlations for

aircraft noise studies.

Strategy 1; Cross-sectional design: Contrast long-term noise

environments of different areas. - This is the conventional community noise survey approach. In the past the general purpose

noise annoyance surveys have not been created with specially

designed samples for studying nighttime issues. Thus the possibilities for evaluating the impact of nighttime noise have not

been fully explored with existing surveys. For this strategy to

succeed it would be necessary to find areas with similar noise

levels at one time of day (eg. daytime), but quite different levels

at another time (eg. nighttime). The most extreme contrast of

this type might be found between airports with and without nighttime curfews.

The fact that reactions in different study areas are compared

leads to an important potential weakness in such a cross-sectional

design. Past research has shown that the responses to noise are

affected by unidentified area characteristics other than noise

level (Fields, 1983). As a result there are uncontrolled area

differences which increase the variance of estimates and make it

more difficult to precisely estimate the effects of the time-ofday noise level area differences. The remaining strategies are

all built on longitudinal designs in which contrasting time of

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day environments are experienced by the same individuals on different days. This offers the potential for controlling some of

the area differences.

Strategy 2; Contrast reactions before and after permanent

changes in noise conditions. - For this study design people from

an area (possibly the same people) can provide two long-term evaluations of two different noise environments; the noise environment

before a change and the noise environment months or possibly

several years after a change. Such changes could be brought about

by changes in policy or, more likely, by changes in physical facilities such as new configurations of runways.

Strategy 3; Contrast reactions before, during and after

temporary changes in noise conditions. - For this strategy it is

necessary to be able to identify locations where there will be

temporary changes in aircraft operations. The same people can

then be interviewed before any changes occur, during the time when

the temporary operating conditions are in effect and after the

operations have returned to the pre-existing state. This design

has been utilized for a study of noise (not specifically nighttime

noise) around the airport in Burbank, California (Fidell, et al.,

1981).

Sterategy 4; Contrast daily annoyance levels for daily variations in noise conditions. - If careful measurements are made of

noise levels on particular days then repeated interviews can be

scheduled to ask about contrasting types of noise environments.

Respondents can be repeatedly questioned about their annoyance

reactions for the previous 24 hours. This type of strategy has

been used in a study of reactions to helicopter noise (Fields and

Powell, 1985).

Strategy 5; Control aircraft operations from military facilities to provide contrasting temporary or daily variations in

noise conditions. - With the appropriate cooperation from military

authorities it is possible at some facilities to vary the noise

levels at different times of day on a daily or possibly longer

basis. Variations in training and operation cycles can also normally create some variations in the noise environments at different times of day around military air facilities (Fields, 1985b).

VALIDITY OF ALTERNATIVE APPROACHES

A satisfactory nighttime study design must provide a valid

measure of human response. A valid measure is one which measures

the concept which it is designed to measure. In the present case

a valid measure must be one which successfully measures human response to noise. The precision of the measures will be considered

in the next section. The effects of sample size and random errors

in responses will be considered at that point. At this point,

the problem is the narrow one of whether the study designs will

produce interviews which will in the long run, on the average,

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provide measures of people's feelings about the noise.

The policy interest is in people's long-terra reactions to a

noise. A valid measure must provide an indicator of how people

would respond to nighttime noise levels over a long period of

time in residential areas. Some evidence suggests that even for

long-term judgements there may be a weaker relationship between

noise and annoyance during the nighttime than during the daytime

(Fields, 1985a). This problem could be even more serious for

short-term judgements.

For daytime noise assessments, there is evidence that reactions are responsive to short-term variations in noise level and

that reactions to short-term exposures are similar to those to

long-term exposures. A study of response to helicopter noise

during the daytime hours found that people were sensitive to both

the noise level and number of helicopter flights during the middle

of the day on a daily basis (Fields and Powell, 1985). A study

around Burbank airport asked about reactions to aircraft noise

". . . while you have been at home over the past WEEK. . ." It

was found that people's reactions followed the noise levels rather

closely in this study in which the interviews were conducted in

as few as 11 days after a change in aircraft noise exposure

(Fidell, et al., 1981). This sensitivity of general noise annoyance responses to changes in noise level contrasts sharply with

the findings from an earlier study of nighttime noise by some of

the same researchers (Fidell and Jones, 1975).

In a previous study of aircraft noise around Los Angeles

Airport, people were interviewed both before and after a major

change in nighttime noise operations (Fidell and Jones, 1975).

The number of nighttime flights were almost completely eliminated

from an area which had previous had approximately 50 flights a

night. Residents were interviewed before the change in operations

and after the change in operations. One round of the repeated

interviews occurred four to six weeks after the reduction in operations. Respondents were asked about the effect of aircraft noise

on sleep, about sleep disturbance and about whether they had

noticed any changes in numbers of flights. When the reactions from

before and after the elimination of flights were compared, there

was no evidence of any change in reactions. People were not even

aware of the reduction in the number of flights. The results from

the study thus suggest that people are not sensitive to changes

in nighttime noise environments over short periods of time. While

a single study does not prove that people do not notice short-term

changes in nighttime noise environments, the results of this study

suggest that it can not be assumed that a valid nighttime rating

can be obtained from judgements of short-term noise environments.

While a number of interpretations of the results from this

study are possible, there are several characteristics of the sleep

period which could account for an absence of a rapid response to

changes in nighttime noise environments. Some of these character-

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istics point to problems with any survey measure of response to

nighttime noise. People may often not be aware of disturbances

to sleep when they are not actually awakened. People may become

accustomed to aircraft noise to the extent that they are conscious of being disturbed by only a very small proportion of nighttime aircraft noise events. Even a month-long period may not then

be long enough to detect the difference in the number of times they

are disturbed at night. People may also have their sleep disturbed

and be awakened but still not be able to accurately identify the

source of the disturbance. All of these characteristics can be

contrasted with the daytime period when people are conscious and

can readily assess the noise environment.

