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DOE/EIA-0555(96)/1 

Distribution Category UC-950

Energy Consumption Series

Residential Energy Consumption Survey

Quality Profile

March 1996

Energy Information Administration

Office of Statistical Standards

Office of Energy Markets and End Use

U.S. Department of Energy

Washington, DC 20585

This report was prepared by the Energy information Administration, the independent statistical and analytical 

agency within the Department of Energy. The information contained herein should not be construed as 

advocating or reflecting any policy position of the Department of Energy or any other organization.
Contacts

This publication was prepared by the Energy Information Administration (EIA) jointly un.c-.Ki :ie 

general direction of Dr. Yvonne Bishop, Director of the Office of Statistical Standards (OSS) (202 6 

1068) and W. Calvin Kilgore, Director of the Office of Energy Markets and End Use (EMEU; !: ; : 586-1617). The FAX number for all OSS personnel is 202-426-1081.

In EMEU, the project received general direction from Lynda T. Carlson, Director of the Energy 't:i 

Use and Integrated Statistics Division (EEUISD) (202-586-1112) and Nancy L. Leach, Chief o: ::ie 

Residential and Commercial Branch (202-586-1114). The FAX number for all EEUISD persccm- i i ?, 

202-586-0018.

Detailed technical questions on the topics indicated may be referred to the following people:

Renee Miller Project Design 202-426-1117

OSS rmiller@eia.doe,gov

Thomas Broene Project Design 202-426-1108

OSS tbroene@eia.doe.giri. 1

Robert Latta Sampling Design, Sampling Variances 202-586-1385

EEUISD End-Use Estimation, RECS Manager rlatta@eia.doe.gov

Nanno Smith Public-Use Data 202-586-5481

EEUISD nsmith@.eia.doe. g;r,

Hattie Ramseur Publications, Report Production 202-586-1124

EEUISD hramseur@.eia..doe,gi

This report was written by Thomas B. Jabine, under contract to EIA. He received assistance r'"n 

William Connors and Barbara Hammond, Allied Technology Group. Incorporated.

Data in these reports are in the public domain and, with appropriate credit, may be reproduced v,i1f .: 1 

permission. A suggested citation is "U.S. Department of Energy, Energy Information Adminislral ' i: 

Residential Energy Consumption Survey Quality Profile. "

Public-Use Data Diskettes containing RECS data are available through the Office of Scientific id

Technical Information (615-576-8401) and the National Technical Information Service (703-487--<'-6: : ;

For questions about RECS publications, call 202-586-8800. Information, reports, and householr 1 i:l

data files are also available by accessing EIA's Home Page on the Internet at http://www.eig,dn:,[ '

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Tribute

Dr. Wendel L. Thompson, Residential Energy Consumption Survey (RECS) Manager, is retiring 

from Federal service on March 29, 1996. He has guided RECS for 17 years and has been 

instrumental in making it a survey known for its integrity arid high quality data.

It is through his untiring efforts that RECS has continued to evolve and improve over the nine 

survey cycles since 1979. With unfailing good humor, grace, and patience under pressure, 

Wendel has been the friendly and knowledgeable expert answering everyone's questions about 

residential energy usage. Wendel epitomizes the very finest in customer service, which EIA and 

the entire Federal government strives to achieve.

"Mr. RECS" will surely be by his colleagues and all the RECS users. 

We wish Wendel the best in Ms retirement.

Energy information Administration / Energy Consumption Series 

Rasidantial Energy Consumption Survey Quality Profile
Page

1. Introduction ................................................. 1

Purpose and Scope of This Report ............................... 1

Sources of Information About Data Quality ......................... 1

Relation of This Report to Other RECS Publications ................... 2

Structure of the Report ....................................... 3

2. An Overview of .......................................... 5

A General Overview of RECS .................................. 5

Overview of the 1993 RECS Design and Proceduress

7

Evolution of the RECS Design: 1978-1993 ......................... 17

3. Coverage ................................................... 23

RECS Target Populations .................................... 23

Frame Development and Sample Selection Procedures ....,,.,,........ 2.7

Evaluation of Coverage on ............... 30

4. Nonrespon§e ................................................ 35

Nonresponse in the Household Survey ........................... 36

Nonresponse in the Rental Agent Summary ...,.,....,.,.,......... 50

Nonresponse in the Supplier Survey ,..,..,..,...,.,..,.......... 52

Summary ............................................... 55

5. Measurement Error ........................................... 57

Special Data Collection Procedures .............................. 58

Comparisons of Individual Household Data from Alternate Sources ........ 63

Questionnaire and Interviewer Effects on Measurement Error ........... 73

6. Data Processing and ................................. 81

Data Processing Other than Imputation .......................... 82

Imputation .............................................. 95

7. Estimation and Error ,.,...,,,...,..,.,..........,.... 107

Sample Weighting Procedures ................................ 107

End-Use Estimation ....................................... 113

Sampling Errors .......................................... 124

8. Comparisons of RECS .................... 133

Comparisons of RECS and Supplier Survey Estimates of Consumption .... 134

Comparisons of RECS Data on Housing Unit and Household

Characteristics with Data from Other Sources ................... 141

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profite
9. Summary ............................

Principal Sources of Error ..............

Current Research and Potential Design Changes 

Some Suggestions for Data Users ..........

Appendices

A. References ...........................

B. Related EIA Publications on Energy Consumption

Figures

2.1 Sources of Information for the RECS. System ........................... (:

2.2 Key Features of RECS, by Survey Year ............................... :

2.3 Sample Design and Selection Procedures for 1993 RECS ................. K

2.4 Data Collection Modes and Timing: 1993 RECS ........................ U!

4.1 Treatment of Nonresponse by RECS Component ........................ 36

4.2 Design Changes That May Have Affected Unit Response Rates .............. 3 (|

6.1 Steps in Internal Processing of Data from Each Major Source ............... 8;

6.2 Processing Operations Involving More than One Data Source ............... 8<:

6.3 Computer-Assisted Edits Used in RECS .............................. 86

6.4 1990 RECS: List of Topics for Special "Reports ........................ 87

6.5 Processing Computer Edit Outputs ................................. 3f:

6.6 Imputation Procedure for Household Survey Mail and Telephone

Questionnaires: 1993 RECS ................................... 9i;

6.7 Imputation of Annual Household Consumption from Billing Period Data

for Electricity and Natural Gas ................................ 100

6.8 Sources of Error in RECS Consumption Data ......................... 102

7.1 RECS Sample Weighting Procedures: 1993 .......................... 107

7.2 Selected Components of the Nonlinear End-Use Consumption Model for

Electricity Used in the 1993 RECS ............................. 1 :<:

7.3 An Alternative Linear Formulation of the Model Components Shown in

Figure 7.2 .............................................. 12C

7.4 Example of the Use of RECS Row and Column Factors to Derive

Approximate Standard Errors ................................. 1.26

8.1 Sources of Differences Between RECS Estimates of End-Use Consumption 

and EIA Supply Survey Estimates of Energy Supplied to the 

Residential Sector ......................................... 136

9.1 Household Survey Completion Rates: 1978-1993 ....................... 154

9.2 Supplier Survey Eligibility and Completion Rates, by Fuel: 1993 ........... 156

9.3 Sources of Data for Estimates of Total Fuel Consumption,, by Fuel: 1993 ...... 158

9.4 Proportion of Total Energy Consumption, by Fuel: 1993 ................. 158

Energy Information Administration / Energy Consumption Series 

vi Residential Energy Consumption Survey Quality Profile
Page

3.1 Ineligible Housing Units in by 19784993 ............ 27

3.2 Sample Counts of New Units by Group: 1984, 1987,

and 1990 ............................................ 31

3.3 Benchmark Adjustment Factors for of Households,

by Census Region: 1990-1993 ................................. 32

3.4 1990 RECS and Census Bureau of Units by Year

Built: 1980-1990 .......................................... 33

4.1 Household Survey Eligibility and by Survey Year: 1978-1993 ... 37

4.2 Household Survey Response by Year and Prior

Status: 1982-1990 \ ........................................ 40

4.3a Household Survey Response by Urban and

Structure.Type: 1979-1993 .....'....................-.....-... 43

4.3b Household Survey Response Rates by Urban and

Structure Type: 1981-1993 ................................... 44

4.3c Household Survey Response by Urban Status, and

Structure Type: 1981-1993 ................................... 45

4.4 Ten Variables with Highest Item 1990 RECS ........... 47

4.5 Housing Unit Survey, Completeness

Information: 1980-1993 ...................................... 49

4.6 Rental Agent Survey Eligibility and by

Survey Year: 1979-1993 ..................................... 51

4.7 Supplier Survey Eligibility and by Fuel and Survey

Year: 1980-1993 .......................................... 53

4.8 Supplier Survey Eligibility for and Gas

by.Structure Type: 19804984 ................................. 54

5.1 Comparison of NIECS and Energy Data on Floorspace .......... 59

5.2 Results of Matching Nameplate Data Against AEJ Directories

to Obtain Seasonal Energy Efficiency 1990 RECS .............. 60

5.3 Differences Between Responses by the Same Household in

the 1980 and 1982 ...',,.., J. .......................... 64

5.4 Differences in Square Footage Reported for the Same Household

in the 1980 and 1982 .................................. 66

5.5 Longitudinal Households: Housing Type Reported in 1984 and 1987 RECS .... 67

5.6 Longitudinal Households: Year of Construction Reported in 1984

and 1987 ........................................... 67

5.7 Changes Resulting from Comparison of Rental Agent and Household

Survey Responses: 19804987 ................................. 69

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profite vii
Paf: ;i

5.8 Comparison of Heating Degree-Days Using NOAA Division Method Versus

Station Method, April 1984 Through March 1985 .................... 7 

5.9 Percent of Household Survey Interviews with Imputation or Edit Changes

Subsequent to Data Entry, by Interviewer Experience: 1987 vs. 1990 ...... 1''.

5.10 Interviewer Experience and Extent of Edit and Imputation Changes.

by Type of Household: 1990 RECS ............................

6.1 1984 RECS: Changes to the Household and Billing Files, by Reason ........

6.2 1984 RECS: Percent Distribution of Changes for Each Process, by Reason ..... 9 !

6.3 Changes to the Household File by Source of Information or by Reason:

1987 and 1990 RECS ..................,...,..'.............. 9-

6.4 Imputation Methods Used for Household Survey Variables: 1981-1993 ........ 9n

6.5 Basis of Estimates of Annual Consumption: 1990 and 1993 RECS

(Percent of Total Consumption of Each Energy Source) .............. I 3 :

7.1 Number and Percent of Sample Households Used in Regression Analyses,

by Fuel Type: 1990 RECS ................ !". ................ 111

7.2 Percentage Difference Between Modeled and Submetered End-Use Estimates . . 124

7.3 Comparison of Standard Error of Difference Estimated by Two Methods for

Changes in Average Consumption per Household Between Survey Years . . . 128

7.4 Sampling Errors for Estimates of Average Consumption per Household:

1993 RECS ............................................. 13

7.5 Design Effects for RECS, Using Optimum and Actual Cluster Sizes ......... 131

8.1 Residential Consumption and Supply of Electricity, Natural Gas,

and Fuel Oil: 1978-1993 .................................... 13!:i

8.2 Residential Consumption and Supply of Kerosene and LPG: 1990 and 1993 . . . 139

8.3 Residential Consumption and Supply of Selected Fuels, Middle Atlantic

Division: 1984, 1987, and 1990 ............................... 14C

8.4 Estimates of Electricity Consumption per Residential Unit from RECS

and the Edison Electric Institute: 1970-1984 ...................... !<- (,

8.5 RECS/AHS Comparisons of Occupied Housing Units by Year Built: 1993 .... [- ---

8.6 RECS/CPS Family Income Comparisons: 1987 and 1990 ................. 14f

8.7 Comparison of Aggregate Expenditures for Selected Fuels, Consumer

Expenditure Survey and RECS: 1984, 1987, and 1990 ................ 14?

8.8 Comparison of Number of Households Receiving Food Stamps. RECS

Estimates and Program Counts: Selected Years .................... 14!*

8.9 Comparison of Number of Households Receiving Low-Income Home Energy

Assistance, RECS Estimates, and Program Counts: Selected Years ....... '.A9

Energy Information Administration / Energy Consumption Series 

viii Residential Energy Consumption Survey Quality Profile
of

The Residential Energy Consumption Survey (REGS) is a periodic national survey that provides 

timely information about energy consumption and expenditures of U.S. households and about 

energy-related characteristics of housing units. The survey was first conducted in 1978 as the 

National Interim Energy Consumption Survey (NIECS), and the 1979 survey was called the 

Household Scree'ner Survey. From 1980 through 1982 RECS was conducted annually. The next 

RECS was fielded in 1984, and since then, the survey has been undertaken at 3-year intervals. 

The most recent RECS was conducted in 1993.

Purpose and Audience

The purpose of this RECS Quality Profile is to present, in a convenient form, a report on what 

has been learned about the quality of RECS data since the survey began. In a broad sense, the 

term "quality" covers the relevance, timeliness, and accuracy of the survey estimates. The 

emphasis here will be placed on "accuracy." The report provides information about sampling and 

nonsampling errors, focusing on the latter. It discusses the types and sources of errors that occur 

and their possible effects on interpretation of RECS data, especially when used for longitudinal 

analysis. This information should be helpful to users of RECS data, to those responsible for the 

design and operation of the survey, and to persons with general interest in survey design and data 

quality. The final section of Chapter 9 provides specific suggestions for data users on how to 

gain access to RECS data and use them for cross-sectional and longitudinal analyses.

Scope of the Report

This report includes information about the quality of data from all of the surveys conducted to 

date, starting with the 1978 NIECS through the 1993 RECS. As background, a summary of the 

survey design and procedures for the 1993 RECS is provided, as well as a description of qualityrelated design changes from 1978 through 1993. The report does not cover the Residential 

Transportation Energy Consumption Survey (RTECS), a separate survey that has been conducted 

for a subsample of RECS households in the year following each RECS data collection. The final 

chapter describes some ongoing methodological research and some anticipated design changes for 

the 1996 RECS.

of About Data

This report draws on both published and unpublished sources of information. Following 

established practices of the Energy Information Administration (EIA), all RECS publications have 

included a substantial amount of information about survey procedures and about the quality of 

the data. Appendices to the reports on Housing Characteristics and Household Energy

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 1
Consumption and Expenditures for all survey years have been a primary source of information 

for this report. It also draws on numerous survey documentation reports, contractor report:?., 

papers presented at conferences, and internal memoranda. These sources are cited in the text of 

the report and as sources for information presented in tables by showing the author's name and. 

the year of publication. A full list of all references cited follows Chapter 9.

