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Vau of * NBS TECHNICAL NOTE 921

U.S. DEPARTMENT OF COMMERCE/ National Bureau of Standards

Survey Manual for

Estimating the Incidence of

Lead Paint in Housing
NATIONAL BUREAU OF STANDARDS

The National Bureau of Standards 1 was established by an act of Congress March 3, 1901. The Bureau's overall goal is to strengthen and advance the Nation's science and technology

and facilitate their effective application for public benefit. To this end, the Bureau conducts

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the Institute for Computer Sciences and Technology, and the Office for Information Programs.

THE INSTITUTE FOR BASIC STANDARDS provides the central basis within the United

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1 Headquarters and Laboratories at Gaithersburg, Maryland, unless otherwise noted; mailing address Washington, D.C. 20234.

- Located at Boulder, Colorado 80302.
rrfS

~b

i iV7s Suftey Manual for Estimating Ac

^ - the Incidence of Lead Paint in Housing

William G. Hall

Institute for Basic Standards

National Bureau of Standards

Washington, D.C. 20234

and

Lillian T. Slovic

Institute for Applied Technology

National Bureau of Standards

Washington, D.C. 20234

Prepared for:

Office of Policy Development and Research The Department of Housing and Urban Development

Washington, D.C. 20410

*< °\C%

U.S. DEPARTMENT OF COMMERCE, Elliot L. Richardson, Secretary

Edward O. Vetter, Under Secretary

Dr. Betsy Ancker-Johnson, Assistant Secretary for Science and Technology

\J C NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Acting Director

Issued September 1976.
National Bureau of Standards Technical Note 921

Nat. Bur. Stand. (U.S.), Tech. Note 921, 111 pages (Sept. 1976) CODEN: NBTNAE

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1976

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D. C. 20402

(Order by SD Catalog No. C13.46:921). Stock No. 003-003-01660-8 Price: $2.20.

(Add 25 percent additional for other than U.S. mailing).
TABLE OF CONTENTS

Page

1. List of Tables v

2. List of Figures vi

Acknowledgement vii

Abstract 1

Introduction 2

Part I - Preliminary Considerations 5

o Overview of the Survey Process 6

o Cost Determinants 7

o Establishment of Objectives 9

o Required Resources 10

- Supervisors 10

- Data Base Manager 11

- Inspection Staff . . 11

- Statistical Consultant 12

- Clerk-Telephonist 12

- Equipment 12

Part II - Preparing for and Performing the Survey 14

o Selecting the Sample 15

- Random Sampling and the Binomial Distribution ... 16

- Sample Accuracy > 17

- Sample Size 17

- Non-Sampling Errors 22

o Proper Timing for the Survey 25

o Characteristics to Look for in Hiring Inspectors. . . 26

o Supervisor Training 27

iii
Page

o Inspector Training 30

o The Survey Questionnaire 33

o Publicity Campaign . 34

o Pretest 37

o Record Keeping 37

o Notification of Residents 38

o Supervisor Follow-up 42

Part III - Data Reduction and Analysis 43

o Data Base Organization 44

o Construction and Maintenance of the Date Base* .... 45

o Estimating the Incidence of Lead Paint on Surfaces . . .50

Recommended Reading for Further Information on Surveys

and Statistics 53

Appendix A - Determination of Sample Size 54

Appendix B - Estimating Incidence of Lead Paint 73

Appendix C - Instructions for Completing the Sample

Survey Form 76

Appendix D - Specifications for Sample Generation

Program 84

Program Listing

Specimen of Output

IV
LIST OF TABLES

Page

1. Relation Between Sample Size and Precision - 95

Percent Confidence Intervals 19

2. Relation Between Sample Size and Precision - 90

Percent Confidence Intervals 20

3. Relation of Sample Size (n) to Hazard Fraction (p)

for Various Population Sizes (N) 57

4. Percentage of Normal Distribution Lying Within

Multiples of a 58
LIST OF FIGURES

Page

1. Inspection Questionnaire Form 28

2. Sample Introductory Letter .36

3. Call-Back Card . . . 41

VI
ACKNOWLEDGEMENT

The authors express their thanks to Harvey W. Berger of the Office

of Housing and Building Technology at the National Bureau of Standards

for his assistance in preparing this manual.

Special thanks go to Janet Bonk of the Allegheny County Health

Department's lead paint survey project, for her many suggestions on

survey administration.

VI

1

Survey Manual for Estimating the Incidence of Lead Paint in Housing

William G. Hall $ Lillian T, Slovic

ABSTRACT

This Manual is intended as a guide for municipal managers in performing a survey to determine the prevalence of lead based paint in

their community's dwelling units. There are four parts to the Manual,

each is intended for a different audience.

Part I discusses the preliminary considerations for a survey. It

is intended for the department head or executive who will initiate plans

for the survey. It presents a managerial overview of the processes, the

cost determinants, criteria for the establishment of objectives and the

resources required.

Part II is intended for the survey manager and the inspector supervisors. It contains more detailed information on the planning, staffing,

training, and execution of the data collection phase of the survey.

Part III is for the use of the person responsible for the control

and management of the data collected and for the analysis of these data.

The Appendices contain quite detailed information about procedures

we have used in previous surveys. These may be used as they are described

or may be modified or adapted to meet specific objectives.

Key Words: Lead paint; lead paint detection; lead paint programs; lead

poisoning; portable x-ray fluorescence; random sampling; x-ray fluorescence.
INTRODUCTION

Lead poisoning, especially of children, is a serious national health

problem. The sources of lead to which children are exposed include: air

(industrial and automobile exhausts); water (industrial waste, minerals,

and leaded pipes) ; food (packaging or storage containers ; the food

product itself) , or dust (particulate fallout) . Another source of lead

intake in children is the ingestion of lead based paint.

Before the 1940's, paints with as much as 50 percent lead were widely

used for residential applications. According to the 1970 Housing Census 1/,

approximately 1.8 million houses, which were built before 1940, are in a

dilapidated condition. Dilapidation denotes chipping, flaking and peeling

paint as well as decaying walls and wood surfaces. Under these conditions

lead paint if present is readily available for inhalation and ingestion

and constitutes a potential health hazard.

Accurate diagnosis and careful treatment of lead poisoned children

will not eliminate the problem if they are returned to the same environment to be reexposed to the hazard. The rate of recurrence of lead

poisoning is high among such children. Reexposure of survivors of

acute poisoning, to an unabated lead source, results in permanent

damage almost 100 percent of the time 2/

1/ Census Bureau, U. S. Department of Commerce, Plumbing Facilities

and Estimates of Dilapidated Housing , HC (6) , 1973

2/ Jane S. Lin-Fu, "Childhood Lead Poisoning- -An Eradicable Disease"

Children': An Interdisciplinary Journal for the Professions Serving

Children, Vol. 17, No. 1, January - February 1970.
The only sure preventative for the ingestion of lead paint, on housing

surfaces, by children, is elimination of the hazard, either by removing

it or covering it up with barrier type building materials. This is an

expensive procedure --costing from hundreds up to several thousands of

dollars per dwelling- -but perhaps not so expensive for society as the

care of youngsters who have suffered permanent physical or brain

damage from lead paint poisoning.

If a serious lead paint poisoning problem has been identified in

your community through discovery of a high lead poisoning incidence and/or

by a child screening program, you may be looking for a strategy to combat

it, either as an independent lead poisoning control program or as part of a

broader housing improvement effort such as a code enforcement program.

Either way, lead paint hazard abatement will affect your overall housing

program in terms of funding and staff commitments. You may find that the

addition of lead hazard elimination as part of your housing improvement

program makes a raze-rebuild strategy more cost effective than a rehabilitation strategy, especially if the latter seemed only marginally feasible

on economic grounds. Whatever course of action is anticipated, the first

step is to estimate the number of contaminated housing units, the extent

of contamination, and housing characteristics which may be associated with

lead paint hazards (location, age, occupancy class, type, etc.). The

sample survey techniques described in this manual can help you accomplish

this much more conveniently and with less expense than the alternative- -a

unit by unit census.

Although this Manual is not intended to be used as a general guide

for household surveys, some of the material is applicable to such acti3
vities. The text includes description of ways in which this survey

differs from the usual opinion or household surveys; the focus on

dwellings rather than people, the collection of physical measurements

rather than interviews are distinguishing characteristics of this survey.

A detailed procedure for drawing a sample is presented. Although it is

by no means the only procedure which can be used it was designed to meet

the specific objectives of estimating the hazard in a number of housing

categories with a high statistical confidence level, with moderate

expenditure of resources.
PART I

PRELIMINARY CONSIDERATIONS
OVERVIEW OF THE SURVEY PROCESS

There are many possible motivations for municipal officials to

consider performing a lead paint survey in housing. The survey may be

health- oriented or code enforcement oriented; it may be part of a large

neighborhood or civic improvement program or an independent lead abatement program; it may be focused on the entire city or some particular

target group or groups for which the city has special legal or social

responsibilities. The findings of the survey may be required either

within an agency or at a higher echelon of government; they may be

used to allocate existing resources more efficiently in an operational

sense, to permit cost effective long range planning for a lead program,

or to estimate the benefits and costs of a lead abatement program relative

to the benefits and costs of alternative action programs. Regardless of

the conditions leading to the use of a sample survey, the following tasks

should be accomplished sequentially to assure that the survey will be

efficient and adequate.

1) Determine the objectives. . .Why is the information needed? What

decisions will be based on the information gathered?

2) Define the housing units to be studied. . .Are all housing units

to be included? Only owner -occupied units? Only rental units?

3) Determine the data to be collected and verify that they do not

exist elsewhere. . .What data are needed? Can they be gathered

by using data from preceding or current studies?

4) Choose the sample unit ... Should it be the building? The dwelling

unit? What size sample is needed? What sampling method is to

be used? What degree of uncertainty can be tolerated?

6
5) Design and test the survey questionnaire or data collection

form. ..Is it unambiguous? Is it clear? Does it minimize

discretionary entries? Is it convenient for inspectors? Is it

convenient for editing, transcription, and keypunching? Should

abbreviations and/or conventions appear on the form or in a

companion manual?

6) Determine the method of resident contact ... Should appointments

be made for inspectors or should they make unannounced calls?

How should publicity be handled?

7) Acquire and train staff

8) Carry out the data collection

9) Edit the data for consistency and completeness.

10) Deal with nonresponses (units for which inspection cannot be

performed.)

11) Analyze the data

12) Report the findings, including recommendations

Although these steps are in sequence, it should be understood that

they are not independent.

Many skills and talents are required and they must be well coordinated if the survey is to be successful. A useful procedure is to have

all who are expected to participate in the survey, or use the data collected

engage in a series of give-and-take discussions during the planning stages.

"What if" questions should be encouraged. These discussions should result

in a useful, practical and economical survey design.

COST DETERMINANTS

The most critical determinant of survey cost is the size (number of

7
dwelling units to be inspected) of the sample. The sample siz % is also

the most important factor in the accuracy o,f the survey. There is therefore a direct trade-off of cost vs. accuracy. The accuracy requirements

must be understood by all involved; insistence on more accuracy than that

required will result in unnecessary expense; less accuracy than is

required will result in an inadequate or even useless survey.

The following is a list of functions which may be considered as

distinct elements for costing:

1. Planning and design

2

.

Management

3. Development of population list

4. Selection of sample

5. Development of data collection form.

6. Acquisition of lead measurement devices

7

.

Pretest

8. Printing

a) Data collection forms

b) Training materials

c) Manuals

d) Contact letters

e) Call back forms

9) Inspection

a) Recruitment

b) Training

c) Dwelling inspection

d) Inspector supervision

e) Inter-unit travel

8
10) Development of editing, coding and analysis

procedures (manual or computerized)

11) Editing, coding, and keypunching

12) Telephone

13) Mailing

14) Analysis of data including programming and computer costs.

15) Report writing

16) Reproduction, dissemination, and presentation of report

As with the steps of the survey process, these functions are

distinct but interdependent. The size of the sample will influence the

methods by which the functions are to be accomplished. The sample size

controls the scale of the entire operation. If the sample size is small,

for example, editing will be manual rather than computerized, documents

can be reproduced by office equipment rather than printing, a smaller

organization is required.