The results of the Los Angeles study and the characteristics

of the sleep period suggest that there may be considerable difficulties in obtaining a valid measure of the reaction to short-term

changes in nighttime noise environments. Even if it were possible

to find a reaction measure which was sensitive to nighttime noise,

it would be likely that nighttime and daytime reaction measures

would differ in the relative speed with which they could sense

changes in their respective noise environments. The result of this

difference in sensitivity would still lead to a biased measure of

the relative effect of daytime and nighttime noise.

STATISTICAL METHODS FOR PREDICTING THE ACCURACY OF NEW STUDIES

The method for predicting the accuracy of estimates which

could be obtained from future studies rests on statistical theory

which utilizes information about study design variables and population parameters. After an introduction to the equations which

are used to predict the accuracy of estimates of the nighttime

weighting, estimates of the relevant population parameters are

provided.

Theory for Predicting the Accuracy of Estimates of Nighttime

Weights

A method is required for predicting the approximate variance

of the nighttime weighting which can be expected for differen t

sample designs. The conventional adjusted energy model which

weights the effects of noise at differen t times of day can be

expressed in a non-linear regression equation of the following

form:

[

LEQH/10 LEQ_/10 1

td«Bd«10

 a + tn»Bn«10 n )/24|

where a and BI are constants, LEO is the equivalent continuous noise

level for either the day (LEQd ) or night (LEQ n ), "t" is the length

of the time period for td (the daytime) or tn (the nighttime), and

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Bd and Bn are partial regression coefficients for the daytime and

nighttime.

The value of the nighttime weight (wn) can then be seen to be the

ratio of the two partial regression coefficients:

wn = Bn/Bd

The sampling distribution of wn departs severely from the normal distribution, but the sampling distribution of Bn is approximately normal. As a result the procedures in this paper are

directed at first estimating Bn and the variance of Bn ( OB )

and then transforming the results to provide the estimates of the

nighttime weight, wn , and the confidence intervals for the estimate

of the weight.

It should also be noticed that since there are only two variables representing the noise level ( LEQd and LEQn) but three slopes

being estimated (Bj, Bd , Bn)/ the equation is over-identified and

there is not a unique value for each of the parameters. When it

is decided to combine two of the parameters in the above ratio by

setting the sum of the partial regression coefficients to one

1), then a unique solution is possible.

An asympotic approximation of the variance of Bn can be formed

(see appendix A). For large sample sizes the sampling distribution

for Bn approaches the normal distribution. The prediction for the

variance of simple random samples is:

°B2 = o2

e/m»( a2x/( o2x0

2

Y - (oXY)

2

))

where:

LEQ /10

X = 10«log10(BnoDIF + tdolO

 d )

LEQ /10

Y = 10olog10(e)oBIo(DIF/(Bn<»DIF + td»10

 d ))

LEQ /10 LEQ /10

and DIP = tn!0

 n

 - td!0

 d

Four of the parameters which enter into the estimate of this

variance are study design variables: the social survey sample size

(m) , the daytime noise exposure ( LEQd ) , the nighttime noise exposure (LEQn) and the relationship between the two noise exposures

Two of the other parameters depend upon characteristics of

the human response to noise, (a e and Bj). Estimates of these

parameters are provided in the next subsection.

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The theory which has been presented relates to simple random

samples. Noise surveys, on the other hand, are almost always based

on clustered samples in which individuals are clustered into study

areas. Theoretical bases for making predictions of variances of

regression coefficients from complex clustered samples have been

developed for two different approaches. Kalton (1983) develops a

method using regression models which include a random intercept

term associated with the sample clusters. Tomberlin (1985) develops a method using regression models which include a random partial regression coefficient term associated with the sample

clusters. Both approaches can be applied to more complex multistage designs and provide bases for choosing the most efficient

sized study areas. Estimates of the parameters which are needed

to apply Kalton's approach are available in a previous publication

(Fields, 1983), however, for the time-of-day design discussion in

this paper a second approach is taken to evaluate the effects of

clustered sample designs.

In this paper the effects of clustering are evaluated by comparing predicted simple random sampling accuracy (standard errors

of partial regression coefficients) with the actual accuracy measured using the appropriate pseudo-replication sampling error calculation techniques. The results of these analyses are reported

in this report.

Values of Regression Coefficients and Residual Error Variances

From Previous Studies

In order to apply the simple random sample prediction equation it is necessary to have information about the relationship

between the values of the residual error variance (o e) and

the partial regression coefficient for the noise index (Bj). The

relationship between these variables has been calculated on the

bases of 24 analyses of annoyance questions used in 10 studies.

The results of these analyses are presented in table I.

References to the studies are included in the first column

of table I. The type of annoyance index is described in the

second column. The "Verbal" scales come from annoyance questions

which present a set of verbal descriptors from which the respondent must choose. The "Numeric" scales come from questions in

which only the end points of a numerical scale are given verbal

labels. Dichotomous measures of high annoyance are described in

table I with the verbal label which was presented to the respondent in the interview. One index based on activity interference

items and another index based on the average of several general

annoyance items are also included. (The exact wording of all

these questions has been presented in appendix A of a previous

report (Fields, 1985c)).

The values of the first two quantities in table I are dependent upon the scaling of the annoyance variable. It is the relationship between the values of the two quantities which affects

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the variance of the time-of-day weighting. The variance of the

weighting is directly related to the standard deviation of the

error term and inversely related to the value of the partial regression coefficient. The last column thus presents the ratio of

these two quantities. The lowest values identify the analyses

which would lead to the greatest predicted accuracy of study

designs. The values of this ratio differ by study and type of

annoyance question. In general the high annoyance dichotomy

provides the least predicted accuracy.