Direct estimates of the sampling error associated with most REGS data are available and form 

the basis for row and column variance factors that have been included in the data tables found 

in the basic REGS reports, starting with the 1984 consumption and expenditures report (SI A. 

1987a). By using these row and column factors, data users can arrive at an estimate of the 

sampling error associated with the value found in each cell of the table.

Most available information about nonsampling errors is less direct. For example, there is detailed 

information about household arid item nonresponse rates for each survey, but it is seldom possible 

to determine the resulting level of bias in the survey estimates that are produced after missing 

values have been imputed and weighting adjustments have been applied in an attempt to minimize 

the effects of nonresponse. Similarly, estimates for the same variable from two different sources 

can be compared, but without detailed analysis and reconciliation of differences between 

individual reporting units, it is generally not possible to be certain which of the estimates is more 

nearly correct. Nevertheless, the analysis of both direct and indirect indicators of nonsampling 

error can contribute in significant ways to understanding and interpreting the survey results and 

to efforts to improve quality.

Information about nonsampling errors comes from several sources:

 Operational or performance data, such as unit and item, nonresponse rates, imputation 

rates, and weighting adjustments

 Methodological experiments and pretests of survey procedures

 Micro-evaluation studies, such as callbacks or reinterviews of respondents who have 

reported unusual values and energy audits or assessments of sample housing units by 

specially qualified persons

 Macro-evaluation studies, such as comparisons of REGS data on energy consumption 

with data from EIA surveys of energy suppliers, and RECS data on household and 

housing-unit characteristics with data from the Census Bureau's Current Population 

Survey, American Housing Survey, and Survey of Construction

Relation of This Report to Other RECS Publications

As noted above, the basic publications for each survey have appendices that describe the design 

and procedures for that survey and provide some information about the quality of the data. More 

detailed information about survey operations can be found in the operations, procedures, and data. 

user's manuals that have been prepared for each survey, starting with 1982. A recent publication

Energy Information Administration / Energy Consumption Series 

2 Residential Energy Consumption Survey Quality Profile
provides a fuller description of the sample design for each survey through 1993 and changes over 

time (EIA 1994). A 198? EIA publication. Trends in Consumption and Expenditures, 1978-1984 

(EIA 1987b) contains longitudinal data and a discussion of how their interpretation may be 

affected by changes in survey design, content, and procedures.

The particular goal of this RECS Quality Profile is to serve both users and survey designers and 

managers by providing a more systematic and complete presentation of information about data 

quality for all of the surveys from the 1978 NIECS through the 1993 RECS. To accomplish this, 

the report follows a structure similar to the one that has been developed in recent years for 

presenting information about the quality of data from other major household surveys, such as the 

Survey of Income and Program Participation and the Schools and Staffing Survey (Bureau of the 

Census 1990, National Center for Education Statistics 1994).

of the

Chapter 2 provides an overview of RECS, with a description of the 1993 RECS design and 

procedures and of the quality-related design changes that have occurred since the first survey in 

1978. Chapters 3, 4. and 5 present information about the three major sources of nonsampling 

error: coverage error, nonresponse, and measurement error, respectively. Chapter 6 discusses 

the contributions to nonsampling error of data processing and imputation procedures. Chapter 

7 looks at the effects of estimation procedures on data quality and reviews the effects of sampling 

error on interpretation of the data.

Chapter 8 presents the results of studies that have compared RECS data with data from EIA 

surveys of energy suppliers and with data collected by other organizations, especially the Census 

Bureau. Chapter 9 summarizes the effects on RECS data of the principal sources of error. It also 

describes relevant research currently in progress and quality-related design changes planned or 

being considered for the 1996 RECS. It concludes with suggestions for users about how to take 

account of data quality in their analyses of RECS data. A list of all references cited follows 

Chapter 9. Appendix A lists ail EIA publications related to RECS.

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profiie
2. An Overview of

For those who may be unfamiliar with the nature and principal features of the Residential Energy 

Consumption Survey, this chapter of the Quality Profile presents some background information. 

The first section provides a general overview of RECS, its objectives, and the timing of the 

periodic surveys. The second section describes the design and methodology of the 1993 RECS. 

The final section identifies significant changes as the survey design and procedures evolved from 

the initial survey (known as NIECS, the National Interim Energy Consumption Survey) in 1978 

through the 1993 RECS.

A Genera! Overview of RECS

Objectives

RECS, the Residential Energy Consumption Survey, is a periodic sample survey that is designed 

to provide timely information about energy consumption and expenditures of U.S. households and 

about energy-related characteristics of these households. RECS data are developed for use by the 

Congress, Government agencies, researchers, and the general public. The data provide major 

inputs to EIA's National Energy Modeling System, a forecasting system that has been developed 

for the Department of Energy. In the 1981, 1984, 1987, and 1993 survey years, RECS included 

a supplemental sample of low-income households to provide information needed by the 

Administration for Children and Families, Department of Health and Human Services, to provide 

data needed to administer its Low-Income Home Energy Assistance Program.

Legal Authority for Survey

RECS is conducted under the authority of the Federal Energy Administration Act of 1974 (Public 

Law 93-275), as amended, and the Energy Policy Act (EPACT) of 1992. The latter act directs 

the EIA to conduct a survey of residential energy use at least once every 3 years.

Data Dissemination

Beginning with the 1980 RECS, the data from each survey have been presented in two major 

publications, a Housing Characteristics 1993 report and a Household Energy Consumption and 

Expenditures 1993 report. Anonymized data files with information for individual households 

have been released to researchers and other data users on public-use tapes for all RECS survey 

years and on diskettes for the 1987 and subsequent surveys. Since August 1995, it has been 

possible for users to download the data files for the 1987, 1990, and 1993 RECS from the 

Internet by contacting the EIA home page (http://www.eia.doe.gov).

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Sources of Data

The information contained in the RECS data files and publications comes from several source;; 

(Figure 2.1). The primary data source is the Household Survey, in which data are collected, 

mostly via personal interviews, from a sample of several thousand households. For most of the^ 

households, a Supplier Survey is undertaken to obtain billing information on energy consumption 

and costs from the households" suppliers of electricity, natural gas, fuel oil. kerosene, and LPG 

For some households occupying, rental units in multiunit structures, a Rental Agent Survey h 

conducted to improve the accuracy of data on the main fuel sources and types of equipment used 

for space and water heating and air-conditioning.

For every sample household, information on heating and cooling degree-days as measured by -,.. 

nearby weather station (and humidity, starting in 1990) is obtained from the National Oceanic: and 

Atmospheric Administration (NOAA). Finally, data from the Census Bureau's decennial censuses 

of population and housing and Current Population Survey are used in the RECS sample desi.gr 

and estimation procedures to improve the reliability and precision of the survey results.

Figure 2.1. Sources of Information for the RECS System

Source Information Provided 

(preferred source)

Household Type Fallback Source

Household survey 

Supplier survey

Rental agent 

survey

NOAA 

Census Bureau

Housing-unit and household 

energy-related characteristics

Housing-unit consumption 

and expenditures by fuel type.

Main fuel source for space 

and water heating, cooking, 

air-conditioning

Weather data for station close 

to each sample housing-unit.

1. Data for formulation 

of sample strata.

2. Household estimates 

for benchmarking 

RECS estimates.

All housing-units

Households that pay 

supplier directly for 

one or more 

delivered fuels

Households in multiunit structures with 

one or more fuels 

included in rent

All housing-units

Household survey 

(kerosene only).

Household survey

Source: Energy Information Adrrrnistraiion, Consumption and Expenditures (February "993)

Frequency, Reference Periods, and Sample Sizes

The survey has been conducted nine times between 1978 and 1993. As shown in Figure 2.2, h: 

was conducted annually from 1978 to 1982, then 2 years later, in 1984, and subsequently at

Energy Information Administration I Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
3-year intervals. Data collection, for the Household Survey has started in the autumn of each 

survey year and has continued through the winter or early spring of the following year. For the 

first 8 survey years, through 1990, the "reference month" for households has been November of 

the survey year. For the 1993 REGS, July 1993 was chosen as the reference month. RECS 

estimates of the number of households are benchmarked to estimates of households for the 

reference month from the Census Bureau's Current Population Survey. Through the 1984 surveyyear, the reference period for consumption and expenditures was the 12-month period starting in 

April of the survey year and extending through March of the following year. From survey year 

1987 on, the reference period has been the calendar year coinciding with the survey year.

Sample sizes are the numbers of sample units initially assigned to interviewers that turned out 

to be eligible for the survey and for which acceptable questionnaires were obtained by field or 

telephone interviews or by mail (Figure 2.2). The numbers of units initially assigned and the 

corresponding eligibility and response rates achieved may be found in Chapter 4, Table 4.1. The 

larger samples in 1981, 1984, 1987, and 1993 were due in part to the inclusion of supplemental 

samples of households in low-income areas, as noted above. The sample for 1993 was also 

supplemented to provide a larger sample of newly-constructed units, defined as those built in 

1987 or later.

Figure 2.2. Key Features of RECS. by Survey Year

Survey

Year

1978

1979

1980

1981

1982

1984

1987

1990

1993

Reference Month3

for Households

November

November

November

November

November

November

November

November

July

Reference Period

for Consumption

and Expenditures

Apr 78 - Mar 79

Apr 79 - Mar 80

Apr 80 - Mar 81

Apr 81 - Mar 82

Apr 82 - Mar 83

Apr 84 - Mar 85

Jan 87 - Dec 87

Jan 90 - Dec 90

Jan 93 - Dec 93

Sample

Sizeb

(Households)

4,081

4,033

6,051

6,269

4,724

5,682

6,229

5,095

7, 11 1

 Month of survey year for which number of households was estimated.

^Number of sample households for which acceptable questionnaires were obtained.

Sources: Energy Information Administration, Housing Characteristics (1980-1993); Consumption and Expenditures (1978-1993).

Overview of the 1993 RECS Design and Procedures

This section summarizes the main features of the 1993 RECS design and procedures. More 

detailed information is provided in Appendix A, "How the Survey Was Conducted," in the 

Housing Characteristics 1993 report (EIA 1995a), the comparable appendix in the Household 

Energy Consumption and Expenditures 1993 report (which includes information about the 

Supplier Survey, EIA 1995d), and the various procedures manuals developed for the 1993 RECS 

(for example, Response Analysis Corporation 1995a,b).

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Content

The data collected for households and housing-units in the 1993 RECS can be classified ink: 

seven major categories:

1. Energy consumption and expenditure by the household

2. Housing-unit characteristics, equipment, and appliances most directly related to energy 

use

3. Socioeconomic characteristics of the household occupying the housing-unit

4. Energy sources, uses and suppliers

5. Ownership and. use of vehicles

6. Use of energy assistance programs

7. Participation in demand-side management programs of utility companies.

Providing an estimate of energy consumption and expenditure by source for the household and 

by end use within the household is the primary goal of RECS. Data in the remaining six 

categories serve several purposes. In addition to being of interest in their own right, variables 

such as the year the structure was built, the number of rooms in the housing-unit, the ares, of 

heated floorspace, and household income can be used as classifiers, in presenting and analyzing 

estimates of energy consumption and expenditures. Socio-economic data for the household and 

data on housing characteristics, equipment, and facilities are extensively used in the imputation 

of missing consumption data and in the model-based allocation of the consumption of each iiie' 

to end-use categories. Data on ownership and use of vehicles are used primarily as inputs to the 

Residential Transportation Energy Consumption Survey and as the basis for selecting a subsample 

of RECS households for that survey.

Although there has been substantial variation in specific items within each category, data in the 

first five categories have been collected in all RECS surveys since 1980. Inclusion of question:; 

about the use of energy assistance programs began in the 1981 RECS, coincident with the 

Congressional authorization of the Eow-Income Home Energy Assistance Program (EIHEAP^ in 

that year. Participation in demand-side management programs (activities sponsored by utilities 

that are intended to influence the timing and amount of electricity use) was the subject of two 

questions in the 1990 RECS. Detail on this subject was substantially expanded in the 1993 

Household Survey questionnaire, and utilities were asked in the; 1993 Supplier Survey to repon 

on the programs they offer to residential customers.

In 1992, the EIA conducted a user-needs study to help determine the content of the 1993 RECS 

(EIA 1993c). Written suggestions were solicited from data users and EIA staff held 15 meetings 

with different user groups. The findings from that study, as well as specific requirements

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
mandated by the Energy Policy Act of 1992, led to several changes in content. Important topics 

covered by questions added to the 1993 RECS questionnaire were:

e Consumer decision-making behavior, such as purchases of new equipment and the 

influence of energy efficiency considerations on purchase decisions

 Additional building envelope and thermal characteristics, such as exterior wall 

materials and number and type of windows

 Lighting: Number of lights. location, bulb type, wattage and control mechanisms; i.e., 

a more detailed lighting supplement was administered to a subsampie of households

 Appliance Usage and Equipment: Levels of usage, especially of equipment using hot 

water, such as washers and dishwashers; identification of some appliances not 

previously included on the questionnaire

9 Emerging Technologies: Awareness and use of several new energy-conserving 

technologies, such as low-E window glass and halogen light bulbs

 Business use of homes.

In order to accommodate these new questions without undue increase in response burden, several 

questions that appeared on the 1990 RECS questionnaire were dropped, taking into account 

expressions of user interest, past experience in the ability of respondents to answer accurately, 

and demonstrated utility of specific questions for imputation of missing data and estimation of 

energy consumption by end use. Several questions on space heating, insulation, and other 

housing characteristics were among those dropped. A more detailed account of these changes is 

provided in the supporting statement submitted to 0MB for clearance of the forms (ElA 1993b).

Sample Design

All of the RECS surveys have used a stratified, multistage sample design. Basic principles that 

guide the sample design are: use of probability sampling at all stages of selection; the ability 

to produce estimates of acceptable reliability for each of the nine Census divisions and for other 

subgroups of the target population; and, at the national level, determination of sample sizes at all 

levels in a way that produces the most reliable estimates possible, given the resources available 

for the survey. For the 1993 RECS, additional requirements were to oversample newly 

constructed housing-units and units occupied by households with income below the poverty level, 

that is, to sample them at rates higher than those used for units not in these categories,

The types of sampling units and the sample selection procedures for each stage of sampling in 

the 1993 RECS are important (Figure 2.3). The grouping of primary sampling units (PSU's) into 

strata was carried out within Census divisions, and, within divisions, separately for each of four 

States with large population (California, Florida, New York, and Texas) and for Alaska and

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile
Hawaii because of their unique weather conditions. Of the 116 strata, 31 consisted of a si 

metropolitan area that had a large enough population to form a stratum by itself.