ESTABLISHMENT OF OBJECTIVES

The first, most important, and most difficult step of a survey is

the establishment of objectives. The objectives must be defined in terms

of the purpose or purposes for which the collected data are to be used.

The survey should provide a sound basis for decision making. The decision

could be: the selection of a course of action from alternative proposals;

defining a course of action or, allocation of resources. It cannot

be overemphasized that the establishment of objectives is a policy

function and one that should be performed at a high policy making level.

Although the services of a survey statistician are required in the process,

his role is to quantify and interpret, and to estimate costs. His job

9
is not to establish objectives. The interaction between the statistician

and the policy maker must be collaborative.

REQUIRED RESOURCES

General

The survey can be carried out completely with "in-house" staff.

However there are some tasks, within the survey, which could be contracted

to individuals, firms, or other parts of your agency. These opportunities

are identified below.

Supervisors

Choosing supervisors is extremely important for their performance

is crucial to the success of the project. They are responsible for

hiring and training the inspectors, maintaining the work flow, scheduling

inspection team assignments, checking the equipment, reviewing forms,

and following departmental policies and procedures.

Ideally one would like the supervisors to have experience in survey

supervision and to be knowledgeable in housing and public health matters.

The former, however, is the more critical. These people may have to be

recruited from outside the department, or the most nearly qualified candidates may have to be trained in one or more of the critical areas.

Some projects have hired university graduate students in the appropriate fields for these supervisory positions. However, in our experience

this has not been a satisfactory approach. The students often have little

work background, even less supervisory experience and have difficulty

maintaining authority over people of their own age and educational background.

The capability of people that you hire for these positions will, of

10
course, depend on the candidates available. Since these are very important positions for the survey, every effort should be made to obtain the

most qualified candidates.

One supervisor has been found to be appropriate for each group of six

inspection teams, that is, one supervisor for twelve inspectors.

Data Base Manager

The data base manager (computer analyst) is responsible for programing, designing and operating the system which stores the data for

future analysis. This person might be detailed from a budgeting

department or some other department of your agency for the duration of

the project rather than hired from the outside.

This is one of the functions which could be performed by an outside

contractor, such as a reliable data analysis group either inside or outside of government. The same contract could also include the services

of a survey statistician to design and perform the data analysis.

Inspection Staff

The number of inspectors needed can be determined from the number

of houses to be inspected. For instance, if 5-6 completed inspections

per day per two -man team are assumed (in our experience this is a

reasonable estimate), then 165-200 team days are needed to complete 1000

inspections. If the inspectors are hired for a three month period, they

will be able to work 60-65 days. Thus, 3 teams would be needed to perform

1000 inspections during those three months. These estimates are based

on a five day-forty hour work week, but the inspection teams will in

fact be required to perform some inspections during evenings and weekends

at the convenience of the occupant.

11
Statistical Consultant

A survey statistician will be needed at various times during the

planning and execution of the survey. During the planning stage, he

should be involved in the establishment of objectives; in discussing all

of the questions concerning the sample and in the design and testing of

the questionnaire. The statistician should, with the data base manager

or the data analysis group, design the analysis procedures to be used.

If possible the statistician should be continuously available on a

consultant basis, both during the survey, to help in solving whatever

problems arise, and during the analysis phase to help interpret the

results. Possible sources for this consultant include your agency, a

nearby university or a commercial firm.

Clerk/Telephonist

Usually a department will have a clerical worker who can become part

of the lead survey group. In addition to maintaining record files, that

person also can be in charge of mailing, maintaining mailing records, and

arranging inspection appointments. Just as the inspectors may have to work

Saturdays and evenings, so might the telephone scheduler. If all the adults

in a household work, the only time they can be reached for an appointment is

at night or on the weekend.

Equipment

Portable X-ray fluorescence (XRF) lead detectors are the most commonly

used and acceptable means for making large numbers of measurements of

lead paint in housing. They are non-destructive, and an inspector can

perform a measurement every 10-20 seconds. A digital readout displays

the measurement in terms of milligrams of lead per square centimeter of

surface area (mg/cm ) . These devices are limited in accuracy and durability.

12
They will measure the lead content of surfaces with good reliability (with an

error of less than 10%) above a level of 2 mg/cm . Below that level, readings

are suspect but can be confirmed with multiple readings at the same location.

New XRF devices of increased accuracy and improved general performance

are under commercial development. If present generation instruments are

used for the survey, it would be advisable to have a repair service

agreement with the manufacturer. Each survey team will need a detector.

In addition, one or two spare detectors should be kept on hand to substitute for those returned to the manufacturer for repair.

There are other methods that can be used for the detection of

lead paint in houses. Chemicals such as sodium sulfide will react

with lead compounds to form dark colors which are indicative of the

presence of lead. Chemical spot tests, however, are not quantitative

and can only give a rough indication of lead's presence. Under some

circumstances they will give false negative reactions to lead, that is,

indicate that no lead is present when in fact it is present.

Lead in paint can be accurately analyzed in the laboratory using

sophisticated chemical techniques and instruments. Such procedures,

however, require that paint chips be scraped or peeled from a number

of surfaces, catalogued and transmitted to a laboratory for analysis.

These alternatives to the use of XRF detectors, in addition to being very

time consuming and expensive, are likely to be unacceptable to most

dwelling occupants.

13
PART II

PREPARING FOR AND PERFORMING THE SURVEY

14
SELECTING THE SAMPLE

Simple random sampling from a city housing director 1/ has been

found to be an efficient means of choosing residences to survey. It is

the method least likely to be affected by bias. Every unit has an equal

likelihood of being selected, with prior selections having no bearing

on subsequent choices.

Housing directories are available for many United States cities in

the population range of 10,000 to 1,000,000 and can be found at libraries

and real estate offices. They are updated every two or three years. In a

lead based paint survey, where the emphasis is on the older housing stock,

the omission of new housing units built after the most recent directory

publication can be overcome by including a correction for population size

in the analysis or by using a better population size estimate from a more

up-to-date source.

Most directories are divided into two sections: the first is an

alphabetical listing of all city residents and their addresses; the second,

an alphabetical listing of streets with the street numbers listed numerically.

In conducting the survey, it is easier to go from street to street than

from name to name, and so the second directory section is more useful.

Errors do exist in these lists, but our experience has been that there

are not enough of them to invalidate the analysis. Someone with a fair

knowledge of the city should make a cursory check of the directory, however,

to assure that no major publication errors exist, such as the omission of

an entire section of the city. In any case it is ultimately the responsibility of the survey statistician to reconcile the population data source

to the survey requirements.

1/Several procedures from drawing samples are described in Appendix A.

15
Random Sampling and the Binomial Distribution

A door to door, unit by unit census of all dwelling units in a city,

to determine the extent and distribution of the community's lead paint

hazard is an extremely costly and time consuming process which most jurisdictions will want to avoid. Random sampling is faster and more economical

and can yield very accurate results.

Random sampling involves choosing a fraction of all the dwelling units

from some population in such a manner that each unit has an equal chance of

being selected. By applying the mathematics of probability to the sample

findings, the relevant characteristics (lead paint content in this case)

for the totality of dwellings can be estimated. In a lead paint survey,

it may be desirable to deal with a number of subpopulations . The incidence

of lead paint may differ according to some physical characteristic of the

unit (age, for example) or the objectives of the survey may require that

incidence of lead paint be estimated separately for different tenancy

groups (public, subsidized, owner-occupied, etc.).

You, the planners and administrators of the lead paint survey, should

have an understanding of the sampling process even if you plan to have

a statistician handle this portion.

A basic statistical concept which underlies the remainder of this

section (and Appendix A) is that of a binomial distribution . If a characteristic of interest is defined in terms of yes -no, true -false, or hazardous -

non- hazardous , the characteristic is binomially distributed. The binomial

distribution accurately reflects the ways in which laws or codes are

usually written and enforced. A typical code might require that a surface

with more than 2.0 mg/cm^ of lead be deleaded according to some acceptable

16
procedures but would not require distinct action for each lead level,

such as 2.5, 5.0, or 10.00 mg/cm^. The binomial distribution does not

accommodate the notion of degrees or gradation of hazard, but can only

distinguish between hazardous and non-hazardous levels.

Sample Accuracy

'There are many ways in which inaccuracies may be introduced into the

estimates resulting from a survey. Errors are of two general types: those

which arise because of the use of a sample to present an entire population

and those which arise from the data collection and/or analysis procedures

and should be expected to occur even if a complete census were taken. Errors

of both types will occur; they cannot be completely eliminated. They must be

controlled to the extent that the error is either negligible or predictable.

Errors introduced by the use of a sample can be estimated and can be

controlled to produce any accuracy required by use of the methods of

statistics. The effect of errors introduced from sources other than the

sampling procedure is not as easy to predict; if however, the number of

such errors is kept small, their effect will be negligible. The term

"accuracy" refers to all errors; the term "precision" refers only to

sampling errors (the more precise, the smaller the error).

Sample Size

If the size of the sample (hereafter denoted by n) is close to the

number of dwelling units (called the total population and denoted by N)

,

precision, as one would expect, is very good. However, in almost all

practical situations the sample may be quite small relative to the total

population. The sample size is the dominant factor in the statistical

precision of the sample; the size of the total population does not

17
significantly affect the precision!/. The Gallup and

example, interview only about 1500 persons to represent nationwide opinion.

Maximum efficiency, in terms of precision per sample unit, can be

attained by the use of a simple random sample 2/. The basic principle

that produces accurate estimates from small samples is that each unit of the

total population must be equally likely to appear in the sample.

There is some element of risk involved in accepting the findings

from any sample as representative of the total population. Tables 1

and 2 illustrate the magnitude of this risk. If a very large number of

samples of the indicated size were drawn from an infinite population, 95

percent (Table 1) of these samples would contain the true percent of

hazardous units of the total population within the given confidence intervals.

For any given sample, there is a five percent risk that the true value is

not contained within the given confidence intervals. For example, if the

percentage of hazardous dwellings in the sample is 10 percent, a sample

size of 100 insures that there are 95 chances out of 100 that the percentage of hazardous dwellings for the total population lies between

4.1 and 15.9 (Table 1), whereas there are 90 chances out of 100 that the

true percentage of hazardous dwellings lies between 5.0 and 15.0 (Table 2).

Note that an increase in sample size (for a given percentage of

hazardous dwellings) always improves the precision, but the improvement

1/ This assumes the total population is "large" relative to the sample.

See Appendix A for procedures appropriate for 'small populations.

2/ Other sampling schemes beyond the scope of this manual may do as well

in terms of precision per sample unit and better in terms of precision

per dollar spent. Cluster sampling for example, will lower the unit cost

of inspection, but will require a larger sample to achieve the same precision. Stratified samples may be considered as combinations of simple

random samples. These more sophisticated techniques may be appropriate

for particular objectives.

18
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is only modest in going from a reasonably large sample to a still larger

one. For example, in Table 1, for 50 percent hazardous units, doubling

the sample size from 50 to 100 narrows the confidence interval by 8.2

percentage points wheras doubling the sample size from 500 to 1000 narrows

the confidence interval by only 2.4 percentage points. Note that the case

in which 50 percent of the units are hazardous yields larger confidence

intervals than any other case for a given sample size. Thus it represents the

worst case for a given sample size. Note also that the magnitude of the

confidence interval when m percent of the units are hazardous is equal to

that for (100-m) percent, and becomes smaller as m approaches zero or 100.

There is no "right" confidence level. The particular level to

aim for - 951, 901, or some other level - is a policy decision which

must be based on the following factors:

1. The cost of performing the survey at various sample sizes.

2. The confidence interval which is required. This is a threshold

determination problem; presumably a different course of action

will be undertaken depending on whether the percentage of hazardous units is greater or less than ml . How precisely must m be

known in order to make a good decision? What are the consequences

if m is off by 5% or by 10%, etc.?

3. The risk involved for example, in adopting a course of action

based on a 901 confidence level rather than on a 95% or 991 one.

If alternative courses of action differ greatly in cost (whether in

dollars, time, difficulty, or whatever), the acceptable risk should

be small.