The values of the partial regression coefficients and residual error variances from the first two columns of data will be

directly entered into the previously presented equations to predict the variance of the time-of-day weighting in a later subsection of this report. In planning future surveys investigators

might base design decisions on the particular surveys in table I

which most closely approximate the conditions which are to be

expected in their planned surveys.

Design Effects from Clustered Sample Designs

Used in Noise Surveys

Community noise annoyance surveys are generally based on

clustered samples. Individuals are selected from houses which

are clustered together at a number of study locations. This

design is relatively efficient for overall study costs because

the noise measurement costs are dependent on the number of study

areas, not the number of respondents. Past research has shown

that people from the same study area have more similar annoyance

reactions than would be expected from their shared noise levels.

As a result the effective sample size is smaller than would be

indicated by the number of respondents in the sample. How much

smaller depends upon the amount of clustering (ie. degree of

similarity) of annoyance reactions within study areas.

In order to measure the effects of this clustering of responses eight studies have been examined. Table II lists the eight

studies and the annoyance questions which were examined from each

study. The effects of the clustering are indicated for two statistics from each study, the partial regression coefficient for the

total noise index (Bj) and the nighttime partial regression coefficient (Bn). (The nighttime coefficient has been standardized as

described earlier so that : B^ + 8,3=1.)

The incorrect simple random sample estimates of the sampling

variances are provided for each of the two regression coefficients.

The table also includes the estimates of the standard errors which

do take into account the clustered sample design. These later

estimates are based on a pseudo-replication technique, jack-knife

repeated replication. For this technique estimates of the regression coefficients are made repeatedly on subsets of the total

sample from which individuals have been removed on the basis of

their study-area membership. The variance of these estimates can

-9-
be used to estimate the accuracy of the entire sample. A more

detailed description of the technique appears in appendix B.

In table II the ratio of the standard error of the regression coefficients (based on the jack-knife) to the incorrect

estimate of the standard error (based on simple random sampling

formula) is labeled the "design effect" (deff). The design effect

can be used to estimate the accuracy of any future sample which

would be based on the same design. If a standard error of a new

study design has been calculated using simple random sampling

procedures and if the new cluster design is similar to one of the

designs in table II then the predicted simple random sampling

standard error can be multiplied by the design effect from the

similar study in table II to predict the expected standard error

for the new design.

For the total noise index partial regression coefficient,

the design effects range from 1.4 to 2.6. For the nighttime noise

partial regression coefficient the design effects vary much more

widely, from 0.9 to 9.6. Previous analyses of these same data

sets found that accurate estimates of the nighttime partial regression coefficients could not be obtained (Fields, 1985c). This

has the secondary effect of providing poor estimates of all statistics including the estimates of these design effects for the nighttime partial regression coefficients. The estimates of the total

noise index partial regression coefficients are much more accurate.

The corresponding estimated design effect of roughly deff=2 is

probably the best estimate.

PREDICTING THE ACCURACY OF TIME-OF-DAY STUDY DESIGNS

The accuracy of estimates of time-of-day weights can be predicted for alternative study designs on the basis of the methods

outlined in the previous section. In order to make these predictions, estimates are needed of the value of the residual error

variance, of the overall noise index partial regression coefficient and of the design effect. Alternative noise exposure designs

and alternative true values for the time-of-day weight can then

be specified and the resulting accuracy of the estimates of the

time-of-day weights can be predicted.

The estimates of the residual error variance and the partial

regression coefficient for the overall noise index come from the

London noise survey. The estimate from this study was selected

after examining the values in table I. For each study the combination of error variance and regression slope which would yield

the best estimate (ie. lowest value of the ratio in the last

column of table I) was identified. Using this criterion the study

with the poorest such estimate was the 1976 Ontario survey. The

study with the next poorest estimate was the London noise survey.

This estimate was accepted as a conservative estimate for the present analysis. The residual error variance is thus set at 3.55

and the slope (the partial regression coefficient) is set at 0.08.

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The estimate of the design effect which will be used in the

analyses is deff=2.0. This estimate comes from an examination of

the data in table II. The value of deff=2.0 is close to the mean

of the estimates of the design effect for the partial regression

coefficients for the overall noise index (1.9). The design effect

is sensitive to factors such as the size of study clusters and

thus could vary for different designs.

A baseline noise environment matrix is presented in table III.

Daytime noise levels vary from 60 to 80 dB(A) (LEQ), nighttime

levels vary from 46 to 72 dB(A), and the differences between

daytime and nighttime levels are assumed to be either 8, 10 or 14

dB(A). These differences were selected for the baseline environment on the assumption that there was a reasonable possibility of

finding such differences around United States airports. This

assessment as to the availability of noise environments is based

on the examination of an earlier report (Fields, 1985b) which

presented data on the proportion of nighttime flights at United

States airports and data on noise environments at different times

of day at permanent noise monitoring sites at 11 airports.

The noise environments found in 10 previous community noise

surveys were also considered (Fields, 1985b: Table II). The standard deviation of the daytime noise for the baseline noise environment is similar to that found in previous aircraft surveys and

better (higher) than that found in most previous road traffic

surveys. The standard deviation of the baseline nighttime noise

levels (°LEQ,, ) is similar to that found in two other aircraft noise

surveys (0LEQp=2.4 in 1967 Heathrow survey,

 aLEQ=3.2 in USA nine

airport survey) but toward the lower end of the range of nine

values in six road traffic surveys (aLEQn varied from 1.5 to 5.7).

The correlation between daytime and nighttime noise levels is

still high (^ = 0.94).