The 1993 sample was the first one for which 1990 Census data were available for use ir 

stratification of PSU's and secondary sampling units (SSU's) within sample PSU's, and to 

assigning selection probabilities at each level. Although a new set of PSU's was selected. fc> 

purposes of efficiency this was done in a manner that maximized the overlap with the sample 

PSU's used in the 1984, 1987, and 1990 surveys. Of the 116 sample PSU's selected for the 199: 

RECS, 94 had been included in the three preceding surveys,

Figure 2.3. Sample Design and Selection Procedures for 1993 REGS

DEFINITIONS 

Primary Sampling Units (PSU's):

Metropolitan areas or groups of counties in non-metropolitan areas. 

Secondary Sampling Units (SSU's):

One or more Census blocks, with a minimum of 50 housing-units i.n the most recent census. 

Listing Segment:

A complete SSU or a selected part of an SSU for which a deta^ed listing of street addresses is 

prepared.

Ultimate Cluster:

A small group of (potential) housing-unit addresses selected from the listing fora listing segment.

SAMPLE SELECTION STEPS

1. Divide the United States into PSU's, each consisting of a metropolitan area or one or more normetropolitan counties.

2. Group PSU's to form strata {some strata consist of a single large metropolitan area).

3. Select one PSU from each stratum.

4. Select several SSU's from each sample PSU.

5. (Larger SSU's only) Divide SSU into listing segments and select one.

Q. Prepare a detailed address listing for each SSU or listing segment.

7. Select ultimate cluster of addresses from each SSU or listing segment,

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10 Residential Energy Consumption Survey Quality Profile
Figure 2.3. Sample Design and Selection Procedures for 1993 REGS (Continued)

SAMPLING FRAME AND SAMPLE SfZE INFORMATION 

PSU's in United States: 1,786 

Strata: 116 

Sample PSU's: 1 per stratum 

Sample SSU's in sample PSU's:

Core sample: 1,461 

Supplement : 149 

Total: 1,610 

Mean per sample PSU: 13.9 

Listed units assigned to field:

Total: 9,869 

______Mean per sample SSU: ______ 6.1

Source: Energy Information Administration, Housing Characteristics (1993).

The basic criterion for assigning selection probabilities at each stage of sampling was to produce 

a national self-weighting sample of housing-units, that is, one for which the overall selection 

probability of each unit, taking all stages of sampling into account, would be the same. However. 

some departures from this criterion were necessary in order to obtain the desired oversampling 

of new houses and low-income households, and to ensure that sufficiently reliable estimates could 

be made for each Census division.

Additional information about the 1993 RECS sample design and selection procedures will be 

found in Appendix A of the Housing Characteristics 1993 and the Household Energy 

Consumption and Expenditures 1993 reports and in the Sample Design Procedures Manual 

(Response Analysis Corporation 1995a) for the 1993 RECS. The publication Sample Design fatthe Residential Energy Consumption Survey (EIA 1994) provides information about sample design 

and selection procedures for all survey years and about how they have changed over the life of 

the survey.

Data Collection Procedures

An overview of the timing and collection modes used in collection of data for the 1993 RECS 

Household, Rental Agent, and Supplier Surveys is important (Figure 2.4). Data collection began 

in October 1993 with personal interviewing for the Household Survey. As shown in Figure 2.4,

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 11
there were three waves of personal interviewing. In the first wave, interviewers made a minimum 

of four attempts (initial attempt plus three callbacks), sometimes several more, on various day-; 

of the week and at various times of day, to try to establish contac: with all assigned households 

In the second wave, a similar effort was made, usually by different interviewers, in an attemp: 

to contact households that had not been available during the first wave and to convince some o: 

those that refused interviews in the first wave to reconsider.

Figure 2.4. Data Collection Modes and Timing: 1993 RECS

Survey

and

Wave

Household Survey

Field Wave 1 

Field Wave 2 

Field Wave 3

Abbreviated Version

Collection 

Mode

Collection 

Period

Start End Remarks

Personal interview 

Personal interview 

Persona! interview

Telephone interview

Oct 93 

Jan 94 

Feb 94

Apr 94

Apr 94 

Apr 94 

Apr 94 Selected sample 

sites with low 

response in Wave 

1 and 2.

May 94 Authorization form;; 

collected by mail.

Abbreviated Version 

Rental Agent Survey 

Supplier Survey

Mail 

Telephone

Mail, with telephone 

followup

Apr 94 May 94

Jul 94 Jul 94

Mar 94 Oct 94

Sources: Energy Information Administration, Housing Characteristics (1993); Consumption and Expenditures (1993).

The third wave was undertaken to try to improve response rates in selected locations that had. low, 

completion rates after the second wave.

The telephone and mail phases of data collection for the Household Survey had two goals: ( [ ] 

to collect, for each household for which a personal interview had not been completed, a limited 

amount of information about housing-unit and household characteristics and (2) to obtain 

permission to contact energy suppliers for billing information on their household's consumption 

and expenditures. Telephone contacts were limited to those households for which telephone 

numbers were already available or could be obtained from reverse directories. Households 

responding to telephone interviews were mailed authorization forms for the Supplier Survey :o 

sign and return, and reminder calls were made to those whose fomis had not been received

12

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Residential Energy Consumption Survey Quality Profile
Results of the telephone phase were:

Eligible households 786

Telephone interviews completed 197

Authorization forms returned 74

Interviewed, no authorization form required 4

Only the last two groups were considered to represent completed households; thus, additional 

interviews were generated for 78 households, or 10 percent of those eligible, A similarly 

abbreviated mail questionnaire was mailed to most of the remaining nonrespondents. Completed 

questionnaires were received from 115 (7.5 percent) of the 1,528 households to whom they were 

sent.

Eligibility for the Rental Agent and Supplier Surveys was determined on the basis of information 

collected in the Household Survey, so data collection for those surveys could be undertaken only 

after data collection for the Household Survey was at or close to completion. The Rental Agent 

Survey was conducted entirely by telephone, in two stages. In the first stage, the survey 

contractor's telephone interviewers called to verify each rental agent's address, in order to mail 

an advance letter explaining the nature and purpose of the survey. In the second stage, data for 

the survey were collected by telephone. The Supplier Survey was conducted by mail. 

Information about eligibility and completion rates for these surveys is presented in Chapter 4.

The quality of data collected in personal interviews is affected by the qualifications, training, 

working conditions, and supervision of the interviewing staff. There were 234 interviewers who 

completed one or more personal interviews in the 1993 RECS. Of these, 35 percent had 

completed interviews in a prior RECS. Nearly all of the interviewers were trained in 3-day 

regional training sessions; a small number were trained in a 2-day session or by telephone. M 

the end of the training, each interviewer took an open book quiz, which was reviewed 

immediately thereafter.

Field interviewers were paid on an hourly basis for all time spent working on the survey and 

were reimbursed for their travel expenses. Contractor staff reviewed the first two interviews 

completed by each interviewer and provided extensive written feedback. Additional telephone 

training was provided when warranted. A 20-percent sample of the interviews was verified by 

telephone or mail to ensure that they had been conducted as intended.

Prior to each stage of telephone calls for the Rental Agent Survey, interviewers and their 

supervisors were briefed at the survey contractor's telephone center by the project manager. The 

project manager or a trained telephone supervisor monitored the first several calls made by each 

interviewer and the interviewers were monitored intermittently for the duration of the calling 

period (ElA 1995b).

Energy Information Administration / Energy Consumption

Residential Energy Consumption Survey Quality 13
Data Processing and Imputation

The 1993 RECS data collection operations produced information, mostly in hard-copy forms; 

from six sources: (1) completed Household Survey interview questionnaires, (2) complete:!. 

Household Survey telephone and mail questionnaires, (3) Housing-Unit Record Sheets, (4) R.err:;: 

Agent Survey questionnaires, (5) Supplier Survey questionnaires, and (6) weather data frcr: 

NOAA. In a complex set of operations, data from these six sources were processed, frs, 

individually and then in merged files, to produce the final RECS data tapes. A brief overviev, 

of these operations is provided here; a more extensive treatment, with emphasis on qimlby 

related aspects, will be found in Chapter 6, Data Processing and Imputation and Chapter '/ 

Estimation and Sampling Error.

Initial processing of inputs from each of the first five sources involved three major steps: (I 

check-in and manual review; (2) data entry; and (3) computer-assisted editing. All data entry 

operations were 100 percent verified. Telephone calls to respondents were made as needed to 

resolve inconsistencies identified in the manual reviews. In the computer-assisted edits, problem 

were resolved first by referring back to the questionnaires and then, when necessary, by cal:.im : 

respondents. The inputs from the sixth source, NOAA, were data files that were manipulated: to 

produce the desired weather information to be associated with each sample household.

Some imputation of missing data, especially for items missing from the Household Survey 

interview questionnaires, was carried out prior to merging the files with data from other sources 

Missing values were imputed for about two-thirds of the Household Survey variables, with "h:r 

deck" imputation being the method most frequently used. Other kinds of imputation required the. 

presence of data from more than one source; for example, imputation of items not included or 

the telephone and mail questionnaires required that the files of those questionnaires be merged 

with a set of potential "donor records" from the personal interview data file.

Following is a brief summary of some of the special features of processing relating to each o: 

the six data sources:

 Household Survey Interview Questionnaires. In addition to consistency and range checks 

the computer-assisted edits included several "special reports" in which computer listing! 

with identifiers and selected data items were produced for housing- units with certair 

unusual characteristics. The listings and corresponding questionnaires were reviewed 

respondents were contacted when necessary, and all changes were entered on the data file 

A few examples of the many topics covered by special reports are: households that die 

not use any heating fuel; households with incompatible combinations of heating fuel am: 

main equipment; and households that reported presence of a heat pump but no central airconditioning. An elaborate set of computer runs was used to convert recorded, lineal 

measurements to estimates of total and heated areas and to iranslate from inside to outside: 

measurements where the former had been recorded by the interviewer.

« Household Survey Telephone and Mail Questionnaires. The items not included on the 

abbreviated telephone and mail versions of the questionnaire were imputed, based or, 

random selections from, eligible "donor" households from the set of interview

Energy Information Administration / Energy Consumption Series 

14 Residential Energy Consumption Survey Quality Profile
questionnaires. The donor households were those that matched on a set of variables 

common to the complete and abbreviated questionnaire versions.

Housing-Unit Record Sheets. For each assigned field interview, the interviewer completed 

a Housing-Unit Record Sheet (HURS) with his or her name and ID number, the type of 

living quarters and occupancy, the date. time, and outcome of each visit, and other 

particulars. These HURS forms were used, along with the original sample control file and 

information from other sources, to create the HURS file, which contained basic 

information about the outcome of data collection for each assigned sample address. 

HURS file records were also created for additional housing-units identified within sample 

addresses or by the application of the "half-open interval check" (see Chapter 3. Section 

3.2), The HURS file had several uses, including the generation of reports on interview 

completion rates and interviewer characteristics and the development of weighting factors 

to adjust for unit nonresponse.

Rental Agent Survey Questionnaires. The Rental Agent Survey covered households in 

multiunit structures that had one or more fuels included in their rent payments. Records 

created from the questionnaires were matched to the Household Survey data files for the 

same housing-units. Responses for items common to both surveys, such as year of 

construction and main heating fuels and equipment, were compared and changes were 

made to the Household Survey data whenever it was judged that the rental agent was 

more knowledgeable than the household respondent.

Supplier Survey Questionnaires. For most households, the Supplier Survey is the 

preferred source of RECS information on household consumption and expenditures. After 

data entry, data from this source underwent a complex series of processing steps. A 

separate data file was created for each of the five fuels covered: electricity, natural gas. 

fuel oil, kerosene, and LPG. A series of edits was performed on the data that had been 

reported by month or another billing period, and the edited data were used to arrive at 

annualized estimates for all respondents who had adequate monthly data. These estimates 

were compared with model-based estimates based on household characteristics, and large 

differences were investigated. Similar model-based estimates were used to develop 

estimates of total consumption and expenditure by fuel for households not eligible for the 

Supplier Survey and those for which usable data were not obtained in the Supplier 

Survey. Finally, total consumption and expenditure of each fuel was allocated to end-use 

categories on the basis of a nonlinear regression model.

Weather Data. Temperature data from all official U.S. weather stations were purchased 

on data tapes from NOAA. Based on analyses of proximity factors and the quality of the 

weather data, a specific weather station was associated with each RECS SSU. 

Temperature data for the selected SSU's were used to calculate heating and cooling 

degree-day estimates. The estimates for each SSU were added to the household and 

billing data files for all households located in that SSU. As one step in processing the 

Supplier Survey data, monthly estimates of degree-days were used to develop annual 

estimates for housing-units for which only part-year data were reported. For humidity.

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 15
the data were keyed from aNOAA publication and linked with REGS sample househol: 

at the PSU level.

Estimation

As in earlier surveys, the estimation procedures used in the 1993 RECS had four goals:

1. To reflect the overall selection probability of each sample housing-unit by :h> 

application of appropriate weights

2. To minimize bias resulting from unit nonresponse by the application of welsh: 

adjustments to groups of sample households with similar characteristics

3. To minimize sampling variance by the use of ratio estimates based on data available 

for all PSU's. whether or not included in the sample

4. To minimize bias resulting from undercoverage by benchmarking the RECS estimate:; 

of number of households to more precise estimates derived from the Census Bureau: 

Current Population Survey.

The overall weight developed for each sample housing-unit reflects the joint effects of sample 

weighting (Item 1 above), nonresponse adjustment (Item 2), and ratio estimation to reduce 

sampling variance and bias (Items 3 and 4).

Sampling errors for most estimates included in the published reports from RECS are estimat;:; 

by the use of a balanced half-sample replication method. The results for individual data cells ; IT 

used to develop generalized variance factors that are presented in the publications. Estima1.iot 

procedures and sampling errors are discussed in greater detail in C'hapter 7.

Data Dissemination

The Housing Characteristics 1993 report for the 1993 RECS was released in June 1995 arid tin.. 

Household Energy Consumption and Expenditures 1993 report was released in October 19^5 

Preparation of public-use data files with housing-unit data from all sources requires variou: 

deletions and other changes to ensure that the identity of individual housing-units and householdcannot be determined by users. For example, all geographic identifiers other than Census regior 

and division are removed. These anonymized public-use files are made available on magnevir 

tape for use with main frame computers and on diskettes for use with personal computers. T"h< 

public-use files for the 1993 RECS were sent to the National Technical Information Service i'o 

distribution in August 1995 and, as noted in Chapter 1, were also made available for downloads: u 

via Internet at that time.