21
Non-Sampling Errors

Non- sampling errors may be either conceptual or mechanical. There

are no statistical formulae which can predict how seriously these errors

will affect the survey results. Such errors cannot be totally avoided or

controlled, and great care at every stage of the survey is essential to

minimize them. This care includes, of course, conscientious performance

of all tasks; it also requires the development and implementation of procedures

at extra cost, for assuring that the number of errors is small. Your

consulting statistician can furnish valuable advice based on your specific

objectives and circumstances. Here are some of the non -sampling errors

which are likely to arise in a lead paint hazard survey:

1) Non-random sampling

Any factor which introduces an unknown bias into the sample can

introduce errors into the findings. For example, use of a telephone

directory for the selection of households would preclude selection of

households with no telephone and those with a unlisted telephone number,

and would offer a multiple chance of selection of households with more

than one telephone.

2) Inaccuracy of listing

If the list from which the sample is drawn is incomplete or

seriously out of date, errors will be introduced into the findings. For

example, a city directory cannot be used to select a sample to

represent a metropolitan (city and suburbs) area.

3) Survey questionnaire ambiguities

The data collection procedure must be completely reproducible: that

is to say, any team of inspectors should produce equivalent data for the

22
same housing unit. This requires a good form design and well-trained

inspectors

.

A common procedure in many household surveys is to re-interview a

sample of the respondents. Re- inspection is not recommended for a lead paint

survey for the following reasons. First, the burden on the occupant (time)

and the intrusion into his privacy (each room of the dwelling must be

entered and inspected) is greater than in the usual survey. Second, to

include enough information on the data collection form to uniquely identify

each surface and room of the dwelling (for re-measurement) requires a significant increase in the volume of data collected, and these extra data have no

intrinsic value. A post- inspection interview, performed by the supervisors,

can and should be done for some fraction of the respondents. This can be

either a doorway or telephone interview. This should include verifying

all of the questionnaire information supplied by the respondent. Also

it should • include questions such as:

When did the inspectors arrive?

How long did the inspection take?

Were all rooms of the unit inspected?

The inspectors will be aware of the post- inspection interview; thus

it has value as a supervisory device as well as checking on the consistency

of the respondents.

4) Detection instrument errors

This is one of the errors peculiar to a lead paint survey. The

detection devices are simply not as accurate and reliable as one would

like. This source of error cannot be eliminated, but the error can be

minimized by use of proper calibration procedures and good inspector

training. 23
5) Inspector performance

Most of the data gathered in the survey are instrument readings; the

remainder are simple "yes -no" or answers to multiple choice questions, so

there is somewhat less chance for the inspector to "lead" the respondent into

the "right" answer. The consistency and reliability of the inspectors in

making all of the appropriate and necessary measurements of painted

surfaces, in the inspected dwellings, is of major importance in minimizing

survey errors. In addition to diligence, they must be accurate in

recording lead measurements and other pertinent information on the survey

questionnaire.

6) Respondent accuracy

This is related to inspector performance and questionnaire ambiguities.

This factor can be a serious problem in a general household survey but is

less likely to be serious in a lead paint survey.

7) Mechanical and clerical errors

These errors will occur; they include transcription and coding errors

(including omissions), key punch errors, illegibility, loss, destruction or

mutilation of data collection forms and arithmetic errors in manual tabulation.

Although they will presumably be random and therefore will tend to cancel

each other, all reasonable precautions to avoid these errors should be

taken. Such precautions should include keypunching directly from the

data collection form (no transcription errors can occur if transcription

is avoided), verifying all card punching, close monitoring of all forms

by supervisors, etc.

8) Non-existent unit

A dwelling unit selected into the sample may not exist. This could

24
be due to an address error, demolition of a unit, conversion to nonresidential use, etc.

9) Non- responses

Inevitably, entry to some dwellings will be impossible. The unit

may be vacant, the occupant may not be at home or the occupant may refuse

entry to the inspectors. Every effort should be made to keep non-responses

to a minimum, but for each non-response, those data which can be obtained by

observation should be collected. These data are useful in detecting any

systematic bias induced by non-responses. The actions required differ

according to the cause of the non-response. For non-existent, vacant,

or non-residence units, nothing can be done; for not-at-homes, efforts

should be made to reschedule; for refusals, rescheduling should be

attempted and the occupant's reason for refusal should be determined --

this information may be useful in retraining, pairing of inspection

teams, etc.

PROPER TIMING FOR THE SURVEY

After you have established the objectives, the number of units to

be inspected, and the data to be collected for each unit, planning for

the operational part of the survey can begin.

Our experience in surveys for lead paint has shown that the

project is most efficiently performed during the summer. There are

no slushy or icy streets and no heavy coats and boots to encumber the

inspectors, and most importantly, householders are more likely to permit

inspection in good weather. In some temperate climates, the season may

be of little importance in choosing the period for the survey.

College students who plan to return to school in the fall tend to work

25
faster than other inspectors who know that the sooner the project ends,

the sooner they will be unemployed. During one survey, for example,

student teams completed an average of 5.5 units inspections a day while

other teams averaged only 4 units.

There are, of course, administrative and managerial problems

involved in such a concentrated effort. The administrator may be

running several other programs and have only a limited amount of

time to spend on the lead paint survey. Money may be tightly budgeted.

But it is most efficient, in the long run, to hire as many inspectors

as is necessary to complete the survey during the summer.

CHARACTERISTICS TO LOOK FOR IN HIRING INSPECTORS

Experience in previous surveys has shown that the teams most

successful in being accepted by housing occupants, were made up of a male

and a female; or a black and a white. You should, therefore, consider

these public preferences in hiring.

While enthusiasm and courtesy are extremely important, a certain

adaptability and resilience are indispensable in dealing with the public.

Inspectors should not overreact for example, if an occupant answers the

door clad only in a wristwatch, as has happened.

One requirement for employment may have to be ownership of a car.

An inspection team must have a car to get to its daily assignments. If

the sponsoring agency cannot provide each team with a vehicle, then the

inspectors will have to use their personal cars and be reimbursed

for expenses as agreed. The inspectors should have a general knowledge

of the city and be able to get around in it.

The prospective inspectors should also understand, when they are

26
hired, that they may be required to work irregular shifts and/or Saturdays.

Since a great many occupants have full-time jobs, they are not available

between 9 AM and 5 PM, to open their homes for inspection.

Also remember, in hiring, that the lead detection equipment weighs

approximately twenty pounds. Your inspectors will have to be able to

carry this weight a good part of the day.

SUPERVISOR TRAINING

While the publicity campaign (page 34) is still underway and approximately ten to fifteen days before the inspectors come on board, the

supervisors should begin training for the survey. If they are not part

of the department already, and are not familiar with the lead paint problem,

they must be completely familiarized with the project, besides learning

all the particulars of running the survey.

Since the supervisors will be leading the training sessions for the

inspectors they will have to learn how to operate the XRF lead detector,

how to complete the survey forms, and generally how to conduct the inspection.

This includes learning all about the various housing materials and conditions,

so that they can instruct the inspectors in filling out the survey form

(Figure 1) and check the completed forms for accuracy. (This is why people

with experience in housing were suggested for this job.)

The pretest (a mini- survey held in advance to check for problems in

the survey plan) would most conveniently be held at this time. It's an

important testing ground for all the tasks that the supervisors must learn and

later teach the inspectors. During this period the supervisors gain firsthand experience of the actual survey tasks as well as insight into the

human problems that are likely to be encountered during the survey.

27
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28
During this pre -survey period, the supervisors can help the project

administrators make certain decisions- -for instance, whether or not automobiles

used during the survey should be identified. This may seem trivial but could

make a difference in participation. In one instance, a rat elimination

program was being conducted simultaneously with a lead paint survey.

Many people refused entry to the lead inspectors for fear their neighbors

would think the department car was there because of rats. Obvious

identification would also help to discourage any sort of impersonation

for criminal purposes.

Another question is whether it is best to saturate a neighborhood

with all the teams at once or assign just one group to a neighborhood.

One team can become thoroughly familiar with an area and the residents

can become comfortable with their presence. On the other hand, the

area saturation approach allows closer supervision.

Inspectors have to be properly identified. Photographs for identification badges should be made of them as soon as they are hired so that

everything will be ready by the first inspection. Residents may deny

entry to anyone who is not properly identified.

The local police should be informed that the survey is being performed

and told what sort of identification is used by the inspectors.

The project administrator and supervisors have other administrative

details to discuss as well, though final decisions need not be made

until further into the project. For instance, when the inspections

begin, the supervisors will want to meet with all the inspectors

daily. These meetings have two purposes: first is the training value of

sharing inspection experiences, finding gaps or ambiguities in training

29
or questionnaires, etc. The second is to permit the supervisors to collect

and spot -check questionnaires and to make new assignments. After the learning

value diminishes, these meetings could be reduced to once a week and each

inspection team could operate more autonomously. The XRF's can be taken

home by the inspectors for calibration and recharging and considerable

time can be saved. Such short cuts and efficiencies will depend on the

policy of the agency and on the personal motivation of the inspectors

,

which can only be determined after the project is underway.

INSPECTOR TRAINING

Approximately one week before the survey is to begin, the inspectors

should be brought on board. Normally, training should be a relatively

simple operation, requiring only about two days (4 half-day sessions)

.

It can, of course, take longer if detailed departmental policy must be

presented or additional "dry-runs" are required.

The first morning of the two -day training should be spent in introducing the lead paint poisoning problem to the inspectors and explaining

to them how the housing survey will help in dealing with it. This is an

extremely important portion of the training. The inspectors must realize that

they are participating in a meaningful activity so that they will give

their best efforts to it. Their enthusiam or lack of enthusiasm for the

program will be perceived by occupants, who will react accordingly. The

inspectors must also gain enough knowledge Of lead poisoning, sampling,

and XRF characteristics to answer the expected questions from the householder

with accuracy and confidence. Also during this first session, the sampling

technique should be explained to the group. They should know how each

dwelling unit was chosen and the importance of inspecting each and every one.

30
The lead detection equipment should be described at the next session.

Instruction should be given on its operation, the meaning of its readings

and especially how the instruments are calibrated. The importance of

recording the readings from the calibration dials should be stressed. It

shouldybe emphasized that the lead detection equipment is delicate and

should be handled carefully, not jostled around or thrown in car trunks.

Instruction in filling out the inspection form (see Appendix B)

will require most of the third training session. Distinguishing

various room conditions as described under Item XI of the Questionnaire

needs major emphasis. Photographs or color slides which demonstrate these

conditions would be most helpful. Instructions in how to move about the

dwelling unit, and how to record the readings can also be given. The

possibility of "read through" (detecting lead on another surface behind

the one being read) should be explained. The inspector's responsibility

for equipment, procedures, schedules, and the need for courtesy and conscientiousness should be emphasized at this point.

To close out the training session, each team should carry out an

actual inspection in at least one test dwelling unit. Supervisors should

take turns acting as an occupant who is as obstinate and uncooperative as

possible. This will give the inspectors experience in dealing with

difficult people.

From experience, we have found that several practice inspection

sessions for each team improves performance. Emphasis on proper classifications for wall materials, conditions, and type of home construction

will result in more consistent and accurate completion of the forms.

The final session includes instructions in the proper approach to

31
use in meeting occupants. The importance of this is obvious. If the

resident is offended or irritated by the way the inspectors present themselves, they may be refused entry.

The survey team should present itself in a courteous manner. One

team member should immediately set up the lead detection equipment while

the second begins completing the form by asking the occupant for the

necessary information. The inspectors should make every effort to have

the occupant accompany them during the inspection. As the inspectors

proceed through the dwelling they should answer any questions posed by

the occupant courteously, truthfully, and briefly. A dwelling unit

inspection should generally require thirty minutes or less. The team

should keep this in mind and move through the task quickly.

Inspectors should check the exteriors of vacant units or units

whose residents have refused entry. They should complete the form for

those characteristics which are observable from outside the unit. This

information can be used to determine whether or not there is a pattern

to the visible characteristics of units to which entry has been refused.

For example, if the refusals have come primarily from residents of units

built before 1940, that would clearly bias the survey results.

After the first day of field work, the survey teams can be assembled

for a general group discussion on experiences and problems encountered.

At this time the forms completed by each team can be examined for

consistency. Immediate feedback to the inspectors is iinportant so that

errors can be corrected while the day's activities are still fresh in

their minds. This session should be used to identify frequently occuring

questions or attitudes of the residents, and the abilities of the inspectors

32
to cope with them. It should help in finding gaps in the training program

and in establishing standard answers to common questions.

Supervisors should randomly select one of each team's completed

forms, on a daily basis, and check it for completeness and consistency.