Table IV contains predictions of the 95% confidence interval

for the estimate of the nighttime weighting. The nighttime weighting is expressed as a number weight, the number of daytime noise

events which would be required to produce the same annoyance as a

single nighttime noise event. The results from the baseline case

for a value of the nighttime weighting of wn=10, are presented in

the first panel of table IV.

The first columns of table IV describe the design. For the

baseline case the daytime noise levels are at 60, 65, 70, 75,

and 80 dB(A) (LEO) as was previously specified ir. table III. The

differences between daytime and nighttime noise levels are also

as given in table III. With an assumed multiplicative weight of

wr=10, the nighttime partial regression coefficient is Bn=0.909.

Results are provided for samples of four different sizes. For the

baseline case a sample size of 1,000 provides a standard error of

the nighttime regression coefficient of 0.12 and a resulting 95%

confidence interval for the nighttime weight of from wr=0.79 to

wn=+°°. Thus the 95 percent confidence interval includes the

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possibility that nighttime noise is less important than daytime

noise as well as the possibility that nighttime noise has an

infinitely greater effect than daytime noise. A sample size of

1,000, thus, does not provide useful information about the value

of the nighttime weighting for the baseline design. The three

remaining sample sizes (2000, 4000, 10000) also fail to establish

upper confidence intervals, but could establish lower confidence

intervals. However, these 95% confidence intervals are so broad

as to not be able to distinguish between quite large differences

in the values of the nighttime weighting.

The next two conditions in table IV are based on alterations

in the baseline sample. In the first case the range of noise

levels is increased (daytime levels are extended down to 50 dB).

This has no effect on the estimated 95% confidence intervals for

the nighttime weighting. The other condition is based on the

optimistic expectation that noise environments could be found so

that the differences between day and night noise levels could be

expanded to evenly cover the range from a 5 dB difference to a 20

dB difference. This later design could establish an upper confidence interval for a prohibitively expensive study of 10,000 respondents but still has a very wide confidence interval.

The remaining conditions in table IV give confidence intervals

for the baseline sample for four different values of the nighttime

weighting (wn=5, 10, 20, 100). In no case is it possible to establish an upper confidence interval, even with a sample size of

10,000. The lower confidence intervals would be considered to be

unacceptably large for most purposes. If, for example, the value

of the nighttime weighting is wn=100, a sample size of 1,000 would

only be able to establish that the weight is greater than wn=9.9

(rounded to wn=10 in Table IV).

Thus far only the conventional cross-sectional study design

has been considered, a design in which all environments contain

some nighttime aircraft noise. Table V presents a new set of

baseline noise conditions for comparisons between aircraft noise

environments with and without nighttime noise. Such conditions

might be found by comparing the normally operating airports which

have nighttime noise to unusual airports which have no nighttime

noise. The noise conditions might also be found if surveys were

conducted before and after a nighttime curfew were instituted or

if surveys were conducted before and after a new runway were

opened. The publicity attending such aircraft operation changes

might raise some additional questions about the validity of a

questionnaire study, but as this point only the sampling aspects

are considered.

The no-night-noise comparison design in table V results in a

design with much greater variances in nighttime noise levels and

in differences between daytime and nighttime noise levels. The

correlation between daytime and nighttime noise levels (measured

in decibels) is also reduced to almost zero (r^n=-0.06).

-12-
The predicted 95% confidence intervals for this design are

presented in table VI. When this new no-night-noise design (first

entry of table VI) is compared to the previous baseline in table

IV, it is seen that the confidence intervals have only improved

slightly. Upper confidence intervals can still not be established.

The lower confidence interval for a size 2,000 sample, for example,

is improved from wn=l to wn=2. The next design in table VI is

similar to that in table IV in that those areas with nighttime

noise have a wider spread of values. This design results in modest improvements in the confidence intervals. In an attempt to

create a better design, in the next part of the table the areas

with nighttime noise are assumed to have unrealistically high

nighttime noise levels which differ from the daytime levels by

only 2, 5, or 8 decibels. This results in an additional improvement in the confidence intervals, but still gives confidence intervals which would be too large for most uses. The prohibitively

large 10,000 sample size would barely establish a lower confidence

interval which was greater than wn=6. All of the confidence

intervals are affected by the value of the weight. If the true

value of the nighttime weight was wn=8 then the last panel of table

VI shows that even with this very optimistic sample design it would

not be possible to set an upper confidence interval with a sample

size of 4,000.

The interpretation of the results from any of these types of

night noise elimination studies would be limited by the fact that

the reduced standard errors for the estimates of the nighttime

weighting actually derive mainly from the contrast between areas

with noise at night and areas without noise at night. The resulting estimates of the nighttime weight (if sufficiently accurate)

would provide a good indication of whether the effect of eliminating nighttime noise is substantially greater than would have been

expected from an equivalent reduction in daytime noise. The estimates could not necessarily be extrapolated to the more normal

situation in which at least some nighttime noise is present.

The predicted levels of accuracy in tables IV and VI suggest

that satisfactory estimates of the time-of-day weighting could

not be obtained from surveys which contrast the noise environments

around different airports or from surveys which compare areas with

nighttime noise with those without nighttime noise. The accuracy

of the later design might be improved somewhat if a longitudinal

design were used in which the same people were interviewed first

when there was normal nighttime noise and then again later after

the nighttime noise was eliminated. Such a longitudinal design

typically results in some increased precision because some of the

individual differences can be controlled. Just how much the

precision might be expected to improve can not be evaluated without analyses of longitudinal data. These analyses have not thus

far been carried out.

Other patterns of changes in noise environments could be

assessed with longitudinal designs and might yield better esti-

-13-
ates, however, it is uncertain as to whether such patterns of

changes could actually be found. Additional accuracy would be

possible if daytime noise levels remained unchanged but if some

respondents experienced substantial increases in nighttime noise

levels while other respondents experienced substantial decreases

in nighttime noise levels. Short-term changes of this nature may

occur in airport operations, but as was suggested earlier the validity of studies of reactions to any such short-term changes is

doubtful. No routine reasons for expecting such long-term changes

have been identified. However, if such changes were identified

greater accuracy might be predicted than has been possible for the

designs described in this report.