Energy Information Administration / Energy Consumption Series 

13 Residential Energy Consumption Survey Quality Profile
Cost of the Survey

The total cost of the 1993 RECS was approximately $3,965,000 over a 3-year period. This 

includes $475,000 provided by the Administration for Children and Families to support the 

collection of data used in the administration of the Low-Income Home Energy Assistance 

Program. The 1993 RECS was the first to use the sampling frame redesigned with the use of 

1990 Census data. The cost of the redesign attributed to RECS was $556,000, The sampling 

frame is updated once every 10 years and the latest update will be used for at least two additional 

RECS.

The total cost of the 1990 RECS, exclusive of any cost arising from the redesign of the sample 

after the 1980 Census, was $2,115,000. The percentage breakdown of the total cost by major 

survey activities was:

Activity Percent of Total

Planning and administration 5.2 

Sample selection, including field listings and updates 18.3

Interviewer training 10.4

Household Survey data collection 28.4

Household Survey data processing 18.9

Rental Agent Survey data collection and editing 1.1

Supplier Survey data collection 5.9

Supplier Survey data processing 8.5

Documentation 3.3

Total 100.0

Evolution of the **=&

Many of the basic design features of the 1993 RECS were present in the 1978 NationaS Interim 

Energy Consumption Survey (NIECS), which was the first in the series of national sample 

surveys of residential energy consumption. Features that have remained constant throughout the 

nine surveys conducted between 1978 and 1993 include:

 Collection of data on household consumption of major fuels as a primary goal

 Collection and integration of data from multiple sources: households, rental agents, 

energy suppliers and weather stations

 Use of a national multistage probability sample of several thousand households

Energy Information Administration / Energy Consumption -Series 

Residential Energy Consumption Survey Quality Profile
 Primary reliance on personal interviews to collect data from households

 Use of regression methodology to estimate consumption of each major fuel by end iii's: 

within each household.

Within this broad framework, however, there have been many changes in specific features cl t ;; 

survey design and methodology. Changes have occurred because experience, experimeme.tic::, 

and research have suggested improvements; because the needs and interests of data users h;a :: 

changed; because there have been changes in the data available for use in sample design and 

estimation; and because the .evel of resources available for the .surveys has varied from year .0 

year. This section identifies important changes that have occurred in the 15-year evolution < 

RECS. Their impacts on the quality of RECS data are discussed in the chapters that follow.

Frequency and Timing of the Surveys

 Surveys were conducted annually from 1978 through 1982. The next RECS '>v; 

conducted in 1984 and, since then, surveys have been undertaken at 3-year interval 

in 1987, 1990. arid 1993. (As noted above, the Energy Policy Act of 1992 requir 

EIA to conduct a survey of residential energy use at least once every 3 years.)

« From 1978 through 1984, consumption data were collected for the 1-year period fro 

April of the survey year through March of the following year. Starting with the 19f 

survey, however, the consumption data have been collected for the survey cale.nd : : 

year.

Survey Content

 Although the broad topics of RECS have remained fairly constant, there have be 

many changes in specific items. Several of these changes reflect efforts to impro< 

the performance of the regression models used to estimate total consumption of eat 

fuel for households with no data from the Supplier Survey and to allocate consurnptic 

for each fuel to specific end uses. Some items used in one or more surveys have ~ei; 

dropped because respondents have found them difficult to answer. Examples 

questions on the location and thickness of insulation.

a:

Several items have been added or deleted in response to changes in energy policies ;;.r 

programs and the emergence of new energy technologies. Questions on participatio 

of low-income households in government programs to help cover the costs of he::T: 

energy and weadierization improvements were added in 1981 and have been a regjl'i 

feature since then. Questions about use of income tax credits for expenditures c 

home energy conservation improvements were asked in the 1982 and 1984 RECS IT 

were then dropped when this provision of tax la.w expired. Questions abo1 

participation in demand-side management programs (utility-sponsored acthrir

Energy Information Administration / Energy Consumption Series 

18 Residential Energy Consumption Survey Quality Profile
designed to affect the amount and timing of customer electricity use) have become 

more detailed as these programs have expanded in scope and coverage.

As described in the previous section, a comprehensive 1992 study of REGS user needs 

led to inclusion of several new items in the 1993 RECS Household Survey 

questionnaire and the elimination of some items covered in earlier surveys.

Sample Design

The sample for the 1980 survey was the first one specifically designed for the purpose 

of a residential energy consumption survey. Coverage of the target population was 

expanded to include Alaska and Hawaii and residential housing-units on military bases.

The samples for the 1984 and 1993 RECS were redesigned in order to take advantage 

of the availability of new data from the 1980 and 1990 Censuses, respectively. In 

each instance, the new census data were used in the definition, stratification and 

selection of PSU's and SSU's and to improve the precision of the survey estimates.

The sample used in 1978 and 1979 was designed to produce data for the four Census 

regions. The 1980 sample was designed to produce statistically reliable data for nine 

Census divisions and 10 Federal regions. The requirement for the ten Federal regions 

was dropped in the 1984 redesign.

The 1980 and subsequent redesigns have provided for the possibility of including a 

longitudinal component in successive surveys that is, a subsample of the housing-units 

included in each survey could also be included in the next survey. Longitudinal 

components were included in the samples for the 1982, 1984, 1987, and 1990 surveys 

but not in the 1993 survey. In each instance, the sample for one-half of the sample 

SSU's consisted of housing-units from the previous survey, plus a sample of newly 

constructed units. Inclusion of longitudinal components has two advantages: it 

increases the precision of estimates of change between successive surveys and it 

provides a basis for longitudinal analyses, at the housing-unit level, of changes in 

consumption and household characteristics.

The core sample for each RECS has been close to a self-weighting sample (the same 

overall selection probability of selection for each sample housing-unit), with 

adjustments to obtain the required minimum precision for each Census division. 

Supplemental samples of housing-units occupied by low-income households were 

provided for in the 1981. 1984, 1987, and 1993 designs. Because of an interest in 

changing trends in the energy efficiency of new houses, the 1993 sample design also 

provided for oversampling of new housing-units, defined as those completed in 1987 

or later.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 19
Data Collection Procedures

« Each Household Survey from 1978 through 1990 included a multiwave effort to obtaii: 

personal interviews for the greatest possible number rf sample households, folio we :i 

by the use of abbreviated mail questionnaires to seek, limited information from thi: 

remaining households. In the 1993 RECS, a telephone fbliowup procedure, also usm;; 

an abbreviated version of the questionnaire, was inserted between the pe;rs;}i:u 

interview and mail phases.

 In the 1978 survey, Household Survey respondents were asked to estimate their 1:0tr. 

floor space, and there were also questions about the number of rooms and the size <: 

the largest room. In 1979, the question on total floor space was dropped. From 198: 

on. interviewers used tape measures to determine the physical dimensions of ss.r.rrpl: 

housing-units.

 Because of Increasing interest in knowing the rated efficiency of major appliance,-;, i 

the 1990 RECS interviewers attempted to obtain relevant information from thi 

nameplates of central air-conditioning units. This procedure had only limited succ^: 

and was not repeated in the 1993 RECS.

 Small cash incentive payments were used in connect! on with the Household Surve 

interviews in 1978 and 1979 and dropped thereafter. Cash incentives were used v/:j[ 

the mail questionnaires through the 1982 survey. No incentives were used in the "' 98 

RECS; from 1987 on, a small token incentive, such as a key chain, has been sent ;:n 

with the mail questionnaires.

 Interviewers for the Household Survey were trained by mail for the 1978 and 197 

surveys. In 1980, interviewers received in-person training in small group session;- ; 

several locations throughout the country. For the 198]., 1982, and 1984 RECS, :h; 

1980 training procedure was used for new interviewers, but training for those w..r 

prior RECS experience consisted of self-study materials and practice interviews. Fc: 

the 1987 RECS, most of the interviewers received in-person training at five differed 

locations. For the 1990 RECS there was no in-person training; all interview;;!: 

received their training via self-study materials, instructional videotapes, and completi :> 

of practice interviews, with evaluation by contractor staff. Interviewer training for :h 

1993 RECS reverted to the 1987 in-person model, vvith training at four differer 

locations.

Data Processing

 Data entry operations were 100-percent verified in the 1978. 1979, and 1980 survey; 

From 1981 through 1984, key questionnaire items were verified 100 percent and :h 

remaining items were checked for a 25-percent sample of households. The cost c 

correcting data entry errors in subsequent stages of processing proved to be substanta 

so 100-percent verification of data entry for all item;; has been used since the :<>8 

RECS.

Energy Information Administration / Energy Consumption Series 

20 Residential Energy Consumption Survey Quality 3 rofile
Procedures for associating weather data acquired from NO A A with individual sample 

housing-units have evolved over the life of the survey. A major change occurred in 

the 1987 RECS, when degree-day information for each housing-unit in an SSU was 

taken from the individual weather station nearest (in terms of distance and other 

factors) to the SSU, provided usable data were available for that station. Prior to 

1987, degree-day data had been based on clusters of weather stations within a NOAA 

weather division.

Estimation Procedures

For each RECS, part of the estimation procedure has been to benchmark the survey 

estimates of the number of households in various subgroups to agree with independent 

estimates derived from the Census Bureau's Current Population Survey (CPS). 

Initially, this ratio estimation procedure was carried out separately for 12 cells based 

on Census region and location type (centra! city, suburban, rural). Early analyses 

showed that RECS estimates of one-person households were low, so in the 1982 RECS 

a preliminary step was added in which the estimates were benchmarked to CPS 

estimates for three categories: single-person households with male householders. 

single-person households with female householders, and all other households. In the 

1993 RECS, a further benchmark adjustment was introduced to ensure agreement of 

RECS and CPS household estimates for the nine Census divisions and for four large 

States: California, Florida, New York, and Texas.

For all surveys through 1990, the CPS-based estimates used as benchmarks for the 

RECS estimates of households were developed for November of the survey year, 

representing the approximate midpoint of the data collection period for the Household 

Survey. In the 1993 RECS. the benchmark month was changed to July of the survey 

year, to coincide with the midpoint of the reference period for energy consumption.

Measured total consumption of each fuel is allocated to end-use categories by use of 

a model-based estimation procedure that relates end-use consumption to a variety of 

housing and household characteristics for which information is obtained in the survey. 

The allocation model has been gradually refined in successive surveys. A major shift 

occurred in the 1984 RECS, when a nonlinear regression model replaced the linear 

model used in the earlier surveys (Carroll 1987). The number of separate end-use 

categories estimated for electricity was increased in 1990 and again in 1993.

Data Dissemination

Starting with the 1980 RECS, there have been two major publications of the results 

from each survey, the first covering Housing Characteristics 1993 and the second 

covering Household Energy Consumption and Expenditures 1993, In 1984, for the 

first time, estimates of energy consumption by end use were included in the regional 

supplement to the Household Energy Consumption and Expenditures 1993 report.

Energy information Administration / Energy Consumption

Residential Energy Consumption Survey Quality Profile 21
Prior to then, the end-use data had been released in various special publications (E 

1983c, EIA 1984c, Thompson 1987).

Public-use data tapes (i.e., tapes with anonymized data for individual sarnp 

households) have been released for all surveys. Starting with the 1987 RECS, pubti 

use data sets have also been released on diskettes for use on personal compute::;;. 

August 1995. public-use data sets for the 1987, I99P. and 1993 surveys were ;nai 

available for downloading via the Internet.

Energy Information Administration /Energy Consumption Series 

22 Residential Energy Consumption Survey Quality Profile
The specification of goals for coverage is an important element of the design of any survey. 

What is the target population for which estimates are needed? Given a clear and precise 

definition of the target population, one can ask how well the sampling frame developed for the 

survey and the sample selection procedures that are used have succeeded in providing an unbiased 

sample for that population. If some units in the target population have no chance of selection 

in the sample (undercoverage), the resulting estimates may be biased. Multiple chances of 

selection leading to overcoverage are also possible, but much less likely in a survey like RECS. 

The effects of coverage bias can be reduced, but seldom fully eliminated, by benchmarking 

survey estimates to data for the target population available from sources external to the survey.

The first section of this chapter describes the target populations for RECS. The second section 

describes those parts of the frame development and sample selection procedures that involve 

individual housing units and are, therefore, a potential source of coverage error. The final section 

examines the relationships between sample-weighted survey estimates and the external data to 

which they are benchmarked.

In broad terms, the goal of RECS is to provide data for the United States residential energy 

sector. The sample for the 1978 and 1979 surveys did not include any representation of Alaska 

and Hawaii. From 1980 on, the sample for RECS has covered all 50 States and the District of 

Columbia.

For both conceptual and operational reasons, survey target populations must be defined as 

precisely as possible. For RECS, some compromises have been required between coverage goals 

that would be ideal if cost were no object and those judged to be achievable with the resources 

available.

In this section, two kinds of target "populations" are identified. The first consists of the housing 

units and households that are meant to be included in the surveys; the second consists of the 

energy consumption associated with those units. Consumption could be treated as a survey 

variable and discussion of goals for its measurement deferred to Chapter 5 (Measurement Error), 

but because of its basic importance to RECS, it seems desirable to introduce the subject in this 

chapter.

Housing Units and Households

The target population for RECS consists of housing units occupied as primary residences in the 

United States. The housing unit and household definitions for RECS are the same as those used 

by the Census Bureau in the decennial censuses of population and housing and in the Current 

Population Survey (CPS). Mobile homes are included. Housing units on military installations 

were not included in the 1978 and 1979 surveys, but have been included in RECS since 1980

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 23
RECS Target Populations 

0 Housing Units-Households

Housing unit ~ Census definition

Occupied - Excludes vacant units

Primary - Excludes seasonal and occasional residences

Reference date

Through 1990 - November of survey year 

1993 - July of of survey year

« Energy Consumption and Expenditures

For housing units in target population 

For major energy sources:

Electricity, natural gas, fuel oil, kerosene, LPG 

Rough estimate for wood 

Solar energy not included

At site - Excludes primary fuels used to produce electricity

Exclusions

Business uses

Some outdoor uses

Common areas in multi-unil facilities

Reference period

Thiough 1984 - 12 months starting in April of survey year 

1987 on - calendar year

(EIA I982a). Group quarters, such as military barracks and nursing homes, occupied by 13 ni 

more unrelated persons are excluded.

The restriction of the target population to primary residences means that housing units thai: v 

vacant or occupied only on a seasonal or occasional basis are excluded. Although it would ha^: 

been desirable to collect information about housing characteristics and energy consumption In 1 

secondary residences and vacant housing units, attempting to do so would have substantia. ': . 

increased the cost of the survey and the complexity of the procedures. An advantage of t n:

Energy Information Administration / Energy Consumption Series 

24 Residential Energy Consumption Survey Quality Profile
exclusion is that it creates a one-to-one equivalence between housing units and households, so 

that estimates of households from the Census Bureau's Current Population Survey (CPS) can be 

used directly to benchmark the RECS estimates.