This procedure results in correctly completed forms in almost all cases.

If new inspectors must be hired during the survey, very little

formal training is required. The background and organizational

orientation (first session of the full program) should be repeated. Each

new inspector should then be paired with one of the experienced inspectors.

THE SURVEY QUESTIONNAIRE

The lead paint survey questionnaire developed by NBS is shown in

Figure 1. Detailed instructions for its use are given in Appendix B.

You may choose to collect data that differ from those appearing on

the sample questionnaire. In any case, it is important to exercise foresight in designing the data collection form to be used in the survey.

Redundancy should be designed into the survey questionnaire to check

the consistency of completed forms. For example, in the sample form,

Outside Surface of Building (XS) should be the same as the material

code in COND or the EXTERIOR section (see Appendix B) . Some of the

data may be checked for consistency as well. Examples are blank column

checks, check for exterior readings, check for a kitchen, etc. Again,

the pretest is an important preview of the workability of the survey

form.

The primary purpose of the lead paint survey is, of course, to

determine the incidence of lead paint in the homes in your community.

A survey such as this is expensive, in terms of dollars and time spent,

33
but the incremental cost of gathering additional information may be small

by comparison 1/. Therefore every consideration should be given to the

collection of supplementary or incidental data which might be valuable

to your department or to other departments. The criteria for deciding

on the inclusion of additional observations should be that (1) they do

not jeopardize the primary survey objectives and (2) the value of the

additional information exceeds the cost of gathering it.

PUBLICITY CAMPAIGN

Our experience has shown that, for the most part, the more informed

people are about a lead paint survey, the more cooperative they are.

For this reason a publicity campaign should be started two weeks to a

month before inspections begin. The dangers of lead paint poisoning,

the reasons for the lead paint survey, and the potential benefits of

the survey to the community should be explained. Mention that the

project is being conducted in all sections of the city, in high and low

income areas, and that the lead paint detector will not harm the occupants

or their possessions. Make the message simple and stress the positive

aspects of the program. Try to prevent rumors before they begin. If

someone believes that a penalty will be imposed or personal costs will

be incurred if lead paint is found in his home, he will not welcome the

inspectors. Be sure to mention that each home has been randomly selected

(both those with and without children) and that the occupants will receive

1/Remember that there is a two person team. The inspection requires 100%

" of the XRF user's time but only a fraction of the recorder's. Thus

almost any yes -no or multiple choice observation about the unit is free

with respect to inspection cost. The only costs are in training and in

data handling. When questions requiring subjective answers or opinions

are put to respondents, things can become quite complicated. This is

not recommended as part of a lead survey.

34
a letter indicating that their homes have been chosen.

Some surveying organizations have expressed concern that an extensive

publicity campaign might create security problems such as criminal impersonation of the inspectors to gain entry to the dwelling units. This risk

can be minimized by advertising that official letters will be sent to the

chosen homes prior to the survey, by stating in the letter that a telephone

appointment will be made and finally by assuring that the inspectors appear

at the appointed time and are properly identified.

Independent studies have shown that information received through

television, radio and newspapers is more readily accepted than information received from other sources. So it is important to solicit the cooperation of these media from the start. Public service announcements,

informing the public of the survey, are invaluable. Since public

service television time is the hardest to obtain, spot TV messages can be

used, with more detail being offered in radio announcements and in the

newspapers

.

It is a good idea to compose the introductory letter (mentioned

above) at this time so that copies can be ready when they are needed.

The letter should be simply and clearly written and include a statement

of purpose, the number of homes selected and the personnel, time and

equipment involved. The introductory letter shown in Figure 2 was

used in a survey in which all inspections were by appointment. The

occupant was subsequently contacted by telephone to arrange an inspection

time and answer questions. I fVnon- appointment inspections are to be

made, an information telephone number should be included. As with the

rest of the publicity campaign, it is best to stress the positive rather

35
Figure 2 SAMPLE

INTRODUCTORY LETTER

Dear "(Your Jurisdiction) Citizen:

The (Your Department) is conducting a study to determine the amount

of lead paint on the walls of your home. High lead levels in paint

may be a health hazard especially for young children. The study will

be conducted in homes where there are no children as well as in homes

with children.

(Your Number) homes have been chosen as a cross section to represent

all neighborhoods and types of homes in (Your Jurisdiction) . Your

home is among those randomly selected for this study

Within the next two weeks, you will be contacted by phone to arrange

a mutually convenient time for two (Your Department) staff members,

bearing identification cards, to visit your home. They will request

your permission to take measurements of the lead content of the paint

on interior walls. and woodwork, as well as painted exterior surfaces.

The measurements will take about one -half hour. They will use a portable lead detector that is safe for you and your possessions, uses no chemicals, and does not harm or mar the surfaces measured.

Thank you for your cooperation in this important project.

Sincerely,

(Name)

(Title)

36
than the negative aspects of the project. Letters which do not dwell on

the health hazards seem to be the most effective. Letters on official

stationary carrying the signature of the best known department official

will earn the greatest credibility and cooperation.

PRETEST

Before beginning the actual housing survey, it is beneficial, if not

absolutely necessary, to first have a pretest- -that is, to select a small

number of dwelling (additional to the sample) and inspect them, using the

supervisors and administrators as inspectors. This exercise will highlight

problems in the data collection form, if there are any, and will give the

supervisors experience with the situations that the inspectors will face

during the actual survey. Several estimations made prior to the survey

can be validated during a pretest. Survey costs and budgeting can be

redetermined. The pretest is the time to make final adjustments. Changes

made as a result of the pretest will make for a more efficient housing

survey.

RECORD KEEPING

As the units are selected from the directory, the necessary information should be transferred to file cards. Make two cards for each dwelling

and file them separately- -one in a control file for the project administrator and the other for the supervisor's or project manager's work file.

For efficient control and retrieval the cards should be filed according to

census tract. The census tract number plus a serial number will serve to

distinguish each dwelling unit from all others in the tract and the total

sample. For instance, if there are 300 houses in census tract 501, the

card will be numbered 501001; 501002,..., 501300.

37
Some supervisors have found it efficient to maintain several files

or several sections within a file, in order to divide the dwellings according

to their survey status: completed units; refusals, not-at -home; vacant

units, etc. This helps the supervisors to determine the status of the

project --how many have been inspected, which homes have to be rescheduled,

and how many additional sample sets have to be drawn.

Address

Name of Occupant:

Final Insp. Date:

Identification No.

Phone

Names of Insp,

Team Members:

Comp. NDU Ref. NSA Vac. UC

No. and Ages of Children in Residence:

Demo.

Comp. - completed

NDU - not a dwelling unit

Ref. - refusal

NSA - no such address

Vac. - vacant

UC - under construction

Demo. - demolished

NOTIFICATION OF RESIDENTS

Approximately five to ten days in advance of the first inspection,

introductory letters should be sent to the group of dwelling units to be

inspected during the first week. Addressing the letters to "Occupant"

will save a lot of time in the long run. After the initial mailing,

letters should be sent out frequently enough to ensure having a group of

notified residents with whom telephone appointments can be made.

38
There are advantages and disadvantages to telephone scheduling. It .

definitely saves inspectors' time and it serves to confirm the introductory letter, thereby giving residents a greater sense of security in

allowing the inspectors to enter their homes. However, telephoning also gives

the individual an opportunity to refuse inspection. Some inspectors are

more successful in gaining entry when they arrive at a dwelling unannounced.

For this reason, they prefer the "cold-call" method (no additional

notification after the letter)

.

If telephone scheduling is used, appointments should be arranged a day

in advance. An approach like the following has been used successfully for

this purpose:

Caller: Hello. This is (caller's name) from the (your department)

calling. Did you receive the letter about the Lead Paint Program?

Occupant: Why yes, I did.

Caller: Good. Then would it be convenient with you if our inspectors

came by about 10 o'clock tomorrow morning?

Occupant: I guess so.

Caller: Fine, they will be there at 10:00 a.m. tomorrow. Thank you

very much.

Telephone refusals should be anticipated. However you should try

to convince the occupant to permit an inspection. You can prepare a

list of common reasons for refusal and develop effective arguments to

counteract them. Typical reasons for refusal are:

1) Suspicion of governmental inspection of their residences;

2) Doubt of the study's importance and intent;

3) Would not be at home for the inspection;

39
4) Have no children and fail to see why you wish to include them

as participants;

5) Have just painted their dwelling with non- leaded paint.

It may be necessary for the scheduler to spend some evenings and/or

Saturdays making appointments with people who are not at home any other

time.

Every attempt should be made to minimize refusals. However, if they

cannot be avoided, additional samples can be drawn to make up the deficit.

If there is a pattern to the refusals- -for example, everyone with a house

built before 1940 denies entry- -this will bias the findings 1/. For this

reason, it is a good idea to keep records on the types of houses from

which you are being barred. The survey statistician will use this information in the reconciliation of population data source -sample -survey

requirements. Fortunately, our previous lead paint surveys have not

encountered such biases.

The self-addressed, stamped call -back card (Figure 3) should be left

at the door of residents who are not home even after scheduling appointments, or who never seem to be in for telephoning.

If the cards are not returned, the inspectors may have to make

unannounced visits in order to complete the survey.

1/ Because refusals stem from a particular attitude, they are not as serious here as in a household survey whose purpose is to ascertain

attitudes of respondents. In a lead paint survey, the occupant almost

certainly has no idea of the lead content of his dwelling; refusals are therefore less likely to be related to lead content.

40
Figure 3

Call-Back Card

Date

Dear

Address

As explained in a letter sent to you by (Your department)

,

two representatives tried to visit you today to measure the

lead content of the paint in your house. Since we were unable

to contact you, a rescheduling of the visit would be appreciated. When may we return?

Date:

Time: 9 10 11 12 12 3 4 5 6 7 8 9 10 11 12

Check the most convenient time and drop this card in the nearest

mailbox. No stamp is needed.

41
SUPERVISOR FOLLOW-UP

Each morning, the inspectors have to calibrate their lead detection

instruments and record all resulting data in their calibration record

books. Check to make sure that this is being done properly, especially

for the first few days.

During the first week the supervisors should start "dropping inM

unexpectedly on the inspectors to help them out and to see how they are

doing. It will not take long to discover which inspectors especially

need that occasional visit.

At the end of the first day the teams should be gathered together

for an informal discussion of the day's events. This allows people a

chance to learn from each other's experience. The supervisors can check

the survey forms and point out any inconsistencies or omissions they find.

Sometimes these meetings afford an opportunity to praise someone who has

done a particularly good job. These discussions should certainly be held

nightly for a week or two but can become less frequent as the project

progresses. Do continue to spot check survey forms fairly regularly for

errors

.

After the supervisors have checked the data collection forms and are

satisfied that they have been completed properly, the appropriate

information can be recorded on the supervisor's cards and the card can

be transferred to the "completed" file.

42
PART III

DATA REDUCTION AND ANALYSIS

43
DATA BASE ORGANIZATION

As the questionnaires are completed by the inspectors and collected

by the supervisors, they must be assembled and organized into a data base.

Such organization or "structuring" of the data is needed to prepare for

and facilitate the analyses that will be required to meet the specific

objectives of the survey. The structure should be flexible enough to

accommodate reasonable shifts in the objectives and to provide, during

the survey, information which is useful for management.

To illustrate the sort of shifts in objective which should be anticipated,

consider the term "hazard". In Section I, the term "hazard" was used as

if there were actually a specific threshold value of lead concentration,

above which it is hazardous, and below which it is innocuous. In fact

lead hazard threshold is determined by a legal and political process

(with input from health and medical experts and from society in general)

.

This basis of hazard definition creates difficulties for the analyst;

he must anticipate changes in the definition of the hazard and be prepared

to accommodate them. The system design should be such that changes should

require little effort.

The data base can provide much information to support the internal

management of the survey. In addition to its key role in quality control

(to be discussed explicitly later) , there are a number of potential uses

such as:

1) Determination of which of various techniques (telephone call,

call and letter, different letters, cold call, etc.) are most

effective.

2) Determination of the best training procedures.

44
3) Determination of productivity by day of week, weather conditions,

etc.