A broad range of day and night noise enviroments has been

considered in this report, but they have not been found to provide

satisfactorily accurate estimates of the time-of-day weighting.

The high level of inaccuracy is present in spite of the fact that

some of the designs have overcome the primary problem which was

identified at the beginning of the report: the high correlations

between daytime and nighttime noise levels. For example, the

designs in table VI have low correlations of either r,jn=0.13 or

0.14. The problem in survey design is not simply to eliminate

the correlation between daytime and nighttime noise values of the

noise index LEQ. An optimal design would need to focus directly

on the complex parameters which were identified in the theory for

predicting the accuracy of estimates of nighttime weights. Given

the range of designs already considered, it seems unlikely that a

careful application of this theory would be able to identify

combinations of existing noise environments which would provide

accurate estimates.

A major difficulty in estimating the time-of-day weighting

may be that the distinction between the different weights is too

fine for any combination of noise environments. The quantity which

is being directly estimated (the nighttime partial regression coefficient coefficient, BQ) must be very exactly specified. For

example for the range of estimates of the nighttime weight from

wn=5 to wn=20 the value of the nighttime regression coefficient

only varies from 8^=0.833 to ^=0.952. Similarly the distinction

between a weighting of wn=20 and wn=100 is represented by only a

0.038 difference in the nighttime partial regression coefficients

of Bn=0.952 and Bn=0.990.

CONCLUSIONS: THE FEASIBILITY OF STUDYING TIME-OF-DAY EFFECTS

IN A COMMUNITY SETTING

The high correlations between daytime and nighttime noise

have been regarded as a major reason that previous social surveys

of noise annoyance could not accurately estimate the value of the

time-of-day weight. This report has described study designs which

would reduce this correlation between daytime and nighttime noise.

-14-
Most alternatives which would reduce this correlation are

dependent upon studying short-term variations in noise environments. The evidence suggests that designs based on short-term

variations in nighttime noise levels would not be able to provide

valid measures of response to nighttime noise, because it is not

likely that people can form accurate evaluations of the long-term

effects of nighttime noise over short time spans.

The feasibility of basing a study on long-term differences

in nighttime noise levels has been assessed for combinations of

noise environments which have been specially selected so as to

increase the accuracy of the estimates of the time-of-day weights.

These designs are based on analyses of noise environments around

United States airports and on optimistic assumptions concerning

the availability of daytime and nighttime noise environments.

Some designs include contrasts between areas with and without

nighttime noise. Some designs virtually eliminate the correlation

between daytime and nighttime noise levels. The analyses in this

report predict that even with these improved designs a cross sectional survey could not provide a useful, accurate estimate of the

time-of-day weight.

ACKNOWLEDGEMENTS

Data for these analyses were obtained from the Economic and

Social Research Archive (ESRC Archive) at the University of Essex,

England. The research was thus made possible by researchers who

deposited their data in the archive. Dr. T. J. Tomberlin provided

professional statistical support especially in writing the

appendices.

-15-
APPENDIX A:

SOURCE OF EQUATION TO PREDICT SAMPLING VARIANCES OF

NONLINEAR REGRESSION COEFFICIENTS

The conventional adjusted energy model for the effects of

noise at different times of day can be represented as:

[

LEO /10 LEQ /10 1

(tdoBd«10

 d + t^wB^lO n )/2)/244 + E

As was noted in the text, a regression equation based on this

model would be over identified. If the sum of the two partial regression coefficients is set to one (3^+1^ = 1) then a new regression

equation can be defined which includes only the nighttime partial

regression coefficient.

A = a +

where DIP is given by

f LEO /10 1

(BnoDIF + tdolO

 a )/24

LEQ /10 LEQ /10

DIP = tn«10 " - tde»10

d

This in a non-linear multiple regression model. Least squares

estimates of the parameters of this model can be achieved via iterative algorithms. See, for example, Neter Wasserman and Kutner

(1985) , Chapter 14.

In designing samples for the purpose of estimating the parameters of this non-linear equation, one must be able to predict

sampling variances for estimators as a function of sample size

and of the distribution of the predictor variables, in this case

the daytime and nighttime noise measures, LEQ^ and LEQn . Formulas

for this purpose can be obtained in a manner analogous to that used

for the more common linear model.

First consider the following linear model:

Then the sampling variances for the least squares estimates

of 3i and $2

 can ^e expressed as a function of the sample size

m and the variances of the two predictor variables, x^ and X2 as

well as their correlation coefficient.

= o£

2

-16-

/m [o1

2

(l-r2)]
= o£

2/m |a22

(l-r2)|

o

where, oe is the variance of the error term E, r is the correlation between the two predictor variables, and a^2 and o2 are

the variances of the predictor variables xj and x2 respectively.

Thus for small sampling variances, one wants a small error variance

oe (i.e. a good prediction model), a large sample size, large

variances in the predictor variables, and a small correlation between the two predictor variables.

For the time-of-day non-linear regression model similar

requirements are necessary for functions of the predictor variables,

xi and x2. These functions are the first partial derivatives of

the non-linear function, taken with respect to the parameters in

question. (See Neter, Wasserman and Kutner, 1985.) Thus, the

two "design variables" for the model are.

9

A LEQ /10

3Bj. = log1Q (Bn«DIF + td»10

 d )

J

3A LEO /10

3Bn = log1 0 (e)oBI e(DIF/(Bn oDIF + td»10

 d ))

These equations provide the definition of the x and y terms

used in the section "Theory for Predicting the Accuracy of

Estimates of Nighttime Weights".