Part of the definition of a target population is the date or time period to which it refers. For each 

of the Residential Energy Consumption Surveys, the data collection for the Household Survey 

has taken place from the fall of the survey year through the winter and, in some instances, to the 

early spring of the following year. For all surveys through 1990, November of the survey year 

was taken as a rough midpoint of the data collection period and the survey estimates of housing 

units were benchmarked, or adjusted to agree with, estimates of households (equivalent to housing 

units) for that month based on data from the CPS. For the 1993 RECS. the data collection period 

for the Household Survey was essentially the same as for previous surveys that is, it took place 

from the fall of 1993 through the spring of 1994 but the survey estimates were benchmarked to 

CPS estimates of households for July 1993.

Ideally, each RECS Household Survey should include a probability sample of all housing units 

occupied as primary residences in the month used as a benchmark for the survey estimates that 

is, November of the survey year for all surveys through 1990 and July for the 1993 survey year. 

In actual practice, the decision on whether to include a housing unit in RECS is based on its 

occupancy status at the time that a survey interviewer succeeds in contacting the unit, and 

determining its status. Thus the 1993 survey sample includes some housing units that were not 

occupied as primary residences in July 1993 but were occupied when they were contacted by 

survey interviewers during the data collection period that started in October 1993.

Energy Consumption

For energy consumption and expenditures, the goal of the surveys is to collect data on energy 

consumption for residential purposes, during a specified time period, by the 'Housing units and 

households in the target population. Energy consumed for businesses located in or closely 

associated with a residence is meant to be excluded. The surveys attempt to measure 

consumption at the site, i.e., at the point it enters the residence. For electricity, this measure of 

consumption does not reflect the total amount of energy used to generate it (primary energy 

consumption). Site values for residential consumption of electricity can be multiplied by a factor 

of three to provide a rough estimate of total energy consumed in the production of electricity for 

residential use (EIA 1993a, p. 13). The Household Energy Consumption and Expenditures 1993 

report includes two tables 5.2 and 5.4 that show primary consumption of electricity for 

residential purposes.

For the first six surveys, through 1984, the goal was to measure residential energy consumption 

by households in the target population for the 1-year period from April of the survey year 

through March of the following year. Thus, for the 1984 RECS, estimates of consumption were 

for the 12 months from April 1984 through March 1985. Complete data cannot be collected from 

suppliers until after the end of the consumption reference period. For the 1987 RECS and in 

subsequent surveys, estimates of consumption have been for the calendar year corresponding to 

the survey year, so that collection of data from suppliers could begin at the start of the calendar

Energy information Administration I Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 25
year following the survey year. Operational problems in using billing data from suppliers 

estimate consumption and expenditures for a specific 12-month -eference period are discussed 

Chapter 6.

Some Implications of the Definitions

The seasonal and year-round vacant housing units that are excluded from RECS do co:i3i;.r .e 

some energy. Some of them are occupied for part of the reference year and even those that are 

not occupied at all may consume moderate amounts of energy for various purposes. Table 2, 

provides information from two sources, RECS and the American Housing Survey, about i: ;: 

portion of all housing units that are vacant.

For sampling purposes, the RECS procedures call for a listing of all households in a designati:;:: 

sample of areas, whether or not they are eligible for interviews Therefore, a rough indieaiu r 

of trends in the proportion of housing units excluded by definition can be derived fro :: 

examination of the data on the proportion of assigned sample housing units in each survey ys-;r 

that turned out to be in the excluded group. The RECS data shown in Table 3.1 are unweightt s 

counts of sample units; the estimates of percent ineligible (seasonal and year-round vacatr;') 

would be somewhat different if the appropriate sampling weights were applied to each unit. <, 

more precise indication of the proportions of excluded units is provided by the data from i :; 

American Housing Survey shown in the last column of Table 3.1; these are weighted, sanip1 ;: 

estimates.

The biennial American Housing Survey estimates of vacant unr.s have varied within a namy. 

range, 9.2 to 11.5 percent, between 1981 and 1993. The mean energy consumption of t:ie;-;: 

vacant units was almost certainly substantially lower than the mean consumption of occarie:! 

units, but no reliable estimates are available. Both the level and the trends in the proportion c : 

RECS sample housing units excluded are quite similar to the corresponding American Ho'j.si.n ; ; 

Survey estimates.

The RECS estimates of consumption do not cover all kinds of residential energy consumption 

Although data are collected on. the numbers of housing units using solar collectors for main an.': 

auxiliary space and water heating, no estimates are developed for fuel equivalents of solar ensrgx 

Some, but not all, outdoor uses of fuels such as for lawn mowers and outdoor grills are :ic 

included. Fuel consumption for common areas, such as lobbies and meeting rooms, in multiimi 

apartments and other residential facilities is also excluded.

The consumption of wood as a fuel is not included in the basic RECS estimates of tjta 

consumption of major fuels, but sufficient data on wood used are collected, to permit a rcagl 

estimate. For 1993, it was estimated that inclusion of wood would, have added 5 percent to tru 

RECS estimate of total fuel consumption (EIA 1995d, Table:; 5.2 and 5.9). Since ISMresidential consumption of wood as a fuel has declined substantially.

Energy Information Administration /Energy Consumption Series 

26 Residential Energy Consumption Survey Quality Profile
Table 3.1. Ineligible Housing Units in Sample by Survey Year: 1978-1993

Year

1978.............

1979.............

1980.............

1981.............

1982.............

1983.............

1984.............

1985.............

1986.............

1987.............

1988.............

 iqsq

1990.............

1991.............

1992.............

1993.............

Housing Units in RECS Sample

Total

.............. 4,849

.............. NA

.............. 7,232

.............. 7,550

.............. 5,808

.............. NS

.............. 7,535

.............. NS

.............. NS

.............. 8,007

.............. NS

.............. NS

.............. 6,607

.............. NS

.............. NS

.............. 9.671

Ineligible

Number Percent

342 7.1 

NA NA 

598 8.3 

709 9.4 

536 9.2 

NS NS 

783 10.4 

NS NS 

NS NS 

824 10.3 

NS NS 

NS NS 

698 10.6 

NS NS 

NS NS 

918 9.5

- ~ American Housing 

Survey: Percent

Seasonal or 

Year-Round Vacant

NS 

NS 

NS 

92 

NS

O £-i *J. O

NS 

11.5 

NS 

11.5 

NS 

11.3 

NS 

10.9 

NS 

11 1

NA = Not Available. 

NS = No Survey conducted.

Sources: Energy Information Administration, Consumption and Expenditures (1878); Housing Characteristics (1980-1993); and 

American Housing Survey (1981-1993).

Frame Development and Sample Selection

As described in Chapter 2, RECS uses a four-stage sampling procedure. The first two stages. 

selection of primary and secondary sampling units (PSU's and SSU's), rely solely on area 

sampling techniques. The third and fourth stages can involve both list and area sampling. In the 

third stage, the larger sample SSU's are divided into listing segments and one listing segment is 

selected for the sample. Smaller SSU's are used as the listing segments. During the spring and 

summer of the survey year, field workers prepare listings of all housing units in each listing 

segment.

The fourth-stage procedures also depend on the size (number of housing units listed) of the 

sample listing segments for which housing unit listings were prepared in the third stage. For 

smaller listing segments, a systematic sample of housing units for interviews is selected directly 

from the listings. The larger listing segments are subdivided into groups of housing units called 

"penultimate clusters." One of these is selected and a systematic sample of housing units for 

interviews is selected from it.

There is no information to indicate that the area sampling parts of these procedures selection of 

PSU's, SSU's and, when necessary, listing segments and penultimate clusters have been affected

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Qualify Profile 2.7
by nonsampling errors irt the selection process. In this section, therefore, attention is directed 

errors affecting the completeness of coverage that can and may have occurred in the listing pa 

of the sample selection procedures.

Listing and Update Procedures

Because of the longitudinal nature of the RECS samples for the 1982 through 1990 survey;; 

different listing and sample selection procedures were used for SSU's designated for selection ::! 

a new sample and those for which the sample of housing units was to be carried over from tin; 

prior survey. In some of the SSU's in the former group, new listing segments, requiring c. !l 

initial listing of housing units, were selected. In others, the 1:.sting for the old segment w-i,: 

updated and a new sample of housing units was selected, k. the carryover group, field, worker 

updated listings for the listing segments from the prior survey.

As noted above, initial listing and listing update procedures have been carried out in the sprit;; 

and summer of the survey year. Field workers are instructed to list as housing units "Houses ar i: 

apartments that are under construction if they are likely to be completed and ready for occupar.c 1 . 

by September [of the survey year]" (Response Analysis Corporation 1992a. p. F-ll). Primaii/ 

because of the possibility of subsequent housing unit additions and deletions, the s'jrve;. 

interviewers, who do their interviews in the fall and winter of the survey year, are instructed. 1:: 

look for new or previously missed housing units in the "half-open" interval between each samp):- 

housing unit assigned for interview and the following housing unit on the listing, whether or ::u I 

that unit had been included in the sample. Any such housing ..mits are to be interviewed, h 

addition to the sample housing units initially assigned. In each of the survey years 1984, 195'." 

1990, and 1993 about 1 percent of the Household Survey interviews completed were for ho us rtv 

units added, at the time of interviewing, through the use of the half-open interval technique (EL 

I995f).

Finally it is possible that, at the time of interviewing, some assigned sample housing units rr.s,-, 

no longer meet the definition of a housing unit because they have been condemned o 

demolished, are being used solely for nonresidential purposes, or are currently being used a 

group quarters with living arrangements for institutional residents or inmates or for other group;; 

of 10 or more unrelated persons. It is also possible that a sample housing unit may have bee : : 

split into two or more separate units; in this event, the survey interviewer is expected to conduc 

a separate interview for each unit.

Evaluation of the Quality of Listing and Update Procedures

Little direct information is available about the quality of the listing and updating procedures, Ii 

the 1979 survey, clusters of housing units were independently relisted by a second interviews' 

in one-fourth of the survey locations. The survey report states that "In general, the orig:.na 

listings and relistings are in agreement for 90 to 95 percent of tie housing units listed." ("£!/' 

1981, p. 77). There have been no checks of this kind in subsequent survey years.

Energy .nformation Administration / Energy Consumption Series 

28 Residential Energy Consumption Survey Quality Profile
On at least two occasions, observation of field activities by EIA staff members has provided 

anecdotal information about errors in listing/updating procedures. During the interviewing for 

the 1990 survey, an observation visit to a listing segment in a small North Carolina community 

developed evidence which suggested that the housing unit listing for the segment, which had been 

carried over from the 1987 survey, had not been updated earlier in 1990 (Battles and Thompson 

1991). Near the end of the interview period for the 1993 survey, an P^IA staff member checked 

the listings for seven RECS sample locations in the Los Angeles area. In one listing segment, 

he found that 67 of 85 units that had been listed were outside the boundaries of the listing 

segment. In the remaining six listing segments, he found a total of 21 addresses that had not 

been included in the 223 addresses originally listed, for a miss rate of 8.6 percent (Thompson 

1994a).

In the 1984, 1987, and 1990 surveys, each of which included a longitudinal panel carried over 

from the preceding survey, the sample was divided into 4 subsamples, or rotation groups, each 

consisting of one-fourth of the total sample of SSU's. Because the frame development and 

sampling procedures varied by rotation group, an analysis of the counts of new housing units in 

the sample by rotation group provides indirect information relevant to the quality of listing and 

updating.

Three different frame development and sample selection procedures were used, as follows:

Procedure 1. For every SSU, select a new listing segment, do the housing unit listing arid 

select a sample.

Procedure. 2. Check each SSU to identify those with significant amounts of new 

construction (25 or more units). For those with significant new construction, follow 

Procedure 1. For other SSU's, update the listings from the preceding survey and select 

a sample of new units and units not sampled in the preceding survey.

Procedure 3. For all SSU's, update the listings from the preceding survey. The sample 

consists of units included in the sample for the preceding survey plus a sample of the new 

units identified in the updating operation.

For all three surveys, Procedures ! and 2 were each used in one of the four rotation groups and 

Procedure 3 was used in the other two rotation groups. In the SSU's for which Procedures 1 and 

2 were used, significant amounts of new construction were found in 130 of 608 SSU's in 1984, 

in 205 of 615 SSU's in 1987, and in 197 of 758 SSU's in 1990. (In 1984 and 1987, the 

preliminary checks for new construction were not undertaken for SSU's in primary sampling units 

that were entering the sample for the first time.)

Table 3.2 shows the sample counts of new housing units those built in the survey year and the 

two preceding years by rotation group for the 1984, 1987, and 1990 survey years. Under the 

hypothesis that new listings are likely to provide better coverage of newly constructed housing 

units than are updates of prior listings, one would expect to find the most new units in rotation 

groups in which Procedure 1 was used. One would expect the rotation groups for which prior 

listings were updated (Procedure 3) to have the smallest number of new housing units and the

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 29
rotation groups for which new listings were used for segments -v'th significant amounts of rur- 1 

construction (Procedure 2) to be somewhere in between. For the most part, these expectation. 

are borne out by the data in Table 3.2, although the distinction between Procedures 1 and '.', :• 

not clear. On the other hand, the data for older houses, shown lor the 1990 RECS only (hous::; 

constructed between 1985 and 1987), do not follow the pattern observed for new hou: ;e:; 

Overall, these data provide support for the hypothesis that new i.stings provide better covenux 

of new housing units than do updates of listings from the preceding survey (Jabine 1993. 0.8'

Evaluation of Coverage Based on External Data Sources 

Comparison with Current: Population Survey (CPS) Estimates of Households

Some indication of the completeness of coverage of housing units and households in RECS ca 

be had by comparing CPS estimates of households with the RECS estimates for each survey vet 

prior to the stage at which the latter are "benchmarked" to (adjusted to agree with) i:h 

corresponding CPS estimates. The benchmarking adjustment is carried out separately for ea: 

of 12 strata defined by Census region and metropolitan statistical area status (MSA central or!\ 

MSA other, and non-MSA). The estimates prior to benchmarking are obtained by applying u 

appropriate weights to sample households based on their overall sample selection probabilities ?rii 

adjusting for unit nonresporise.

Table 3.3 shows, by Census region and survey year, the percentages by which the benchmarks';. 

estimates exceeded the estimates prior to benchmarking. These percentages provide indication: 

of possible net undercoverage of households in RECS. The}- axe not precise measures o 

undercoverage for several reasons:

 Both the benchmarked estimates and the estimates prior to benchmarking are subjec 

to sampling error,,

 Based on comparisons with the decennial censuses and census post-enumerator] 

surveys, it seems likely that the CPS estimates of households are themselves somewhat 

low.