CONSTRUCTION AND MAINTENANCE OF THE DATA BASE

The data base manager has the responsibility for both constructing

and maintaining the data base. The primary input is the set of completed

questionnaires, but there will be other inputs as well. The organization

of the data base is by dwelling unit; each dwelling unit is represented

as an "element" of the data base. The characteristics of the dwelling

unit (measurements, codes, etc.) are "data items".

Each element of the data base will contain some (perhaps all) of

the questionnaire information. Depending upon the specific objectives of

the survey, there may be data items from other sources (census, tax rolls,

school records, etc.) and some which are derived or computed (hazard

indices, mean or median XRF readings, etc.) as well. Four conceptually

distinct functions are required to construct and maintain the data base. 1/

First, the questionnaire must be edited. The information in the

questionnaire must be edited for intra -dwelling unit consistency; this

includes checking for omissions (blanks), for illegal codes, and for

compatibility among those data items for which redundancy has been designed,

into the questionnaire. The editing may include some coding, classifying

or calculation to produce other data items as well.

1/ These functions include both "editing" and "recoding". Our experience

has been with large computerized data bases in which editing and

recoding have been accomplished with a single access to the data base.

If the data are to be tabulated manually and if the base is small, only

the questionnaire edit is required. The other functions do not require

formal procedures if the data base is small; they can be performed on

an ad hoc basis. The lack of formal procedures does not imply that less

editing is done, for a small sample each error is more critical than it would

be for a larger sample; thus even more work per unit is justified to assure

the integrity of the data base.

45
Second, the data base edit must include checks for inter-element

consistency. Typical tests include checks for duplicate serial numbers,

for duplicate elements, and for the proper number of elements. This

editing may include adding new elements from sources other than the

questionnaire

.

Third, there must be capabilities for deletion, replacement, modification, and addition of elements (dwelling units) . There must be capabilities for replacement and modification of data items (characteristics

of dwelling units)

.

Fourth, each element and each data item should be traceable to its

source. That is, direct comparison of the questionnaire and the analogous

element of the data base should be possible.

The management procedures necessary for performing these functions

must be designed and implemented prior to the data collection phase of

the survey.

Editing of the questionnaires is a major factor in assuring the

quality of the data base. Proper editing procedures and appropriate use

of editing outputs can improve supervisor and inspector performance during

the course of the survey.

In any survey instrument, there should be some redundancy. That is,

much of the data should be derivable from other data collected on the same

questionnaire or on some other questionnaire which may be associated in

some logical way. There are also characteristics of the questionnaire

which can be checked for consistency. Use of redundancy and consistency

checking improves data base quality in several ways.

First, it is possible to "manufacture" valid data under some circum46
stances. For example, in the sample questionnaire (Figure 1), if the code

for exterior wall material in the section showing the exterior condition and

XRF readings is left blank, the proper value can be manufactured from the

field XS, the exterior surface material. Examples of consistency checking

include checking for blank fields, for illegal codes, and for missing

lines. Every dwelling unit must have a kitchen; every single family

dwelling unit must have an accessible exterior.

Second, the errors found by consistency and redundancy checking enable

the supervisors to determine how well the inspectors are performing. A

consistently high error rate among all inspection teams indicates a need

for more training or more supervision. If the errors are more frequent for

one particular check, there may be an ambiguity in the questionnaire which

is causing the difficulty. If the error rate varies among the inspection

teams, the supervisors will know which teams need more supervision.

Third, the check enables the individual inspection team to estimate

how well its inspections are being performed. This self-evaluation is

possible on both absolute (number of inconsistencies) and relative (inconsistencies compared to those of other teams) scales. If the checked results

can be returned to the inspector quickly, there may be a very favorable

impact on the morale of the inspectors. We have found that the inspection

team is isolated in its work and may feel that the data being collected are

not being used, are useless or are being lost. Since meaningful analyses

cannot be performed until the data base is complete, any interim results,

particularly as they relate to individual performance, can help to alleviate

feelings of isolation and futility.

Fourth, we assume, from necessity, the frequency of uncheckable

47
errors and the frequency of checkable errors are related. If the error

rate as measured by consistency and redundancy errors, is low, there is

some assurance that errors which cannot be checked are also infrequent.

It is important to return the results of the questionnaire edit to

the supervisors (and inspectors) very quickly. Many errors are such that

no additional field work is required to make the appropriate correction if

the error report is received quickly enough. For example, an inspector

would probably remember that the house at 123 South Main Street, which he

inspected the previous day, was a frame unit built prior to 1940. Put

he might be unable to recall the age and construction type of the unit

at 123 North Main Street which was inspected three weeks ago last

Tuesday.

To permit interim processing (i.e., exploratory analyses prior to

the completion of data collection) , every field within a record must be

recognized as legitimate by the analysis programs. This can be insured

by using "default" values for variables or characters Which have illegal

or inconsistent values. Each default inserted should trigger a warning

"message" that the default value has been generated. There are several

general classes of default values; each of them requires somewhat different

treatment in the edit process and the remedial procedures.

The first class is that for which no "true" or "most likely" value

can be substituted. These include blank fields and illegal values.

Examples are:

1) Non-numeric characters where XRF readings are expected

2) Missing or illegal codes for age of unit or exterior surface.

These values are to be replaced by a default value to be interpreted

as "unknown".

48
A second class is that for which a true or most likely value can be

deduced. Examples are:

1) Substitution of the "material" part of the "condition" code from

the exterior description portion of the questionnaire for the

field XS.

2) Substitution of the code denoting a "good" surface for a blank

"surface" code.

3) Substitution of 0, 1, or 5 for 0, I, or S in positions where

numeric values are required.

These values which have been defaulted are subject to subsequent

update or correction. The correction may be supplied by the data base

manager, the supervisor, or the inspector. Even if no update is ever

made, some useful data are retained. One error or inconsistency does not

mean that the questionnaire is useless. This can be illustrated by example:

the age of dwelling category has three legitimate values: 1, 2, and 3.

The consistency edit should accept a 1, 2, or 3; default an I to a 1; and

default any other value to a 4 which is then interpreted as "unknown"

for subsequent processing. The warning message indicates that the default

has occurred; first the data base manager, then the supervisor and finally

the inspectors, will try to produce the proper correction, if required,

for the defaulted value. Even if the correction is never made, the data

element retains most of its value for the analyses. For any analysis

which does not depend on age of dwelling, this element is as valid as any

other in the data base, whereas for an analysis which is dependent on

age, the sample is smaller by one unit.

Exactly how far to pursue the attempts to correct defaulted values

49
in the data base is an extremely difficult question . Considering

that two to three man hours are required to collect data for a single

unit, a considerable effort is justified to salvage as many units as

possible. However, re- inspection or partial reinspection is not worthwhile, both because of the costs involved and the anticipated reaction of

the occupant. Certainly those corrections which can be made without

gathering additional information- -those dependent upon the original questionnaire, the supervisor's record and the inspector's memory, should be

made. The corrections which require only observation of the exterior

by the inspector or supervisor or minimal participation by the occupant

(doorway interview or telephone interview) should be made unless considerable cost or time is involved. Those corrections which require reinspection should not be made; the default values should remain in the data

base.

ESTIMATING THE INCIDENCE OF LEAD PAINT

ON SURFACES

Once the data have been collected and edited, the determination of

the fraction of hazardous units in the sample is in principle a simple

counting problem. The interpretation of the meaning of this sample

fraction is done in much the same way as the estimation of the required

sample size - by use of tables such as Tables 1 and 2 in Part I of the

formulae in Appendix B.

Depending on the measure of hazard used to characterize a dwelling

unit, the conceptually simple task of counting the hazardous units may

be arduous and time consuming. If the average lead content of a number

of surfaces is used, a considerable amount of arithmetic is required simply

50
to calculate the measure for each dwelling unit; if the median lead readings is to be used, even more work is required to compute the measure.

Typical measures which could be used for any set of surfaces are:

1 - Highest lead readings

2 - Average. lead readings

3 - Median lead readings- or some other percentile

4 - Fraction of surfaces having lead reading which exceed some

threshold (this is an exposure index of sorts.)

The measure problem is further complicated by the fact that there

are many sets of surface populations : walls , doors , windows , exterior

surfaces, wet rooms, dry rooms, combinations of surface and substrate

conditions as well as demographic characteristics of the occupants.

If the survey has a code enforcement orientation, the measure (or

measures) to be considered are those which are specified in the particular code. These code requirements may depend on surface type, surface

condition, lead content, and occupancy characteristics but are relatively

straightforward and can be fixed as a part of the establishment of objectives of the survey.

For a health oriented survey, the criteria for the selection of

appropriate measures are not nearly so simple. There is no generally

accepted medical definition of a lead hazard in terms of condition, lead

content, and accessibility. There is neither dose-response information

nor an understanding of the mechanisms by which lead is transported from

lead painted surfaces to the child. Fortunately, in practice, the

reasonable candidates for appropriate measures are related; our experience

in previous surveys has been that these measures are well correlated. In

51
the absence of any firm technical definition of what constitutes a

hazard, two choices - what measure or variable to use, and the critical

or threshold value of that variable - are policy decisions.

52
RECOMMENDED READING FOR FURTHER INFORMATION ON SURVEYS AND STATISTICS

1. Kish, Leslie. Survey Sampling . New York; John Wiley & Sons, Inc., 1965,

2. Lin-Fu, Jane S.. "Childhood Lead Poisoning - An Eradicable Disease,"

Children: An Interdisciplinary Journal for the Professions Serving

Children , Vol. 17, No. 1, Jan-Feb 1970.

3. Natrella, Mary Gibbons, Experimental Statistics , National Bureau of

Standards Handbook 91, August 1963.

4. The Rand Corporation, A Million Random Digits with 100,000 Normal

Deviates , The Free Press, New York; Collier-MacMillan Limited, London.

5. Slonim, Morris J., Sampling in a Nutshell , New York: Simon § Schuster,

1960.

6. Sudman, Seymour, Reducing the Cost of Surveys . Chicago: Aldine, 1967.

7. U.S. Bureau of the Budget, Household Survey Manual , 1969.

8. U.S. Bureau of the Census, Supplemental Courses for Case Studies

in Surveys and Censuses: Sampling Lectures . Washington , D . C .

Bureau of the Census, 1968.

9. Weiss, Carol H.; Hatry, Harry P., An Introduction to Sample Surveys

for Government Managers , The Urban Institute, March 1971.

53
APPENDIX A

DETERMINATION OF SAMPLE SIZE

A door to door, unit by unit census of all dwelling units 1/ in a

city to determine the lead paint hazard is an extremely costly and time

consuming process which most jurisdictions will want to avoid. Random

sampling is faster and more economical and can yield very accurate results.

Random sampling involves choosing a fraction of all the dwelling

units in such a manner that each unit has an equal chance of being selected.

By applying the mathematics of probability to the sample findings, the

characteristics of all the dwellings (lead paint content in this case)

can be estimated.

You, the planners and administrators of the lead paint survey,

should have an understanding of the sampling process even if you plan

to have a statistician handle this portion of the survey.

There is a statistical formula which gives the appropriate sample

size (n) for any desired confidence level. This formula involves the

following quantities:

n = number of units in the sample

N = number of units in the total population

P = the fraction of the total population which is hazardous.

P, of course, is not known; we are performing the survey to determine P. However, we can use the best current estimator of P which we

call p.

Q = the fraction of the total population which is not hazardl/'TJwellingTJnir' is defined by the U.S. Bureau of the Census as a group of

""rooms or a single room occupied or intended for occupancy as "separate living

quarters" by a family or other group of persons living together or by a person

living alone.

54
ous. Note that Q = 1 - P, and that just as p is used as

an estimate of P, so q = 1 - p is used as an estimate of Q.

E = The maximum error as a fraction of N which can be tolerated

in the estimate of P.

k = A number related to the required confidence level (e.g., 90%,

95% etc.) The appropriate value of k is obtained from a table

such as Table 4 (page 58)

.

All of these quantities except P and n are known, but we have an

estimator, p, which may be substituted into the theoretically correct

k

2NPQ

n " k'^PQ + (N-l) E

2 to yield

n=_^9__

k pq + (N-l)E^ which we use to calculate n.