Thus, for small sampling variance in the estimate of the

nighttime regression coefficient Bn , one requires again, a small

error variance, oe , a large sample size, large variance in the

functions 3A/3Bn and 3A/3Bj , and small correlation between

3A/3BJ and

For sampling variance prediction, one requires a preliminary

estimate of the error variance oe , as well as of the parameters

to be estimated, Bj and Bj^ .

-17-
APPENDIX B:

JACKKNIFE VARIANCE ESTIMATES

The adjusted energy summation model can be represented by a

non-linear multiple regression model. Estimates of parameters

and functions of parameters can be obtained via iterative procedures. All three major statistical computer packages, SAS, SPSS,

and BMDP, contain routines for this purpose. See Brown (1977);

Nie et. al . (1975); and SAS Institute, Inc., (1979).

Since the principal computer used for data analysis at NASA

Langley Research Center is a CDC machine, the most convenient of

these computer packages is SPSS. The SPSS subprogram, NONLINEAR,

developed at Northwestern University (Robinson, 1977), produces

the required parameter estimates from SPSS when the program is

installed on a CDC (Control Data Corporation) machine.

Although the standard packages can provide estimates for

parameters of the adjusted energy model they cannot, by themselves,

provide estimates for standard errors based on complex sampling

designs. Standard errors produced automatically by these "canned"

programs are based on simple random sampling assumptions. For

complex samples, a pseudo-replication procedure is appropriate.

Pseudo-replication refers to a class of estimation methods. Of

these, the jackknife is perhaps best suited for use with SPSS.

This technique requires repeated computation of sample estimates

using samples which differ slightly from each other,

Specifically, the estimation procedure is carried out repeatedly, each time leaving out a single primary sampling unit (PSU).

Each individual replication yields a single "pseudo-value". Consider the following notation. Let Y denote an estimate obtained

from the complete sample and Y(i) denote the corresponding

estimate obtained from the sample leaving out the ith PSU. Then,

YI = mY - (m-l)Y(i)

is called the ith pseudo-value, where m is the number of PSU's in

the sample.

The jackknife estimate of the sampling variance is then

obtained as,

Var(Y) = £[Yt - Ye]

2

/m(m-l),

where ,

is the mean of the n pseudo-values. See Miller (1974) and Efron

(1979) for further details of jackknife variance estimation as

well as other pseudo-replication techniques.

-18-
These jackknife calculations require the re-analysis of the

results of m intermediate analyses. Specifically, parameter

estimates based on each jackknife replication, the Y^'s, must be

converted into pseudo-values, and the variance of these pseudovalues calculated. This was accomplished for routine variance

calculations by writing a FORTRAN program which read the output

from the SPSS NONLINEAR Subprogram, and calculated the appropriate

summary statistics.

-19-
REFERENCES

Brown, M.B.: 1977, BMDP-77 Biomedical Computer Programs.

P-Series, University of California Press, Los Angeles.

Efron, B.: 1979, Bootstrap Methods: Another Look at the Jackknife.

Annals pf Statistics, vol. 7, no. 1, pp. 1-26.

Fidell, S.; Horonjeff, R.; Teffeteller, B.; and Pearsons, K.:

1981, Community Sensitivity to Changes in Aircraft Noise Exposure.

NASA CR-3490.

Fidell, S.;Jones, G.: 1975, Effects of Cessation of Late-Night

Flights on an Airport Community. J. Sound Vib., vol. 42, no. 4,

pp. 441-427.

Fields, James M.: 1983, Variability in Individuals' Responses to

Noise: Community Differences. Proceedings of Inter-noise 83',

pp. 965-968.

Fields, James M.: 1985a, Research on the Effect of Noise at

Different Times of Day: Models, Methods, and Findings, NASA CR-3888,

Fields, James M.: 1985b, Data on Noise Environments at Different

Times of Day Around Airports, NASA CR-172612.

Fields, James M.: 1985c, The Relative Effect of Noise at Different

Times of Day: An Analysis of Existing Survey Data, NASA CR-3965,

(1986).

Fields, James M.; and Powell, C.A.: 1985, A Community Survey of

Helicopter Noise Annoyance Conducted Under Controlled Noise

Exposure Conditions. NASA TM-86400 (March 1985).

Kalton, G.K.: 1983, Estimating Regression Coefficients from

Clustered Samples: Sampling Errors and Optimum Sample Allocation,

NASA CR-166117 (May, 1983).

Langdon, F.J. and Buller, I.B.: 1977, Road Traffic Noise and

Disturbance to Sleep. J. Sound Vib., vol. 50, no. 1, pp. 13-28.

Miller, Rupert G.: 1974, The Jackknife—A Review. Biometrica,

vol. 61, no. 1, pp. 1-15.

Neter, John; Wasserman, William; and Kutner, Michael: 1985,

Applied Linear Statistical Models (2nd. Ed.). Irwin, Homewood,

Illinois (Irwin).

Nie, N.H.; Hull, C.H.; Jenkins, J.; Steinbrenner, K.; and Bent,

D.H.: 1975, Statistical Package for the Social Sciences (2nd.

Ed.). McGraw-Hill, New York.

-20-
Robinson, Barry: 1977, SPSS Subprogram NONLINEAR—Nonlinear

Regression. Manual No. 433, Vogelback Computing Center,

Northwestern University, Evanston, Illinois (August 1977).

SAS Institute, Inc.: 1979, SAS User's Guide (1979 Ed.) Raleigh,

NC.

Second Survey of Aircraft Noise Annoyance Around London (Heathrow)

Airport, 1971. Her Majesty's Stationery Office, London.