 The "benchmarking factors" on which the values shown in Table 3.3 are based actual.]} 1 

combine the effects of two stages of ratio estimation, only the second of which. 

represents the actual benchmarking to the CPS estimates. The first stage, which. 

applies only to non-self-representing primary sampling units in the RECS sample, :s 

based on Census counts of households by PSU and reduces the component of sampling 

variance that results from the selection of a sample of PSU's.

At the national level, the undercoverage indicator shown in Table 3.3 stayed within a fairly 

narrow range from 6.6 to 9.7 percent from 1980 through 1.990, but. in 1993 it declined to 4,2 

percent. In each survey year between 1981 and 1990, the indicator -was substantially higher for 

the South than for the other three Census Regions, but in 1993 it declined to a much lower level 

The redesign of the sample for the 1993 RECS, using 1990 Census data, may have accounted,

Energy Information Administration /Energy Consumption Series 

30 Residential Energy Consumption Survey Quality Profile
Table 3.2. Sample Counts of New Housing Units by Rotation Group: 1984, 1987, and 1990 RECS

Survey Year

List/Update 

Procedure3 Rotation Group

Sample Count of 

Housing Units

1984 REGS

(Units built in 1982 to 1984)

1987 REGS

(Units built in 1985 to 1987}

1990 REGS

(Units built in 1988 to 1990)

(Units built in 1985 to 1987)

1

2

3

3

1

2

3

3

1

2

3

3

1

2

3

3

D Cb 

E

E 

Fb 

C 

D

C 

D 

E 

F

C 

D 

E 

F

56

41

29

29

58

71

39

39

49

34

21

34

65

48

47

65

aSee description of procedures in text.

bBecause of the introduction of new Primary Sampling Units, Procedure 1 was used in about 1/5 of the SSU's in 1984 and 1987.

Source: Special tabulations of 1984, 1987. and 1990 RECS data.

at least partially, for the changes that occurred in these indicators between 1990 and 1993. In 

the 1993 redesign, 1,250 out of 1,610 secondary sampling units (SSU's) were newly-selected, 

whereas in the 1984 redesign, based on the 1980 Census, only 266 of 1,515 SSU's were new. 

As noted earlier in this chapter, the use of the "half-open interval" technique in the 1984, 1987, 

and 1990 survey years to add housing units missed by listing and updating procedures accounted 

for about 1 percent of the household interviews completed in each of those years. Given the 

undercoverage indicators shown in Table 3.3 for those years, it would appear that interviewers 

using the half-open interval technique succeeded in identifying only about 10 percent of the 

previously missed housing units.

Coverage of New Housing Units: Comparisons with Data from Census Bureau 

Surveys of Construction New Mobile Homes

The results of the 1990 RECS showed an unexpected reversal in what had been a nearly 

uninterrupted downward trend in average energy consumption per household by year built. This 

finding led to a search for ways of using external sources of data to improve the accuracy of the 

estimates based on the sample housing units that had been built in the period 1980 through 1990.

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 31
Table 3.3. Benchmark Adjustment Factors5 for RECS Estimate's of Households, by Census R^sin 

1990-1993

Census Region

Northeast..............

Midwest................

South....................

West.....................

U.S. Total.............

1980 , 1S

_ . . . i

....... 4.1 5

...... 4.7 3

....... 8.1 IS

...... 9.1 8

...... 6.6 7

81

.5

.5

1.2

6

8

1982

Q 4

6.9

12.3

4.1

8.7

Surv«>y Yea t

1984

7.2

6.8

14.7

09

8.4

1987

4.7

7.3

16.4

6.5

9.7

1990

? Q

4.3

15.5

5.7

8.1

"Benchmarkadjustment factors a re applied to weighted sample estimates, following adjustment for unit nonresponse, to rnaks tN:>: 

agree with Current Population Survey estimates of households. The value shown in eszr- table cell is equal to 100 (M). v^ers 

is the benchmark adjustment factor.

Source: Robert B. Latta, Analysis of Listing Undercounf and Other Undercoun< Facfcre for RECS (October 1994).

Two such sources were the Census Bureau's Survey of Constriction and its Survey of Nev 

Mobile Home Placements. The special estimation procedures thai were developed by using dak 

from those sources are described in Chapter 7. This chapter looks ai what these data reveal aba..1. RECS coverage of housing units by year built.

Table 3.4 shows Census Bureau and 1990 RECS estimates of housing units, by housing type anc 

main space-heating fuel, built in the periods 1980-1984, 1985-1987, and 1988-1990. The UM: 

sets of estimates are not fully comparable. The Census Bureau estimates, based on the Sur>/e\ 

of Construction for each year during the periods shown, include housing units used as vacation 

homes, second homes, and seasonal rentals. They may include some units that have subsequently 

been demolished or converted to nonresidential use by 1990. when :he RECS data were collected. 

For some of them, the main space-heating fuel may have changeo by 1990. The estimates from 

both sources are subject to sampling error.

Keeping these differences in mind, one still finds that the data in I able 3.4 strongly suggest thai 

undercoverage associated with the 1990 RECS frame development and sample selection 

procedures was greatest for units built during the most recent period, 1988-1990.

The timing of the 1990 RECS data collection was such that one; could not expect coverage of a ...I 

newly-constructed housing units first occupied during the latter part of 1990, and units built but 

not yet occupied in 1990 vvouid generally not be included. Nevertheless, the apparent 

undercoverage of new housing units for the 1988-1990 period is larger than could be fully 

accounted for by these factors and may be due in part to failure of the listing, updating, and b,i:! 

open interval procedures to capture such units. The data show that :l:c deficit is greatest for ne.v 

housing units in multifamily buildings.

32

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Prefiie
Table 3.4. 1990 RECS and Census Bureau Estimates of Housing Units by Year Built: 1980-1990

Year Built

and 

Housing Type

Main

Space-heating

Fuel

hstimated No. of

Housing Units (000)

Census 1990 

Bureau RECS

Ratio of 

RECS 

Estimate to

Census 

Estimate

1980-1984 

Single family 

Single family

Units in multifamily buildings 

Mobile homes

Total

1985-1987 

Single family 

Single family

Units in muitifamily buildings 

Mobile homes

Total

1988-1990 

Single family 

Single family

Units in multifamily buildings 

Mobile homes

Total

Gas or oil

Electricity or other

All

All

Gas or oil

Electricity or other

All

All

Gas or oil

Electricity or other

All

All

1,954

2,429

2.433

7.628

1,722

1,619

1,789

778

5,908

1,916

1,188

1160

622

2,461

2,671

2,083

812

8,027

1,245

1,996

1,230

610

5,081

1.26

1.10

0.86

1.00

0.69

0.78

2,767

0.69

0.54

0.28

0.76

0.57

"Census data were not available for this period, so the 1990 RECS estimate has been used. 

Source: Energy Information Administration, Consumption and Expenditures (1990), Tables B19-B21.

Similar comparisons based on data from the 1993 RECS suggest much improved coverage of new 

housing units in that survey year, perhaps as a result of the special sampling procedures that were 

used to ensure a larger sample of newly-constructed housing units. The Census Bureau estimated 

that 4.070 million housing units were built or, in the case of mobile homes, put in place, from 

1991 through 1993. The 1993 RECS estimate for this time period was 4.5 million housing units 

(plus or minus 0.7 million at the 95 percent confidence level), reversing the pattern shown in 

Table 3.4 for estimates of the newest homes in the 1990 RECS.

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 38
Nonresponse occurs in each of the three RECS surveys: Household. Rental Agent, and Supplier 

Surveys. Unit nonresponse occurs when no information at all is obtained from an assigned 

sample unit or when so little information is obtained that, the questionnaire is classified as 

unusable. Item nonresponse occurs when a usable questionnaire is obtained but the desired 

information is missing for one or more items. Item nonresponse can occur because a respondent 

is unable or unwilling to answer a specific question, because the interviewer fails to ask it. or 

because a data entry clerk fails to key the response.

Both kinds of nonresponse affect the quality of the survey results. The magnitude of their effects 

can seldom be determined precisely. They depend in part on the efficacy of the imputation and 

estimation techniques that are used to try to limit the extent of bias due to nonresponse. They 

also depend on how large the nonresponse rates are and the degree to which the characteristics 

of nonrespondents differ from those of respondents.

This chapter presents information about the amount of. and trends in, nonresponse in the three 

component surveys, the characteristics of nonrespondents, the reasons for nonresponse. and the 

techniques that are used to minimize nonresponse rates. The imputation and estimation 

techniques used to deal with nonresponse are covered in detail in Chapters 6 and 7, respectively; 

however, a brief summary is given at this point. Figure 4.1 summarizes these techniques by 

survey component and type of nonresponse.

For the Household Survey, weighting adjustments are used to minimize the biases caused by unit 

nonresponse. For most variables based on the Household Survey questionnaire, missing values 

are imputed by using one of several different deterministic or probabilistic imputation techniques. 

For a few variables, some or all of the missing values are not imputed. Housing characteristics 

not fully imputed in the 1990 RECS included several variables related to conservation practices. 

such as thermostat settings, participation in demand-side management programs, and the use of 

insulation, caulking, and weatherstripping. Also not fully imputed in 1990 were missing 

responses to questions on the age and other characteristics of appliances and equipment and on 

the number of years survey respondents expected to remain in their present homes.

For households in multi-unit structures with one or more fuels included in the rent, the Rental 

Agent Survey is designed to provide the most accurate information possible about the main space 

heating fuel and equipment and the main fuels for water heating and air-conditioning. 

Information provided by rental agents is the preferred source for this kind of information-that 

is, it is usually considered to be more accurate than the corresponding answers obtained from 

household respondents. When information is not available from the Rental Agent Survey due to 

unit or item nonresponse, the fallback procedure is to rely on information reported in the 

Household Survey.

For households that pay suppliers directly for one or more delivered fuels, the Supplier Survey 

provides information from billing records about housing unit consumption and expenditures byfuel type. When no Supplier Survey information for a specific fuel is obtained for a household. 

the household's consumption and expenditures for that fuel are imputed on the basis of household

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 35
Figure 4.1. Treatment of Nonresponse by REGS Component

Type of Non response

Survey Component Unit | Item

Housing unit Weighting adjustments v/gnes by item:

'! Impute 

•'• Publish as NA

Rental agent Use corresponding data fron Housing unit survey

Supplier Impute from housing Varies by fuel:

characteristics > rnpute from part-year dats

(electricity, NG) 

rnpute from housing 

characteristics {other fuels)

Source: Energy Information Adrrinistrat'on. Housing Characteristics (1993): C:ir:Sui>:ition and Expenditures (1993).

characteristics by using a nonlinear regression method. Wher. partial information is obtained ir 

the Supplier Survey, the imputation technique depends on the hie type. For electricity art. 

natural gas, annual consumption and expenditures are sometimes imputed on the basis of part-yeai 

data from the Supplier Survey. For other fuels, part-year o';na on billings are not i;:;eT 

consumption and expenditures are imputed in the same way ;.is ir ro data had been obtained ir. 

the Supplier Survey.

The next section of this chapter covers nonresponse in the lloiiseholc Survey, with subsection!; 

on unit and item nonresponse. The following two sections present comparable information far 

the Renta! Agent and Supplier Surveys, respectively. The final section summarizes the nakrf: 

and consequences of nonresponse in these three components of

 nresponse in the Household Burvey 

Unit Nonresponse

The information on overall unit response rates for RECS survey years 1978 through 1993 has 

been summarized (Table 4.1). All rates shown in Table 4.1 and sui'svquenl tables in this chap^r 

are unweighted—that is, they are based on counts of eligible' sarrple units and do not reflect 

variations in the overall selection probabilities of individual v.nir-;. The implications of using 

unweighted rates are discussed later in this section.

Energy Information Administration / E:.nergy Consumption Series 

36 Residential Energy Consumption Survey Quality PrnfMe
Table 4.1. Household Survey Eligibility and Response Rats by Survey Year: 1978-1993

Category 

1978 1979 1980 198

Survey Year

1 1982 1984 198? 1990 1993

Number of units 4,849a 4,935 7,338 7,668 5,903 7,658 8,232 6,757 9,869

assigned to 

interviewers

Percent

ineligible:

Not housing NA 2.8 1.4 1.5 1.6 1.6 2.7 2.2 2.0 

units

Vacant or 7.1 7.0 8.1 9.2 9.1 10.2 10.0 10.3 9.3

seasonally

vacant

Number of units 4,507 4,453 6,634 6,841 5,272 6,752 7,183 5,909 8,753

eligible

Percent 85.2 85,5 87.5 86.

completed by 

interview

Percent not

completed by 

interview:

7 84.8 81.1 81.5 81.7 79.0

Refusal NA NA 8.4 8.2 11.5 14.9 14.0 12.1 15.3

No one home NA NA 2.2 3.2 1.9 2.5 3.1 3.8 2.7

Other NA NA. 2.0 1.8 1.7 1.5 1.4 2.4 3.0

Percent 5.3 5.1 3.7 4.9 4.7 3.0 52 4.5 2.2

completed by 

mail"

Percent 90.5 90.6 91.2 91.

completed by 

interview or mail

6 89.5 84.1 86.7 86.2 81.2

"Data unavailable for assigned units that were not housing units.

bData for 1993 include households completed by mail and telephone.

NA = Not Available.

Sources: Energy Information Administration, Housing Characteristics (1980-1993); Consumption and Expenditures (1978-1979).

As explained in Chapter 2, the multi-wave, multi-mode data collection procedure used in the 

Household Survey is designed to maximize the level of survey response for the eligible units in 

the initial sample. Following multiple attempts to complete a personal interview for each unit, 

mail questionnaires are sent to most of the remaining addresses. In the 1993 RECS, a telephone 

followup was inserted between the personal interview and mail phases of data collection.

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 37
Substantially abbreviated versions of the personal interview questionnaires are used in :n, 

telephone and mail phases of data collection, their main purpose being to identify suppliers ;n 

obtain respondent waivers so that consumption and expenditure data for these units can •:> 

obtained in the Supplier Survey. The mail and telephone version;: also include some of the C3 : i 

items used to estimate end-u.s;: consumption for these housing units. One consequence of usm;: 

abbreviated questionnaires fcr the mail and telephone foilowup; is that item nonresponso J ; 

automatic for all data items not included on those questionnaires

Personal interview and mail.'telephone response rates are shown separately in Table 4.1. es:1 } 

being calculated as a percent of all housing units eligible for interviews. Personal interview 

completion rates remained fairly stable, in the neighborhood of 85 percent, from 1978 throus-l 

1 982. but declined to a lower level, in the neighborhood of 80 percent, for all subsequent surveys 

For most survey years, mail response rates were close to 5 percent, the exceptions being 1^80. 