If p is a poor estimator of P, we have a poor approximation to n, but the

value of n is relatively insensitive to changes in p. A change in p produces

a smaller change in n. Table 3 illustrates this: consider N = 100,000;

if p = .2, n = 256; if p = .4 (changed by a factor of 2) , n = 383 (changed

by only a factor of 1.5). Our actual procedure in calculating n is to use

the best available data and judgment to establish a preliminary estimate for

p, then solve for n. As the data are collected we periodically recalculate

n using the fraction of the inspected, units which are hazardous as an estimate

for p. This is a trial and error method with the important property that each

trial produces a better estimate for p than its predecessor.

If there is no reasonable initial estimate of p, an upper bound on the

size of n can be established by setting p = .5. Table 3 illustrates that

even if this estimate is off by a factor of 5, the calculated n is well within

a factor of 2 of the true n. Before proceeding to examples, we will explain the quantity k which

55
appears in the sample size formula. Both experience and theoretical

considerations lead to the conclusion that the probabilities of errors of

different sizes, in a great variety of situations, are accurately

representable by the bell -shaped or "normal" curve shown in Figure 1.

The total area under the curve is taken to be 100% of all possible

errors, both with respect to error size and error frequency. Errors of

greater and greater size, whether positive or negative, are less and less

probable, corresponding to the curve's falling away steadily from the

peak. Normal probability curves differ in the rapidity of this falling

away; associated with each curve is a number a (the "standard deviation"),

which is a measure of the steepness of the decline. The probability of

an error greater than a in magnitude is roughly 11.1^% (this is 2 x 13.591

+ 2 x 2.14% + 2 x 0.14% as illustrated in Figure 1); the probability of

an error greater than 2a is about 4.54%; and an error greater than 3a

will occur less than 0.28% of the time. The coefficents 1, 2, and 3 in

la, 2a, and 3a therefore correspond to respective levels of confidence

of 68.26% (2 x 34.13%), 95.46% (2 x 34.13% + 2 x 13.59%), and 99.72%

that the error will not exceed the stipulated size. Turning the situation

around, we can fix a desired level of confidence, say 90%, and from the

normal curve or a table such as Table 4, find the particular k such that

the probability that the error will not exceed ka is just 90%.

Specifically, for 75%, k = 1.150; for 90%, k = 1.645; for 95%, k = 1.960;

and for 99%, k = 2.576.

In our situation, the "error" which is distributed according to the

normal curve is the discrepancy between P, the fraction of hazardous

units of the total population, and p, the fraction of hazardous units

56
w

<DN

•H

COao

•H

+-> rHI

Pw3

PL< 5h

crt

m > o

Jh o\o o LO m

Pi /—\

•H

p- .p

4-3 H

pi |g o

•H 1/) +-> •H u

03 PL. fH

U-, +->

Cti T3 .a

rH +-> rtN >.

cd 4-> E •H

iH o •H M JQ

as /—-\ 4=i c o

Jh

P-,

<L> N o\Q •H LO

CO CTi

<D rHa

COmoPo• HM

aj !—1

(1) c* cLOo o

<st- vO

Pn

p c c

to rli li

PL. P o c: cm oc

II ll Pu P o00

-5* to * o to vO rH LO r*> a. o o CM OO r- OO a. a. o

CM CM CM to to to to «tf o

00

CM

to vO

vDen *

OO r^ c CM to

I-- LO F- 00 OO

CM to to to to#

OO vO a> to rH CM LO VD <!t vO

f» CM LO OO CO LO rH CM to to

i—l rH iH CM CM to to to to

CM to

to LO

*=t «=t

O LO vO c «* LO LO LO CM CM CM CM CM o o

rH &

II II P. PLO LO o a~.

iin Ph Ph

CM 00 o cr>

ll ll pu Pu o 00

00

a. VDO

vD

CM

O CM

rH 5

vO

LO vO vO

vO

VO

LO vO

CO >* 00 CT> o o

CM CM CM to to to *

CM

to

Oo ooCM

CO

CO

to oo-3- o

LO o oo

LO oo oCOooLO o

COo o

CO

COo

COooooo

57
Table 4

Percentage of Normal Distribution Lying Within Multiples of a

{C% are within ko of the mean)

k C(%)

1.0 78.3

1.1 72.9

1.2 77.0

1.3 80.6

1.4 83.8

1.5 86.6

1.6 89.0

1.7 91.1

1.8 92.8

1.9 94.3

2.0 95.4

2.1 96.4

2.2 97.2

2.3 97.9

2.4 98.4

2.5 98.8

2.6 99.1

2.7 99.3

2.8 99.5

2.9 99.6

3.0 99.7

3.1 99.8

3.2 99.9

58
in the sample.

.141

2.14%

13.59%

34.13% 34.13%

/ \

\

13.59%

2.14%

.14%

•3a 2o la la 2a 3a

Figure 1

Example 1:

To determine the size of the sample in a population of 25,000 dwelling units where it is desired to state with 95 percent confidence (2a)

that you are within 5 percent of the total number of hazardous units,

you must first make a preliminary estimate (p) of the fraction of units

that are hazardous. For this example we will assume that half the units

are hazardous (p = 0.5) and half are not hazardous (q = 0.5), so that we

will get the largest possible value of pq, (.25) as is shown below:

_Q_ PQ

0.1 0.9 .09

0.2 0.8 .16

0.3 0.7 .21

0.4 0.6 .24

0.5 0.5 .25

0.6 0.4 .24

0.7 0.3 .21

0.8 0.2 .16

0.9 0.1 .09

59
The higher the value of PQ the larger the sample required and the more

expensive the survey. Using the equation we have already introduced,

_ k

2NPQ

k

zPQ + (N-l) E2 and substituting,

n = (2)

2 (25,000)(0.5)(0.5) _

(2) (0.5) (0.5) + (24,999) (0. 05) 2

Thus, 394 dwelling units should be included in the sample.

Example 2:

Table 3 (page 57) illustrates, still using 95% confidence and a 10%

interval width, the effect on sample size of changes in P and N. Note

that while n varies with both P and N, the variation with P is far greater.

Note also that for a given P, once N is as large as 10,000, a larger N

affects n by at most 4%

.

Example 3:

For this example a difference not exceeding 10 percent is required

with 99.7 percent confidence (3 ), with N = 25,000 and P = 0.5.

n =

(3)

2 (25,000)(0.5)(0.5) = 223

(3)

2 (0.5)(0.5) + (25,000) (0.10)

2

The larger the acceptable difference, the smaller the sample size needed

for the desired confidence. From these examples we see how important

our estimates are in choosing an appropriate sample size. The sample

size is heavily dependent on the fraction of hazardous dwellings. The

pretest can be used to validate your original estimates so you can be

assured that your sample size is acceptable.

Suppose that after sufficient data have been collected and analyzed,

the value of p is found to be 0.2, rather than the original estimate (P)

60
of 0.5. Since 0.5 gives the largest possible value of PQ and the largest

sample, you have oversampled. This error, though costly, will increase

the level of confidence in your findings. Your statistician can help

in determining this.

Example 4:

Just to see how the value of P can affect the sample size, let's

recalculate Example 1 using 0.2 instead of 0.5 as the value of P.

(2)

2 (25,000)(0.2)(0.8) _ n = —;

o— " 254

(2) 2 (0.2) (0.8) + (25,000) (.05)

z

This is 140 fewer units than were necessary had P been 0.5 (See Example

1).

If the opposite situation occurs and the sample size is too small

for the specified degree of confidence, additional sampling will be

necessary.

STRATIFICATION FOR SAMPLING

The construction, type of usage (renter or owner occupied) , and size

of housing unit reflect the method of design and construction used for

them as well as the probable type of maintenance received over the years.

All these are clues to the probability of presence of a lead paint hazard.

We may wish to treat each subpopulation of interest as a total

population in itself and make separate estimates of p for each of the

subpopulations or cells. This approach requires that we have a sample

size from each such cell. It may not be possible to select samples

from stratified lists (i.e., separate lists for each cell), however,

since this information is not always available. The samples may be

drawn from an aggregated list by use of the procedure to be described.

61
The following is a table of cells typical of a lead hazard survey.

Category Year Built

Pre1939

1940-

1959

Post1960

A. Owner Occupied

Structures

B. 1-4 Unit

Structures .

C. 5-19 Unit

Structures

D. Over 20 Unit

Structures

Using the values from Example 1, we can demonstrate the process

required if stratified lists are not available.

N = 25,000

k = 2

E = .05

P = as listed below for each cell

Assume that from some other source, the population N can be disaggregated

as shown.

Category

Single Family

Detached

Multifamily

Dwelling Units

Built

Prior to 1939

Built

1940 to Present

Cell I

10,000

Cell II

5,000

Cell III

4,000

Cell IV

6,000

62
Suppose that for Cell I we have no idea of the value of P. Therefore, we assume the worst possible case (i.e., corresponding to the largest

possible sample size) with P = 0.5.

For Cell II our best estimate is P = 0.2; for Cell III, P = 0.3;

and for Cell IV, P = 0.4.

Applying the sample size formula gives the following sample sizes;

Category

Dwelling Units

Built

Prior to 1939

Built

1940 to Present

Single Family

Detached

Cell I

385

Cell II

237

Multifamily

Cell III

310

Cell rv

361

We must (assuming our estimates for the respective p's are not

modified) eventually inspect 1306 dwelling units; these must include 385

in Cell I, 337 in Cell II, 310 in Cell III, and 361 in Cell IV. We

begin by drawing a sample, from an aggregated list, of some size less

than 1306, say 1000 dwelling units. Assume that after the 1000 units

are inspected, we find that their distribution among the cells, and the

p associated with the cells, are as follows:

63
Category

Single FamilyDetached

Dwelling Units

Built

Prior to 1939

Cell I

322; p = .2

L

Multifamily

Cell III

226; p = .3

Built

1940 to Present

Cell II

200; p = .2

Cell IV

252; p = .25

We discover that our estimate for P in Cell I was rather poor, so

we recalculate the sample size using p as the estimate for P. The n thus

calculated is 250, which is less than the 322 we have inspected. Cell I

is completed (and we've actually oversampled) even though the 322 units

are fewer than we had originally estimated.

For Cell II, our estimate for P was quite good; the estimated

sample size required is unchanged at 237 units. Cur subsequent samples

must include 37 units in Cell II. Similarly our estimate of p for Cell III

was good and Cell III requires an additional 84 units.

Using the p for Cell IV we recalculate n and get a new value of

301; thus we need 49 units in Cell IV.

We now draw a second sample in an attempt to fill one or more of

the unfilled cells. In the second sample all units falling in Cell I

will be discarded, but all other units in the sample mast be included,

because of the equally likely principle, to avoid a bias. We must

therefore determine a "good" size for the second sample in order to

avoid unnecessary costs which would be incurred by overshooting the

required sample size.

64
Based on the original total population we expect 20% of a siinple

random sample from our list to belong to Cell II, 16% to belong to Cell III

and 24% to belong to Cell IV. In order to produce an efficient sample

size, we form the ratios of the supplementary requirements for each

cell to the fraction of an unstratified sample expected to fall into

that cell. These are:

for Cell II, 37

.2 = 185,

for Cell III, 84

TIF = 525, and

for Cell IV, 49

72T = 205.

Using the minimum from among these ratios * , 185, we add a small increment,

say 15, to avoid an undersample. We expect that a second sample of 200

units will fill Cell II; it will also add about 35 units to Cell III

and about 45 units to Cell IV.

The procedure used for the second sample may be iterated until all

cells are completed. Each iteration should complete at least one

unfilled cell.

Some "ground rules" are required to accommodate the idiosyncrasies

of a city directory. These ground rules are:

1. Any commercial or industrial address selected is discarded.

2. Any blank (non address) line selected is discarded.

*If the cell population estimates are suspect, the denominators of these

ratios may be replaced by the fractions of the first sample which belonged

to the cell.

65
3. Any line selected which is not the first line of an address is

discarded.

Several methods for the drawing of a sample are illustrated byexample. There are advantages and disadvantages to each; the method to

select depends on the objectives of the survey, characteristics of the

population list, the equipment available, etc.

For each of the methods we will assume a city of 600,000 dwelling

units, with 5,000 units to be selected for a sample. Our city directory

has 1450 pages with five columns per page and 100 lines per column. Note

that there are 250 pages more than are required to contain the dwellings;

these are due to commercial addresses; multi line listings; interspersed

blank lines and short pages.

Method 1

i Step 1 - Calculate the total number of entries, n , from the

directory, such that 5000 (or n) could be expected to be dwelling units.