Tomberlin, Thomas J.: 1985, Statistical Methods for Efficient

Design of Community Surveys of Response to Noise: Random Coefficients

Regression Models, NASA Contractor Report 177933.

Wilson, Alan: 1963, Noise: Final Report. Cmnd. 2056, Her

Majesty's Stationery Office, London.

-21-
TABLE I: REGRESSION COEFFICIENT AND RESIDUAL ERROR VARIANCE

FOR 10 STUDIES

Study3

Annoyance

scale

(number

of scale

points)

Regression anal

Regression

coefficient

for noise

index

(Bx)

Residual

error .-

variance

(o2

e)

/sis

Indicator

of accuracy

(°e/Bj)

PART A: AIRCRAFT SURVEYS

USA nine airport

1967 Heathrow

Numeric (5)

Very

Verbal (4)

Very

Activity

0.07

1.23 .

0.08

1.97

0.13

2.09

1817.83

1.01

1146.74

2.30

21

25

13

17

11

PART B: ROAD TRAFFIC SURVEYS

England traffic

London traffic

1975 South Ontario

1976 South Ontario

1978 Ontario

Western Ontario

French expressway

Verbal (4)

Numeric (7)

Very

Numeric

Verbal (5)

Considerably

Verbal (5)

Numeric (11)

Considerably

Verbal (S)

Numeric (11)

• Considerably

Numeric (7)

Verbal (4)

Very

0.03

0.11

0.48

0.08

0.07

1.48

0.04

0.06

0.81

0.08

0.24

1.54

0.12

0.06

2.67

0.55

3.16

421.84

3.55

0.84

592.23

1.07

7.41

789.78

0.83

4.88

601.69

3.99

0.96

2014.40

21

16

42

23

13

16

29

44

35

11

9

16

16

15

17

PART C: RAILWAY SURVEY

British railway Verbal (4)

Numeric (7 )

Index (11)

Activity

Very

0.03

0.07

0.11

0.42

0.83

3.08

6.45

627.70

32

26

23

60

a The studies and annoyance questions have been described in a recent

publication (Fields 1985c).

-22-
ORIGINAL PAGE IS

OF POOR QUA'ITY

TABLE II: DESIGN EFFECTS FROM TIME-OF-QAY REGRESSION

ANALYSES FOR EIGHT STUDIES3

Study Annoyance

scale3

(number

of scale

points)

Total Noise Index

Regression

coefficient

Bj

Standard

error

(Jackknife)

CBp

I

Incorrect

Std.

error

(SRS)

°

B

I(srs)

Design

effect

^Ksrs)

Nighttime Noise Variable

Regression

coefficient

(Eh)

Standard

error

(Jackknife)

%

Incorrect

Std.

error

1 (srs)

"^(srs)

Design

effect

(

 t%L/t

tL(

srs

PART B: ROAD TRAFFIC SURVEYS

England

traffic

London

traffic

1975

South

Ontario

1976

South

Ontario

1978

Ontario

Western

Ontario

French

expressway

Verbal

(4)

Numeric

(7)

Numeric

(7)

Verbal

(5)

Verbal

(5)

Numeric

(11)

Verbal

(5)

Numeric

(11)

Numeric

(7)

Verbal

(4)

PARC C: RAILWAY S;

British

railway

Verbal

(4)

Numeric

(7)

Index

(11)

0.035

0.115

0.080

0.071

0.035

0.062

0.080

0.244

0.121

0.065

JRVEY

0.029

0.067

0.111

0.003

0.007

0.027

0.009

0.019

0.034

0.010

0.026

0.015

0.010

0.004

0.009

0.015

0.002

0.005

0.011

0.006

0.008

0.018

0.005

0.013

0.009

0.007

0.002

0.004

0.006

1.6

1.4

2.6

1.6

2.4

1.8

1.9

1.9

1.7

1.4

1.9

2.1

2.3

0.566

0.383

0.809

-0.698

0.956

1.012

0.567

0.103

1.016

1.037

0.540

0.340

0.810

1.140

0.170

0.150

0.420

0.870

0.110

0.078

0.479

0.719

0.742

0.510

0.340

0.240

0.359^

0.366

0.134

0.413^

0.082

0.016

0.237

0.451

0.048

0.031

0.490

0.226

0.184

1.5

0.9

6.1

2.8

2.1

9.6

1.8

1.9

2.3

2.5

1.0

1.5

1.3

a

 All data in this table come from a non-linear regression

two time periods. (See the adjusted energy irodel in the

analysis of annoyance on the noise in

text.)

-23-
TABLE III: FIFTEEN NOISE ENVIRONMENTS ASSUMED TO BE PRESENT IN

THE BASELINE STUDY DESIGN

Noise

environment

identification

number

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Daytime

noise

level

(dB(A),LEQ)

60

65

70

75

80

60

65

70

75

80

1 60

65

70

75

80

Difference

(Day-Night)

(dB(A),LEQ)

8

8

8

8

8

10

10

10

10

10

14

14

14

14

14

Nighttime

noise

level

(dB(A) ,LEQ)

52

57

62

67

72

50

55

60

65

70

46

51

56

61

66

-24-
TABLE IV: 95% CONFIDENCE INTERVALS FOR ESTIMATES OF THE NIGHTTIME

WEIGHT (wn) FOR SIX STUDY DESIGNS AND FOUR SAMPLE SIZES

Desk

Description

Baseline

Wider range

of noise

levels

Wider range

of differences

Assume small

nighttime

weight

(wn=5)

Assume large

nighttime

weight

(wn=20)

Assume large

nighttime

weight

(wn=100)

na

Noise levels [dB(A)LEQ]

Daytime

range

[

a

LB^]

60-80

17.1]

50-80

[10.8]

-B-

-B-

-B-

-BDay-night

levels

8,10,14

12.5]

-B5,10,20

[6.2]