1984, and 1993. In 1993. the proportion of all eligible units completed by mail and telephone 

combined was only 2.2 percent, and the overall response rate. 8) 2 percent, wras the lowest y;;i 

experienced in the RECS Household Survey.

Two kinds of explanations car be sought for the decline in unit response rates for RECS in the 

four most recent survey years. It might be argued that there, has been a general decline in tr? 

willingness of persons to respond to national household surveys for which participation s 

voluntary. Such a trend ;:an be countered to some extent by ncreased efforts to motivate 

response and to contact persons cr households that are difficult to reach, but there are limits c 

what can be done to counteract outright refusals. The question of whether such a trend hfi? 

actually occurred is controversial. Based on a review of eight per cdic demographic surveys, a 

subcommittee of the Office of Management and Budget's l"ed<ral Committee on Statistic-.!. 

Methodology recently concluded that:

... there was little evidence of declining response rates over time ... There was 

some evidence that refusal rates were increasing in demographic surveys; however, 

the analysis revealed that there are no changes in overall response rates. This 

could be due to a greater effort in data collection, (Goiv,alez. Kasprzyk, and 

Scheuren 1994)

Response trends in a specific survey can also be affected by changes in the survey's design aid 

procedures. There have been several changes in Household Sun';;}' procedures that might be 

expected to have some impact on unit response (Figure 4.2).

Of the five design feature;; shown in Figure 4.2, only the firs! ':wo, involving the use o:f 

incentives, might be expected to increase unit response rates. The first RECS, in 1978, used H 

$2 cash incentive that was given to each household at the time it was;, first contacted, plus anoth^v 

$2 included with the questionnaires mailed to households for which personal interviews had no: 

been obtained. To evaluate the effectiveness of the initial $2 incentive, an experiment was 

undertaken in the 1979 RECS. Primary sampling units in large cities, where response rates zit 

usually lower, were excluded from the experiment. In the remaining 80 primary sampling unit:;. 

the incentive was used in 60 randomly selected secondary sampling units and was not used in tb=i 

remaining 20. The $2 incentive had no significant effect on uniit aric item nonresponse rates, bill

Energy Information Administration / Energy Consumption Series 

38 Residential Energy Consumption Survey Quality Prof le
more of the respondents who received the incentive agreed to sign waivers allowing EIA to 

collect data from their energy suppliers and a slightly higher proportion of them permitted 

interviewers to measure floor space inside the house, rather than outside (Thompson 1985b).

Figure 4.2. Design Changes That

Design Feature i

1978 | 197S

Initial incentive Y Y 

payment3

Mail questionnaire Y Y 

incentive*3

Longitudinal N N 

component

Returning households

.,... ... _,. , ... , ..... 

1980 1981 I 1987

Y N N N N

Y Y Y N Y

N N Y Y Y

N Y N

1993

N N

Y Y

Y N

N

identified for 

interviewers0

Low-income 

supplement

N Y Y N Y

a ln 1979, the incentive payment was provided to a subset of the sample households as part of an

"From 1978 to 1982, a cash incentive was used. From 1987 on, a token gift has been used.

'Applicable only in years with a longitudinal component.

 = No longitudinal component

Source: Energy information Administration, Housing Characteristics (for years shown).

ment.

Nevertheless, the initial $2 incentive was discontinued after the 1980 E.ECS. The $2 incentive 

that accompanied the mail questionnaires was continued through 1982. In 1984, no incentive was 

used; in the 1987, 1990, and 1993 surveys a non-cash token gift was included with the mail 

questionnaires, The absence of any mail incentive, cash or non-cash, in 1984 may explain the 

low mail response rates observed in that year,

Among the factors that might be expected to lead to lower response rates was one that clearly had 

this effect the introduction, starting in 1982, of a longitudinal survey design component, 

involving the collection of data from some households in two successive surveys. A subset of 

the households affected were also asked, in the interval between the two surveys, to participate 

in the Residential Transportation Energy Consumption Survey (RTECS). Many of the RTECS 

households were requested to keep track of their vehicle mileage and fuel purchases for a period 

of several months. Table 4.2 shows personal interview and mail response rates, separately for 

new households and those included in the sample previously, for the four years in which RECS 

included a longitudinal component. Clearly the new units had lower refusal rates and higher 

overall response rates, especially in 1984, 1987, and 1990,

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 39
The data do not distinguish, among households previously include 

that participated in RTECS and those that did not (Table 4.2). 

McCarthy 1986) provided such a comparison for the 1984 '.'IF.C 

RTECS included all households in the 1982 RECS that reporter 

12.500 miles or more and a subsample of the remaining househol 

and relevant comparison is of" the response rates for the RTECS p; 

among the latter group, those households that reported fewer that 

In that group, the 1984 RECS participation rates were 75.4 percer 

been included in the sample for the 1983 RTECS and 80.6 percen 

not been selected.

:l in the sample, between thosi 

A special study (Hersey ant 

S. The sample for the '. ;)'K:

having driven their vehici;;: 

:is. Therefore, the most diree/ 

"tic-pants and non-participarr::-

12,500 vehicle miles driven. 

 [ for 644 households that hnc 

it for 375 households thai /KM.

Table 4.2. Household Survey Response Rates by Survey Year Bind 'Prior Sample Status: 1982-1SWO

Survey Year

and 

Prior Status 3

1982

Previously in sample 

New units

Response Rates; 

(percent of eligible units

Personal

Interview

83.9

85.9

Mail

4.8 

4.6

Personal Interview 

Nonresponse

Rates

(percent of

eligible units)

Refusals Other

12.7

10.3

3.4 

3.9

1984

Previously in sample 

New units

78.2

84.1

2.8

3.2

17.6

12.2

4.2 

3.7

1987

Previously in sample 

New units

79.5

83.5

5.2

5.1

84.7

88.6

16.3

11.7

4.2 

4.8

1990

Previously in sample 

New units

79.4

84.0

4.5 

4.5

13.9

10.4

6.7 

5.6

aThe category "Previously in Sample" incudes a few units missed in the previous survev or constructed subsequently. 

Source: Energy Information Administration Housing Characteristics (for years shown)

Another factor, which was operative only in the 1984 RECS, wa.s a ample assignment procedure' 

which made it possible for the survey interviewers to know which of the units assigned to the/r 

had already been included in the 1982 RECS. There is no direct evidence about how th:;: 

knowledge may have affected interviewers" efforts, but it has been suggested that interviewermight not have tried as hard to obtain interviews for these "recycle:)" bousing units as they dk; 

for new ones (Response Analysis Corporation 1987).

40

Energy Information Administration / Energy Consumption Ssries 

Residential Energy Consumption Siurvey Quality Profile
A change in the relative sample selection probabilities for different subsets of the RECS target 

population can affect MM^gzg^fcY/ unit response rates. An example of this is provided by the lowincome supplements that were included in RECS in survey years 1981, 1984, 1987, and 1993. 

In these years, housing units in secondary sampling units that were classified by interviewers as 

low-income areas were oversampled. with the result that a larger proportion of the total sample 

was located in such areas. Data for the 1987 RECS (Slider 1995) show that response rates were 

somewhat higher in the areas thai were oversampled, so that the overall unweighted response 

rates were higher in 1987 than they would have been if there had been no low-income 

supplements in these years. PKe/^/7/ec/ response rates, of course, would not be affected by 

differences in sampling rates for different segments of the population.

There is some evidence that personal interview response rates may be higher for interviewers who 

have had experience in prior RFCS survey years. A special tabulation for the first round of 

interviewing in the 1990 RECS showed that interviews were completed for 73.7 percent of the 

sample households assigned to interviewers who had also worked in the 1987 RHCS, as compared 

with only 64.7 percent for interviewers who had not worked in 1987 (Response Analysis 

Corporation 1991, Table II). However, these findings could also he explained by higher turnover 

rates in areas where respondents lend to be less cooperative.

The proportion of experienced interviewers (those who had worked on earlier RHCS surveys) was 

in the neighborhood of 60 percent for survey years 1981, 1982. and 1984. It declined to about 

45 percent in 1987 and to about 15 percent in 1990 and 1993. To some extent, this trend tracks 

the decline in personal interview response rates in the most recent survey years, but 1984, with 

a high proportion of experienced interviewers and a low response rate, is an exception.

The 5 percent drop in response rates between 1990 and 1993 resulted primarily !rom a conscious 

decision to reduce the target response rate for RECS from 85 to 80 percent. In order to maintain 

the desired sample size, the level of followup effort for households not completed in the initial 

wave of interviewing was reduced in comparison with prior survey years. Another factor that 

may have influenced response rates for the 1993 RECS was the substantial increase in the length 

of the Household Survey questionnaire compared with preceding surveys. The basic household 

questionnaire contained 117 pages in 1993, compared to 63 in 1990 and 50 in 1987. Potential 

respondents might not have had a very clear idea, when approached to participate in the survey, 

of the length of the questionnaire or the time it might take to complete it. However, some of the 

experienced interviewers, knowing of the increased length of the 1993 questionnaire, might not. 

have tried quite as hard to enlist the cooperation of initially reluctant respondents, knowing that 

there was an increased risk of their breaking off the interview prior to its completion.

In summary, the drop of 5.4 percentage points in overall response rates between the 1982 and 

1984 survey appears to have resulted from the confluence of several factors: the elimination of 

incentives, cash or noncash, for mail questionnaires; the inclusion of a longitudinal component 

and a low income supplement: and the use of a procedure which allowed interviewers to identify 

housing units previously interviewed. Several steps were taken to try to increase response rates 

in the 1987 RECS. These measures appear to have had some success in 1987 and 1990. but did 

not bring the rates back to the levels achieved prior to 1984. In the 1993 RHCS. the absence of 

a longitudinal component was not accompanied by the reduction in refusal rales thai might have

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 41
been expected; on the contrary, the proportion of eligible housing units refusing person; : 

interviews increased to its highest level. The increased length of the 1993 Household Sur-r e/ 

questionnaire may have played a role. This increase, in combination with a substantial decl:n: 

in the success of mail and telephone followups, led to the lovves: overall completion rate so ; ;;r 

experienced in the RECS Household Survey.

The effects of nonresponse on the quality of survey estimates depend not only on the overall le'-d 

of nonresponse but also on its distribution among subgroups of the RECS target populatior 

Trends in overall response rales (personal interview, plus mailAelephone) for selected housing; ij.ii: 

characteristics Census region, urban-rural location, and type of structure for survey years 197 

through 1993 are importanl (Table 4.3).

Even with the declining trend in overall response rates, some fair) 11 consistent patterns in relati'< '; 

rates are evident. The Northeast Region has consistently had the lowest overall response rates 

The South has consistently had the highest response rates for personal interviews but has had IYK 

lowest mail response rates in nearly all survey years. Households located in urban areas (t !ru= 

central cities of metropolitan statistical areas) have consistently had the lowest overall response 

rates and those in rural area-, have had the highest rates. Except for 1993, households ir 

structures with five or more housing units have had the lowest personal interview response rate.:. 

Households in single-family structures (including mobile homes) have had the highest overdl 

response rates except in 1990, when the rates for units in structures with two to four bous:.rtg 

units were the same as those for units in single family structures. As explained further in Chapter 

1 . the weighting procedures used to produce RECS estimates include weighting factors that are 

specifically designed to minimize the effects of nonresponse bias arising from differential 

response rates by Census region and urban/rural status.

Item Nonresponse

Responses to individual items on a completed questionnaire: can be assigned to one of :he 

following categories:

1. No entry required for item (skip based on prior item)

2. Entry required for item 

a. Item left blank 

b. Allowable nonresponse

(1) Don't know or not sure

(2) Refused

c. Non-standard response 

d. Standard response

Energy Information Administration / Energy Consumption -Series 

42 Residential Energy Consumption Survey Quality
sable 4.3a. Household Survey by Status, Structure Type: 

1979-1993

Census Region

and 

Survey Yesr

of i

Persona! 

Interview.,' Questionnaire3

» 

jnits)

Total 

ResponsePersonal interview 

Nonresponse Rates 

(percent of 

eligible units)

Refusals Other

Northeast

1979

1980

1981

1982

1984

1987

1990

1993

Midwest

1979

1980

1981

1982

1984

1987

1990

1993

South

1979

1980

1981

1982

1984

1987

1990

1993

West

1979

1980

1981

1982

1984

1987

1990

1993

82.0

83.8

83.2

81.7

81.2

79.0

77.5

75.8

86.7

87.4

86.7

84.4

79.7

80.7

83.1

80.4

87.6

89.8

88.9

86.5

83.5

84,0

84.9

81.0

84.2

87.9

86.9

85.8

79,4

81.8

80.4

77.8

6.0

4.9

6.3

5.2

2.0

5.7

5.9

2.6

4.4

3.7

5.2

5,4

4.1

5.9

4.3

2.9

3.8

3.1

3.4

3.2

2.1

4.2

3.1

1.8

7.5

3.5

5.3

5.4

4.0

5.1

5.1

1.9

88.0

88.7

89.5

86.9

83.2

84.7

83.4

78.3

91.1

91.1

91.9

89.9

83.8

86.6

87.4

83.3

91.4

92.9

92.3

89.7

85.6

88.2

88.0

82.8

91,7

91.4

92.2

91.3

83.4

86.9

85.6

79.8

NA 

10.5 

10.5 

13.1 

15.1 

16.3 

13.8 

16.6

NA

11.2

15.7

13.3

13.0

16.0

NA 

5.7 

6.3 

5.2 

3.7 

4.7 

8.7 

7.7

91.1

91.1

91.9

89.9

83.8

86.6

87.4

83.3

NA

9.0

8.8

12.5

16.5

15.1

11.8

15.5

NA

3.6

4,4

3.0

3.S

4.2

5.1

4.1

91.4

92.9

92.3

89.7

85.6

88.2

88.0

82.8

NA

6.8

6.2

9.7

12.8

11.7

10.3

13,8

NA

3.4

4.9

3.8

3.7

4.3

4.8

5,2.

NA 

3.3 

4.7

2.9

4.8

4.9 

6.6 

6.1

"Data for 1993 include mail and telephone questionnaires,

NA = Not Available.