5000 1200 (pages to contain 600,000 units)

n 1450 (total pages)

145

n =

J2TT x 5000 = 6042

Step 2 - Divide the total number of entries in the directory (pages

x columns x lines) by n to get I.

I = 1450 x 5 x 100

UWl = 120

66
Step 3 - Using a random number tablel choose a three digit (the

same number of digits as are in I) sequence of random digits. If this

three digit number is greater than I, discard it and choose another

three digit sequence until one is found which is 120 or less. This

number becomes J. For example if our random number table is:

314 159 102 271

314 is greater than 120

159 is greater than 120

102 is less than 120 and becomes J.

Step 4 - Our sample is now defined as the Jth entry in the list

plus the (J + I)th entry, the (J + 2I)th etc up to the (J + (n - l)I)th

entry. Now we must simply translate both I and J from index numbers to

their equivalent in page, column and line. I is 120 corresponding to no

pages, one column, and 20 lines. J is 102 or no pages, one column,. and 2 lines.

The first few units of our sample are:

Location In Directory

Index Page Column Line

102 1 2 2

222 1 3 22

342 1 4 42

462 1 5 62

582 2 1 82

602 2 2 2

722 2 3 22

1/ For example the Rand Corporation, A Million Random Digits with 100,000

Normal Deviates , the Free Press, New York; Collier -MacMillan Limited, London

67
This method has the advantage of being conceptually simple; the

drawing of a single random number fixes the entire sample. Other

advantages are that the units selected are in directory order (and thus

approximately geographical which implies that any sequence forms a

reasonable inspector itinerary), and that there will be no duplicates.

It has two disadvantages; first, while the entire sample is random, its

roughly geographical order implies sequences of consecutive entries will

not in general produce an unbiased sample; second, the arithmetic for

getting successive entries may be messy since non decimal "carries" are

involved in stepping from one unit to the next. The method requires

substantial personnel time for large samples.

Method 2 - For this procedure, an m digit sequence of numbers in which

each digit is 0, is interpreted as a 1 followed by m zeroes. For the cases

in which we are interested in our example, 0000 becomes 10000; becomes

10; and 00 becomes 100.

Step 1 - Determine the largest multiple of the number of pages which

is less than or equal to the largest number which can be expressed in the

same number of digits contained in the number of pages. In this case, the

number of digits in the number of pages is four; four zeroes (oooo) is taken

to represent 10000; 1450 is contained in 10000 six times.

Step 2 - Determine the largest multiple of the number of columns which

can be expressed in a single column. In this case, 2 x 5 = 10; thus 2 is

the largest multiple

.

Step 3 - Determine the largest multiple of the number of lines per column;

100, which can be contained in a two digit number. In this case the answer

is 1 (remember that 00 is interpreted as 100)

.

68
Step 4 (to be repeated 6042 times)

a. Choose a four digit random number to select the page. If this

number is greater than 8700 (6 x 1450) .discard it and select another number.

If it is less than or equal to 1450, it becomes the page index; if it is

greater than 1450, divide the number by 1450 and use the remainder as

the page index.

b. Choose a random digit to determine the column index. In this

case, since 5 is an even multiple of 10, a random digit of 1 or 6 selects

column 1; 2 or 7, column 2; 3 or 8, column 3; 4 or 9, column 4; and 5

or 10, column 5.

c. Choose a two digit random number to select the line. In this

case, the random number is the line.

d. Combine the three numbers thus selected; now we have a page,

column, and line which fix a directory address.

For example, if our sequence of random digits is:

912 314 159 265 427 182 849 045

For the first entry

4a 9123 is larger than 8700; we discard

1415 is less than 8700 and less than 1450; it becomes our page

number

.

4b 9 denotes column 4

4c 26 denotes line 26

4d our first entry is page 1415, column 4, line 26.

For the second entry

4a 5427 is less than 8700, greater than 1450

69
1450 ) 5427

4550

WTT

1077 becomes the page number

4b 1 denotes column 1

4c 82 denotes line 82

4d our second entry is page 1077, column 1, line 82.

Method 5

This procedure is based on a list of random numbers R, , R~, R, etc.

each of which is greater than or equal to zero but less than one.

Step 1 - our page number is the largest integer contained in

R-, x number of pages + 1

Step 2 - our column number is the largest integer contained in

R

2 x number of columns + 1

Step 3 - our line number is the largest integer contained in

R x number of lines + 1

This process is repeated using R,, Rr, and R, for the second entry;

R , R , and R

Q

, for the third, etc. until the entire sample has been

constructed.

For example, if our list of R's is:

.14132

.91286

.01030

.31415

.92654

70
.27182

.84904

our first entry is

.14132 x 1450 + 1 = 204.9140 + 1 = 205.9140 or 205

.91286 x 5 + 1 = 4.59430 + 1 = 5.59430 or 5

.01030 x 100 + 1 = 1.030 + 1 = 2.030 or 2 or 205-5-2

our second entry is

.31415 x 1450 + 1 = 456

.92654 x 5 + 1 = 5

.27182 x 100 + 1 = 28 or

456 - 5 - 28

The advantages of methods 2 and 3 are that the arithmetic is simple

and that any sequence of selected entries form a random sample. The disadvantages are that the sample will not be in directory order and that

entries may be duplicated; these methods also require substantial personnel

time.

Method 4 - This procedure makes use of a computer program to generate

and organize the samples required. Such a program would operate as

follows

The program generates a number of triples, each triple being a set

of indices each of which is randomly selected, denoting page, column and

line of the directory. Duplicates are eliminated and the first sample

is sorted by order of appearance in the directory and printed. This is

the original sample to be used in the survey. The remainder of the

triples are then sorted by 40 -entry blocks into directory order and these

71
samples are printed.

Each of the samples thus generated is a random sample, as is any

combination of these samples. If any entry from a sample is used, then

the entire sample must be used in order to preserve the validity (absence

of bias) of the procedure.

In using the samples, the entire first sample is used to start a

survey. Some entries, because of directory anomalies such as short pages,

commercial addresses, blank lines, etc., can be identified in advance

as not denoting dwelling units; others will be found, in the field, to

denote non-existent or nonresidential buildings. If the remaining sample

is smaller than that required, one or more of the 40 -entry samples must

be added to the survey sample. This process of adding 40-entry samples

to the survey sample may be iterated until the survey sample is of sufficient size for the desired confidence level.

A program is provided in Appendix D along with specimen inputs and

outputs

.

Advantages of this method are that the creation of the sample is

economical, quick, and easily reproducible. The disadvantages are that

there may be lag time until the program is created (or adapted) for the

available computer configuration, and, of course, access to the computer

is required.

72
APPENDIX B

ESTIMATING INCIDENCE OF LEAD PAINT

The procedures used to estimate the hazard fractions after the data

collection has been completed are similar to those described in the preceding section for estimation of sample size. For the present analyses,

the sample size and hazard fraction for the sample are given; the precision

with which the sample fraction P represents the total population fraction

P must be determined. To illustrate we will consider several examples.

First, assume an extremely small total population (N=400) , with a

sample size, n of 200, and that the number of hazardous units in the sample

is 100. Thus p = .5 and Tables 1 and 2 (in Part I) indicate that: 1) there

is a 95% probability that P lies between 43.1% and 56.91, i.e, that the actual

number of units lies between 172 and 228, and 2) there is a 90% probability

that P lies between 44.2% and 55.8%, i.e. that the actual number of hazardous

units lies between 176 and 223. However, since the confidence ranges in

Tables 1 and 2 are based on "large" populations and our N of 400 is "small",

we also consult Table 3 in Appendix A. This shows that there is at least a

95% probability that P is between 45% and 55% (180 to 220 hazardous houses),

because a "small" N means that n = 200 is relatively quite "large", the range

for P is reduced by 3.8 percentage points.

As a second example, we consider a case in which n does not appear

in Tables 1 and 2. Let N = 5000, p = .2, and n = 150. Since we have

no confidence ranges in these tables for n = 150, we must use the entry

from the table for the largest sample size which is less than n. In

this case the value is 100 and we can say: 1) there is at least a 95%

probability that P lies between 12.2% and 27.8%, and, 2) there is at

73
least a 90% probability that P lies between 13.4% and 26.8%.

From Table 3 we see that the effect of having a relatively small

N is not enough to assure a 95% probability that P lies between 15%

and 25%.

We can, however, estimate the confidence interval for any given

confidence level (95% in the example following) by a direct calculation

from the sample size equation:

k

2NPQ

n = k

2PQ + (N-l)E 2

Substituting p for P and q for Q and solving for E yields:

f

2 = k pq ^~n) h —g—-$nr

F

2 . (1.96)

2 x 2 x 8 ( 5000 - 150)

I5D (5000 - 1 )

E

2 = .00399 or

E 6.3%

Thus there is a 95% probability that P lies between (20.0 - 6.3%)

and 20 + 6.3%), i.e., between 13.7% and 26.3%. This narrows the

confidence interval from that which was the best attainable from the

table

.

Alternatively, we can calculate the confidence level for any

prescribed interval by solving the sample size equation for k. We give

two examples:

A. For a confidence range of 15 - 25%

k

2 = E

2n (N-l)

pq (N-n)

2 _

.05 2 x 100 (5000 •• 1)

.8 x .2 (5000 - 100)

74
k

2 = 1.60 or

k. = 1.3

Using Table 4 of Appendix A, we find that k = 1.3 corresponds to

C = 80.6; thus there is a probability of 80.6% that P lies between

15% and 25%.

B. For a confidence range of 10 - 30%

k

2 = .l 2 x 100 (5000 - 1)

.8 x .2 (5000 - 100)

k

2 = 6.37 or

k = 2.5

Using Table 4 we find that there is a 98.8% probability that P

lies between 10% and 30%.

75
APPENDIX C

INSTRUCTIONS FOR COMPLETING THE SAMPLE SURVEY FORM

The instructions for completing the data collection were used in a

survey in which some 4000 dwelling units were inspected. The specification

as to legitimate codes, conventions, etc. reflect the format of the instrument itself, the editing and analysis software used, and the objectives of

the survey. They are not presented as doctrine but rather as an example

of instructions which were effective in a particular survey.

General

The Data Collection Form (NBS-744), hereafter abbreviated as DCF, is

the sole reporting instrument for the housing portion of a survey. There

are several different types of data appearing on the DCF. Some of the

data will have been entered prior to the inspector's receipt of the DCF;

others will be transcribed from the XRF log book; the remainder will be

collected on-site from the individual dwelling unit.

In the description of the data elements, the term "field" is used to

denote a sequence of characters or digits which are treated as an entity.

DU is used as an abbreviation for dwelling unit.

The DCF consists of 19 lines each pre -numbered in the field CODE. The

field SERIAL NUMBER forms the unique identification of the DCF. Generally,

if the entity defined by a line of the DCF does not exist (a one story DU,

for example, would have no stairway), that line is left completely blank,

when the data are collected. The position of the relevant (or non-blank)

characters within the field is immaterial e.g. AAl is considered identical

to AlA or 1AA, where A is used to denote the character "blank". However,

the characters must be successive (a field such as 1A2 is meaningless

whereas A12 and 12A are meaningful and equivalent) . Decimal points are

76
ignored in entering XRF readings; a decimal point is assumed in subsequent

processing. No particular convention must be observed to avoid ambiguity

of l's and I's or O's and o's.

The right hand side (RHS) of the DCF contains a comprehensive list

of the codes required in the body of the form.

When the inspection is completed, lines 1, 2, 3 and one or more of the

lines from 4 to 9 will be entered. If the DU did not exist (mistake in the

directory), only line 1 will be entered. If the DU exists and could not be

inspected, lines 1 and 3 will be entered.

Section I - Identification

Line 01 - This line is not the responsibility of the inspector. All the fields

here except "V" will be filled before he receives the form. The

field "V" will be filled, if necessary, by the supervisor after

the form is returned.

SERIAL NUMBER - the arbitrarily assigned unique identifying number

for the dwelling unit. It must be numeric.

TRACT - the census tract in which the DU lies.

BLOCK - the census block in which the DU lies.

ZIP CODE - the postal ZIP CODE of the DU mailing address.

V - the visitation code which is entered after the DCF is returned.