-B-

-B-

-BCorrelation

r

dn

0.94

0.97

0.75

-B-

-B-

-BSample

size

1,000

2,000

4,000 •

10,000

1,000

2,000

4,000

10,000

1,000

2,000

4,000

10,000

1,000

2,000

4,000

10,000

1,000

2,000

4,000

10,000

1,000

2,000

4,000

10,000

Nighttime regression

coeffiient

(assumed)

0.909

0.909

0.909

0.833

0.9524

0.9901

Predicted accuracy

Standard

error

Bn

<<&n )

0.12

0.08

0.06

0.04

0.12

0.08

0.06

0.04

0.06

0.04

0.03

0.02

0.27

0.19

0.13

0.09

0.06

0.04

0.03

0.02

0.02

0.01

0.01

0.01

95% coi

for nit

Lower

limit

1

1

2

3

1

1

2

3

2

3

4

5

— W0

1

. 1

3

4

5

7

10

14

19

27

ifidence intervaJ

jjhttime weight

Nighttime

weight

(wn)

10

10

10

5

20

100

Upper

limit

+»

+ 00

+«

-l-oo

+ €»

4-o>

•(-co

+ 0°

4-09

+ 00

+ 00

63

+ °0

+ 00

+ 00

+ 00

+ to

+ 00

+ 0»

+ 00

+ 00

+«

+ 00

+ 00

3

 B - same as baseline condition

-25-
TABLE V: FIFTEEN NOISE ENVIRONMENTS ASSUMED TO BE PRESENT IN THE

NO-NIGHTTIME-NOISE BASELINE STUDY DESIGN

Noise

environment

identification

number

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Daytime

noise

level

(dB(A),LEQ)

60

65

70

75

80

60

65

70

75

80

60

65

70

75

80

Difference

(Day-Night)

(dB(A),LEQ)

8

65

8

75

8

60

10

70

10

80

14

65

14

75

14

Nighttime

noise

level

(dB(A),LEQ)

52

0

62

0

72

0

55

0

65

0

46

0

56

0

66

-26-
TABLE VI: 95% CONFIDENCE INTERVALS FOR ESTIMATES OF THE NIGHTTIME

WEIGHT (wn) FOR FOUR NO-NIGHTTIME-NOISE STUDY DESIGNS AND

FOUR SAMPLE SIZES

Desk

Description

Baseline

( No-nightnoise

environments)

Wider range

of day-night

differences

Small daynight

differences

Lower nighttime weight

(wn=8)

jn3

Noise levels [dB(A)LEQ]

Daytime

range

[

aLEQ^l

60-80

-B-

-B-

-BDay-night

levels

(LBCU-LEQ )

["LE^-LBQnl

8,10,14,

60,65,70

75,80

[30.0]

5,10,20 .

60,65,70

75,80

[29.7J

2,5,8

60,65,70

75,80

[32.8]

2,5,8

60,65,70

75,80

[32.8]

Correlation

rdn

-0.14

-0.14

-0.13

-0.13

Sample

size

1,000

2,000

4,000

10,000

1,000

2,000

4,000

10,000

1,000

2,000

4,000

10,000

1,000

2,000

4,000

10,000

Nighttime regression

coeffiient

(assumed)

0.909

0.909

0.909

0.889

Predicted accuracy

Standard

error

( TJn)

0.07

0.05

0.04

0.02

0.06

0.04

0.03

0.02

0.05

0.03

0.02

0.01

0.06

0.04

0.03

0.02

95% confidence interval

for nighttime weight

Lower

limit

2

2

3

4

2

3

4

5

3

4

5

6

2

3

3

4

Nighttime

weight .

(wn)

10

10

10

8

Upper

limit

+ 0>

+00

+«0

+ 00

+ 00

+ 00

+ 00

48

+ 00

+ 00

1050

28

+ 0.

+ 09

+ 00

24

-27-
1. Report No.

NASA CR-178062

2. Government Accession No. 3. Recipient's Catalog No.

4. Title and Subtitle

An Evaluation of Study Designs for Estimating a

Time-of-Day Noise Weighting

5. Report Date

March 1986

6. Performing Organization Code

7. Author(s)

James M. Fields

8. Performing Organization Report No.

10. Work Unit No.

9. Performing Organization Name and Address

The Bionetics Corporation

20 Research Drive

Hampton, VA 23666

11. Contract or Grant No.

NAS1-16978

12. Sponsoring Agency Name and Address

National Aeronautics and

Space Administration

Washington, D.C. 20546 .

Federal Aviation Administration

Office of Environment and Energ)

Washington. D.C. 20591

13. Type of Report and Period Covered

Contractor Report

14. Sponsoring Agency Code

505-35-13-53

15. Supplementary Notes

Langley Technical Monitor: Clemans A. Powell

16. Abstract

The relative importance of daytime and nighttime noise of the

same noise level is represented by a time-of-day weight in noise

annoyance models. The high correlations between daytime and nighttime noise have been regarded as a major reason that previous

social surveys of noise annoyance could not accurately estimate

the value of the time-of-day weight. This report describes study

designs which would reduce the correlation between daytime and

nighttime noise. It is concluded that designs based on short-term

variations in nighttime noise levels would not be able to provide

valid measures of response to nighttime noise. The accuracy of

the estimate of the time-of-day weight is predicted for designs

which are based on long-term variations in nighttime noise levels.

For these designs it is predicted that it is not possible to form

satisfactorily precise estimates of the time-of-day weighting.

17. Key Words (Suggested by Author(s))

Noise

Annoyance

18. Distribution Statement

Unclassified - Unlimited

Subject Category 71

19. Security Qassif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

29

22. Price

A03

N-305 For sale by the National Technical Infoimation Service, Springfield. Virginia 22161