If an inspection has been made, this field is left blank, otherwise

V describes the reason the inspection could not be made. The codes

are listed in I - VISITATION on the RHS. The accompanying codes is

self-explanatory; note, however, that 3 and 6 are different.

STREET NAME AND NUMBER - self-explanatory. This field is used only

by the inspector for locating the DU. It will be dropped in the subsequent construction of the data base.

77
Line 02 - These data are collected daily and copied onto the DCF from the XRF

Log Book. They relate to the day of inspection rather than the

specific DU.

XRF SERIAL - the three character numeric serial number of the XRF

instrument used.

TEST BLOCK - the XRF reading obtained from the painted panel test

block included with each instrument. This is taken after the instrument is calibrated.

ZERO READING - the reading obtained from the non- leaded wood block

included with each instrument. This is taken as the instrument is

calibrated.

ZERO VERNIER - the zero vernier (dial) setting corresponding to the

zero reading.

CALIBRATE READING - the reading obtained from the lead-foil-on-wood

block included with the instrument used in this survey. This is taken

as the instrument is calibrated.

CALIBRATE VERNIER - the vernier setting corresponding to the calibrate

reading.

PEAK READING - the reading obtained from the lead- foil -on -wood block

included with each instrument. This is taken as the instrument is

calibrated.

PEAK VERNIER - the vernier setting corresponding to the Peak Reading.

INSPECTORS - the initials of each of the members of the inspecting team

DATE -MONTH - conventional numeric codes 1-12 for month,

DATE -DAY - day of month

.

Line 03 - This line is completed on-site. The fields YEAR and those following

require the active participation of the householder.

78
SEP - is the sequence within the day, in which the DU was inspected

(first, second, third, etc.)' Do not include DU's which were

visited but not inspected.

TEST BLOCK - the average of five XKF readings on-site of the

medium painted panel test block included with the instrument.

TYPE - coded according to II --Type of Construction- -on the PHS.

If the type is mixed, enter the code for the predominant type.

XS - the material of the exterior according to III --Outside Surface

of Building- -on the RHS. If the surface is mixed, enter the

predominant type code.

PC - the code according to IV- -Occupancy- -on the PHS. For multiunit buildings, a mailbox count is a convenient way of

ascertaining the number of units.

YEAR - the code according to V--Year Built --on the RHS. This

should be obtained from the householder if possible; otherwise

it is to be estimated by the inspector.

OWNER - the code according to VI--Owner-Renter--on the RHS.

MORT - the code according to VI I- -Mortgage- -on the PHS.

PUBLIC - the code according to VIII --Public Housing- -on the RHS.

Public housing is that which is operated by some agency of

government whether federal, state, or local.

STTR - the subsidized housing code according to IX- -Subsidized- -on

the RHS. Subsidized housing is that for which at least some

portion of the rent is paid by some agency of federal, state,

or local government.

CHILD - the number of children, age 6 and under, resident in the DU.

79
Section II - Interior

This section of the DCF includes lines 4 through 17. Each of these

lines has the same format; the pre -printed CODE field identifies the

room type.

No attempt is to be made to move furniture , pictures^ etc . , or to

stand on anything to obtain readings. If the entity is inaccessible

(the ceiling, for instance) or does not exist, the field is left blank.

Lines 04 - 17.

Walls and Ceilings

COND - the material and condition of the walls, ceiling, etc.

This is a three character field. in which one character

is a material code according to XI (a) of the RHS; one

character is a base or a substrate condition code according

to XI (b) of the RHS; and the third character is a surface

condition code according to XI (c) of the RHS. The order

in which these characters appear is immaterial . The code

P2Q, for example is equivalent to PQ2, 2PQ, or QP2.

These codes are for the room in general rather than for

each specific surface. If the surfaces are mixed, the

code which most nearly characterizes the room at the height

of four feet or less should be entered.

Base Condition codes must come from a subjective evaluation

of the painted or varnished surfaces. The criterion is an

estimation of the magnitude of work involved if redecoration

were considered:

1. If no work is required

80
2. If minor -work is required, (small cracks,

no deep or large holes.)

3. If major work is required. (Large cracks,

deep or large holes, structural repairs).

Since COND reflects the general condition of the room,

it is best to complete this field after the room inspection has been completed.

WALL 1. WALL 2. WALL 3. WALL 4. and CEILING - each of these is

an XRF reading taken on the indicated surface. The walls

are numbered clockwise beginning with the wall to the left

of the entry. The wall reading should be taken at any

point less than four feet from the floor if such a point

is accessible. The ceiling reading is to be taken only

if it is possible to do so by standing on the floor or

stairs. (Do not stand on furniture or counter tops, etc.)

If the surface is inaccessible enter an "x" in the field;

if the surface does not exist (three walled room for example)

leave the reading field blank. If the surface is not painted

or varnished, enter a U rather than an XRF reading.

TRIM COND - as with the COND field for walls and ceilings , this

field must be for all trim within the room (i.e., windows,

doors, and baseboards).

WINDOW NUMBER - Enter the number of windows in the room (M0" if none)

WINDOW READING - enter the reading obtained from the window frame

or sill; if possible this should be from a vertical surface

within four feet of the floor.

81
DOOR NUMBER - enter the number of doors in the room ("0" if none)

DOOR READING - enter the XRF reading from any point on the door's

surface which is within four feet of the floor. If the door

is of variable thickness, take this reading from the thick part

of the door to avoid the read through problem (detecting lead

on the back surface of the door)

.

BASEBOARD - enter the XRF reading from an accessible part of the

baseboard from any convenient wall of the room. If the baseboard is inaccessible enter' an "x"; if there is no baseboard,

leave blank.

OTHER

FLOOR - enter an XFF reading only if the floor is a painted one.

RADIATOR, CABINET, FIREPLACE - enter XFF readings for each of

these which exists; leave blank for each non-existent entity.

Section III - Exterior

The exterior section differs from the interior in that there is

a condition code for each XRF reading taken. Criteria for each condition

are the same as for the interior. Readings are to be made only if the

designated areas are painted. As with the interior readings, the XRF

readings should be taken at any point at a height of four feet or less.

WALL RDG - the outside wall of the dwelling unit, reading taken

from the predominant exterior surface.

PORCH RDG - the floor of the porch.

DOOR RDG - the exterior door. If the door is of variable

thickness, this reading is taken from the thick portion.

WINDOW RDG - the window frame or sill

.

82
RAILING RDG - the porch or stair railing.

FENCE RDG - the fence.

GARAGE RDG - the garage wall.

EXCLUDED ROOMS - enter the number of rooms of indicated type

which were not inspected.

83
APPENDIX D

SPECIFICATIONS FOR SAMPLE GENERATION PROGRAM

This appendix contains the specifications for a computer program to

generate samples, the listing of the program and an example of the output

generated. For the example, 1023 entries were generated with 320 appearing

in the first sample. The directory assumed has two columns with sixtylines per page; the names appearing beginning on page 6 and run through

page 50; the starting seed (base 8) = 000011111111 012345012345.

Sample Generator

The sample generator creates a set of samples from a hard copy directory.

It is parameterized so that any directory may be used. The parameters:

1. Page number of first page containing addresses (must be >1 and <1024)

2. Page number of last page containing addresses (must be ^1 and <1024)

3. Number of columns per page (must be >1 and <8)

4. Number of lines per page (must be >1 and <512)

5. Size of first sample (must be <8190; the sample produced will be the

least multiple of 160 which is greater than or equal to the size

specified)

The program generates 8191 triples, every triple being a set of randomly

generated indices denoting page, column and line of the directory. Duplicate

triples are eliminated and the first sample set is put into directory sort

and printed. This is the original sample to be used in the survey. The

remainder of the triples are then sorted by forty entry blocks into directory

sort and these sets are printed as extras.

Each of the sample sets thus generated is a random sample as is

any combination of these sets. In order to preserve the validity, if

any entry from a group is used that entire sample set must be used:

84
otherwise a bias occurs (see Section I on sampling).

Because of inconsistencies in the directory (short pages, commercial

addresses, blank lines, etc.) and other problems which may be discovered

in the field (non-existent or non-residential buildings) , extra sets

may have to be added to the survey sample until the reauired number of

units are obtained.

This program consists of a main program (DIRECT) and a subroutine

(SORT) both written in FORTRAN V, and a function subroutine (RAND) in

assembly language. The FORTRAN programs should be readily transportable

to any computer system of sufficient size with word length of 36 bits or

more. On another system, RAND must be replaced by a function which calculates a random variable, x, 0<x<l, with a statement:

X = RANDNO (0, SEED) where SEED is changed within the function subroutine

to advance the random variable with successive calls. The method used

in RAND is the conventional multiplicative one which requires input of

a starting value of SEED and provides for generation of a different

sequence of random variables on subsequent runs by printing the last

value of SEED at run termination.

Required Input

1. Seed § sentinel (Format 3013)

a. High order part of SEED

b. Low order part of SEED

c. Sentinel of all binary l's (377777777777)

2. Directory parameters (Format 515)

a. IPS Starting page

b. IPN Last page

85
c. IC Number of columns/page

d. IL Number of lines/page

e. IFIRST Size of first sample

Output

INPUT PRINT

a

1 a a z

3

6

i So G,

2 3

NEXT VALUE OF SEED y y THERE APE 6 UNIQUE UNITS followed by the

l 2 samples generated.

a is 12 character octal high order part of initial SEED

a is 12 character octal low order part of initial SEED

a is 377777777777 (a - a are print back of seed § sentinel input)

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3

3 is IL

3 is IFIRST (3-6 are print- back of directory parameters.")

5 1 5 Y is 12 character octal high order part of last SEED

Y is 12 character octal low order part of last SEED

<5 is the number of distinct units in the 8191 unit sample after

duplicates have been eliminated.

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103
NBS-114A (REV. 7-73)

U.S. DEPT. OF COMM. BIBLIOGRAPHIC DATA

SHEET

1. PUBLICATION OR REPORT NO. NBS TW-9272. Gov't Accession

No.

3. Recipient's Accession No.

4. TITLE AND SUBTITLE

Survey Manual for Estimating the Incidence of Lead Paint

in Housing

5. Publication Date September 1976

6. Performing Organization Code

7. AUTHOR(S)

William G. Hall and Lillian T. Slovic

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, D.C. 20234

10. Project/Task/Work Unit No.

11. Contract/Grant No.

12. Sponsoring Organization Name and Complete Address (Street, City, State, ZIP)

Division of Energy, Building Technology and Standards

Office of Policy Development and Research

The Department of Housing and Urban Development

Washington, D.C. 20410

13. Type of Report & Period Covered

14. Sponsoring Agency Code

15. SUPPLEMENTARY NOTES

16. ABSTRACT (A 200-word or less {actual summary ot most significant information. If document includes a significant bibliography or literature survey, mention it here.) This manual is intended as a guide for municipal managers in performing a survey to determine the prevalence of lead based paint in their community's dwelling units.

There are four parts to the Manualf each is intended for a different audience.

Part I discusses the preliminary considerations for a survey. It is intended for the

department head or executive who will initiate plans for the survey. It presents a managerial overview of the processes, the cost determinants, criteria for the establishment of objectives and the resources required.

Part II is intended for the survey manager and the inspector supervisors. It contains more detailed information on the planning, staffing, training, and execution of the

data collection phase of the survey.

Part III is for the use of the person responsible for the control and management of

the data collected and for the analysis of these data.

The Appendices contain quite detailed information about procedures we have used in

previous surveys. These may be used as they are described or may be modified or adapted

to meet specific objectives.

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name; separated by semicolons)

Lead paint; lead paint detection; lead paint programs; lead poisoning; portable

x-ray fluorescence; random sampling; x-ray fluorescence.

18. AVAILABILITY [xX Unlimited

22 For Official Distribution. Do Not Release to NTIS

I XQfder From Sup. of Doc, U.S. Government Printing Office Washington, D.C. 20402, SD Cat. No. C13.

rinting <Jtt to 8 931

22 Order From National Technical Information Service (NTIS)

Springfield, Virginia 22151

19. SECURITY CLASS

(THIS REPORT)

UNCLASSIFIED

20. SECURITY CLASS

(THIS PAGE)

UNCLASSIFIED

21. NO. OF PAGES

111

22. Price

$2.20

USCOMM-DC 29042-P74

*U.S. GOVERNMENT PRINTING OFFICE: 1976 210-801/375 1-3
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