Source: Energy Information Administration. Housing Characteristics (1979-1993),

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 43
Table 4.3b, Household Survey Response Rates by Region- U 7 ban Status, and Structure '"'up; 

1981-1993

Response Rates

Location Type {percent of eligible units)

and

Survey Year8 Personal Mail 

interview Questionnaire

Urban

1981 82.4 6.5

1982 80.8 6.1

1984 79.4 3.5

1987 73.8 5.2

1990 79.0 4.9

1993 77.6 1.7

Suburban

1981 85.8 5.9

1982 85,0 4.6

1984 79.3 3.7

1987 80.4 6.0

1990 80.6 5.2

1993 77.6 2.9

Rural

1981 91.1 2.6

1982 89.7 3.2

1984 86.2 1.4

1987 85.8 4,1

1990 87.2 2.8

1993 84.2 1.5

Total 

ResponsB

88.9

86.8

82.9

85.0

83.8

79.4

91.7

89.6

83.0

86.4

85.9

80.5

93.7

93.0

87.6

89.7

89.9

85.7

Personal Interview

IMonresponse

(percent

Rates

of

eligible units)

Refusals

10.0

13.6

15.8

14.4

12.3

15.5

9.7

12.5

15.7

15.6

13.7

17.1

5.4

7.5

10.9

'K).9

9.3

11.1

Other

7.6

5.6

4.8

5.8

8.7

6.8

4.6

2.5

4.0

4.0

5.7

5,4

3.5

2.8

2.9

3.5

3.5

4.7

"Data available for 1979 and 1980 are based on a different location type classifica-ior

b Data for 1993 include mail and telephone questionnaires.

Source: Energy Information Administration, Housing Characteristics (1981-1983).

44

Energy Information Administration /{Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Table 4.3c. Household Survey Response Rates by Region, Urban Status, and Structure Type: 

1979-1993

Structure Type

and 

Survey Year

Single Family 

1979

1980

1981

1982

Mobile Home

1979

1980

1981

1982

Single-Family/Mobile Home 

1984

1987

1990

1993

Buildings with 2-4 units 

1980

1981

1982

1984

1987

1990

1993

Buildings with 5 or more units 

1980

1981

1982

1984

1987

1990

1993

Response Rates 

(percent of eligible ui

Personal Mali 

Interview Questionnaire"

87.6 4.3

88.9 3.3

88.3 4.1

86.2 4.4

88.1 2.8

90.0 2.1

89.2 2.6

87.4 2.0

83.8 1.2

82.3 5.4

82.3 4.4

79.6 2.3

80.1 7.1

86.4 4.8

85.0 4.2

81.4 2.9

80.1 3.9

83.2 3.5

74.7 2.5

76.3 9.5

78.6 9.9

76.7 7.9

79.4 3.8

7S.4 5.4

77.4 6.0

78.6 1.7

ilts)

Tota!

Response

91.9

92.2

92.4

90.6

90.9

93.0

91.8

89.5

85.0

87.7

86.7

81.9

87.2

91.2

89.2

84.3

84.0

86.7

77.1

85.8

88.5

84.5

83.2

84.8

83.4

80.2

Persona! in!

Nonrespo

O ""^sj'jeas,"' iXd i&zs

(percent 

eligible us

Refusals

9.7

6.4

8.1

11.6

/ b

8.4

6.1

8.9

1 2 . 2

14.5

12.8

15.8

NA

/ /,

10.2

1 2 . 7

12.4

8,0

17.0

NA

10.5

i Q n \ \J . VJ

14.4

13.0

11.7

12.0

srview

nse

of 

 sits)

Other

2.6

2.7

3.6

2.3

4.4

2.7

4 . 7

3.6

4.0

 "* ' ")

o . Z

^,9

A R

HA

8.4

4.8

5.9

~7 .q / . 'J'

3.8

8,4

NA

10.9

10.3

6.2

t . O

10.9

g./i

a Data for 1993 include mail and telephone questionnaires.

NA = Not Available.

Sources: Energy Information Administration. Housing Characteristics (1980-1993): Consumption ar

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 45
Some of these categories can be illustrated by the 1990 REQ: item on age of water he£.iir. ii, 

equipment (Item C-5). The item was skipped (Category 1) if the- preceding question "Doe:; fh:; 

main equipment for heating water for your home also heat watei for other buildings or hous:.n.i 

units?" was answered "Yes" or "Don't know." If item C-5 was asked, acceptable entries include.I 

"Don't know" (Category 2b(l)) and "As old as the house/orighai equipment" (Category 2c 

Standard responses (Category d) were ranges for number of ye-rrs. For this item, blank:; ;n 

"Don't know" responses would be counted as item nonresponse.

A few items specifically permit the interviewer to check "Refuseo"; on the 1990 questionnf.ir 

these were the items on family income, account numbers offuc suppliers, and relationship I' 1 

respondent of person to whom fuel bills are addressed. Except for family income. refus£.i-; b; 

these items had no direct effect on the survey estimates.

For the purpose of this chapter, item nonresponse will consist o; categories 2a and 2b, blanks 

and allowable nonresponse. Nonstandard responses generally provide some kind of usabh 

information. In the example given above, if the response to the item on age of water heair.u 

equipment were "As old as the house/original equipment," the response to the item on age ;) 

house or building would be used to assign an age range to the water heating equipment 

Nonresponse rates are calculated by dividing the number of househ ::>!ds with nonresponse (2a pi u; 

2b) by the number of households for which a response w;;s required (total number :>:' 

questionnaires minus those in. category 1).

Item nonresponse in the Household Survey has been reatK;;ly low for most items or 

questionnaires completed by personal interview. As noted above, many questions are rot 

included on the mail and telephone questionnaires. To the extent that these excluded items a;:oly 

to all housing units, the additional item nonresponse from this source has varied from 2,7 to 5,9 

percent mail response as 3. percent of total response depending on the survey year (see TYbU: 

4.1).

A tabulation of the 1990 RECS Household Survey personal interview data file prior to computer 

edits shows that, of 416 survey variables based on questionnaire entries, 51(12 percent) had item 

nonresponse rates of 5.0 percent or more. Variables related to .:ousehold measurements wee::' 

excluded from this count: nonresponse for this topic is discussed separately below.

Table 4.4 lists the 10 questionnaire items with the highest nonresponse rates in the 1990 RECS, 

Of the 10 items listed, only 3--age of water heater and two items related to household 

income required entries on more than 10 percent of the questionnaires. The basic household 

income item, which asked each sample household to report its total income in one of 25 da:; 5 

intervals, had a nonresponse rate of 14.4 percent, with half of the nonresponse being accounted 

for by refusals. Nonrespcmdents to the basic item were asked whether their income was above 

or below $35,000, the cutoff used for deciding whether to ask about participation in incoir.f:- 

tested government assistance programs. Nonresponse to this quest;on was 21.4 percent, so then; 

was no income information of any kind available for 3.1 percent of the sample households,

Energy Information Administration / Energy Consumption Series 

46 Residential Energy Consumption Survey Quality Prufile
Tabie 4.4. Ten Variables with Highest Item Nonresponse Rates: 1990 REGS

Question 

No. Description

Number of Housing Units

Entry 

Required for 

Item

lype of Response

Don't Know No Entry

L-5 Amount received 255 

from government for 

home heating costs

H-18 Amount per gallon 209 

paid for kerosene

B-5 Month of change in 137 

main heating fuel

I-8 Proportion of 4 

kerosene bill for 

nonhousehold uses

K-10 Household income 695 

over or under 

$35,000

P-7 Sales of agricultural 100 

products

72a

51

38

15

'Response category "Not sure."

"Includes 100 refusals.

"Includes 347 refusals.

Source: Preliminary tabulation of 1990 RECS data prior to edit changes.

10

10

38

K-10

L-4e

C-5

B-5

Household income

Other form of

government

payment for home 

heating costs

Age of water heater

Year of change in 

main heating fuel

4,840

239

4,089

137

250

1

400

7

445 C

33

171

12

Moriresponse

32.2

29.2

27.7

25.0

21.4

19.0

14.4

14.2

14.0

13.9

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 47
Items on age of equipment had relatively high nonresponse rate : as follows:

Water heater (shown in Table 4.4)

Main heating equipment

Central air-conditioning equipment

Most used window or wall unit

Most used refrigerator

Second most used refrigerator 5..'!

The higher nonresponse rates in this group were for the ages of :;qu;ipment that was less read it 

accessible at the time of the mterview. Perhaps for the sari';: re-r-ion. water heater capacity ";s 

a nonresponse rate of 10.t percent. Although no items on insiilanon were among the 10 with t 

highest nonresponse rates, 7 of 18 items relating to present;'; and extent of insulation h'< 

nonresponse rates in the range of 5 to 11 percent.

Households living in renta: units in multiunit buildings are non; likely to be unable to respnri 

to questions on age and other characteristics of equipment. In Tie 1990 REGS more than IM 

(57 percent) of the "doiri know" responses to the item on age ol main heating equipment avr 

from such households. Recent occupancy of the housing unit is another factor associated v.•'•[• 

some types of nonresponse. The 1990 data show that 91 per;;enl of the "don't know" respc :" ;<: 

to the question on main healing fuel used at the time of the l':)87 REGS were given by merrT':;; :: 

of households that had moved into their current residences subsequent to 1987.

The highest item nonresponse rate, 32.2 percent, was recorccd for a question on the total do!k 

amount received from the government for assistance with. aoine heating costs. Other do'l?;i 

amount items with high nonresponse rates were amount per gallon paid for kerosene., '•/.'•'• 

percent, and sales of agricultural products, 19.0 percent.

The extent of missing information on household size merits spec it! attention. REGS interviewer 

were instructed to measure tic dimensions of all "area enclosed from the weather," usirs; 

retractable 50-foot rneta! iape measure. Outside measurements v-crc preferred, but interviewer 

were allowed to measure from the inside when necessary. They were instructed to record writeof these options was employed for each housing unit. They were asked to prepare a rcu« 

diagram of the floor plan, indicating which areas were heaved, which were unheated, and ;,'o : 

dimensions of each.

Table 4.5 shows, for survey years 1980 through 1993, the ex lent to which the interviewer: 

succeeded in providing some or all of the household size information. (Comparable data on irta 

and heated floor space were not obtained in the 1978 and 1979 surveys.) The data in Table <"' .: 

cover only housing units for wnich personal interviews were conducted. The data for surw:-' 

years 1982 and 1984, the first two years in which RECS ircluc.cd a longitudinal component 

exclude a substantial number ;vf units for which the measurements -.vere taken from the data f: ii 

for the previous survey.

Energy nformation Administration / Energy GoniB.irnption Series 

48 Rei'icential Energy Consumption Survey Quslity Profile
Table 4.5. Housing Unit Survey, Completeness of Household Size information: 1980-1993

Category

Number of personal interviews 

completed3

Percent with:

Complete measurement 

information

Partial Information:

Unknown if inside or outside 

otherwise complete

Some measurements missing 

No usable information

Total

Survey Year

1980 1981 1982 1984 1887 loan i 1 <}«?"? £ tj la? \g \ i tJ &J jj

5,804 5,937 3,64 l,895c 5,856 4,82.8 6,918

81.5 82.2

15.4 15.4 

(9.9) (9.2)

(5.5) 

3.2

(6.2) 

2.4

100.0 100.0

62.4

32.3

(27.3)

(5.0)

5.3

100.0

56.0 73.0 75.4 66.4

37.5 25.8

(31.7) (20.7)

(5.9) (5.1)

6,4 1.2

100.0 100.0

o. i

100.0

"Excludes housing units for which mail or telephone questionnaires were completed. 

fc Excludes 827 units for which measurements from 1981 survey were used. 

cExcludes 584 units for which measurements from 1982 survey were used. 

Source: Energy Information Administration. Housing Characteristics (1980-1993).

The substantial year-to-year variation in the proportion of cases for which full information was 

obtained has resulted primarily from wide variations in the proportion of cases for which all 

required measurements were obtained, but interviewers did not specify whether they were inside 

or outside measurements. That: proportion peaked in the 1982 and 1984 surveys, at 27.3 and 31.7 

percent, respectively. In those same years, the proportions of cases with no usable information, 

5.3 and 6.4 percent, were also higher than in other years, although 1993 was not far behind, with 

5.1 percent in this category. The proportion of cases with some measurements missing (some of 

which may also have failed to distinguish heated and unheated areas) has been remarkably stable, 

in the vicinity of 5 or 6 percent.

These variations in the proportion of cases for which the method of measurement was not clearly 

specified may have resulted in part from changes in how interviewers were asked to record this 

information. Variations used from 1980 through 1993 have been as follows:

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 49
Location of Question on 

Survey Year Measurement Procedure Response Categories

1980 Start of measurements inside, outside

module

1981 Same as 1980 Inside, outside, other

1982 End of measurements Inside, outside, and 4

module other options

1984 Same as 1982 Same as 1982

1987 After each floor. Inside, outside, other

attic and basement

1990, 1993 Same as 1987 Same as 1987

The information on where the measurements were made i.s needed in order to standardize a 

measurements for all housing units to outside dimensions. I-or housing units known to have K 

measured on the inside, scale factors are used to convert their values to outside dimension;;. ' 

this purpose, all housing units for which the measurement nethod was not specified ", 

assumed to have been measured on the outside, this being tic much more common!;, .i c 

method, at least for single-unit structures.

Nonresponse in the Rental Age ; it Survey

As explained in the introduction to this chapter, the Rental Agent Survey covers househoi.cs 

multiunit structures that have one or more fuels included in their rent. Its main purpose U 

improve the quality of information about types of fuels used for .pace heating, water heating, z 

air-conditioning and the main type of heating equipment usec. 1 n 2 survey years, 1980 and 1 '-) ! 

permission was also requested from some of the rental agents to collect consumpticr. a 

expenditure data for buildings from their fuel suppliers, l.n those two years, both personal a 

telephone interviews were conducted; in other years, the ren.al agents have been cents.:,t 

entirely by telephone. Each rental agent is requested to provide information for all of the sari IT 

housing units for which the agent is responsible, whether the unr.s are located in a single bin I'M 

or in more than one building.

Table 4.6 shows response rate information for the Rental Age;:t Survey for survey years 1 ' ? 

through 1993 and partial Information for 1979 and 1980, The proportion of intervicvv 

households eligible for the Rental Agent Survey has varied from I 0.7 to 15.4 percent. The 'bUd: 

eligibility rates in 1984 and 1987 were probably associated \v- th the inclusion of low-:ine:::r 

supplements in those years. The average numbers of housing units per rental agent interview 

were also higher in those: years, as well as in 1981 and 1993. w''ie:a there were also low-in:rjr 

supplements.

Energy information Administration / Energy Consumption Series 

50 Residential Energy Consumption Survey Quality Profile
Table 4.6. Rental Agent Survey Eligibility and Response Rates by Survey Year: 1879-1993

Category

Year3

1979 1980 1981 1982 I 1984 1987

No. of household 6,269 4,724 5,682 6,229

1990

5,095

1993

7,111

281

1.96

764

10.7

625

81.8

285

2.19

interviews completed

Eligible for rental 

agent survey

Number NA NA 746 540 826 961 

Percent 11.9 11.4 14.5 15.4

Rental agent survey

completed

Number NA 551 466 308 549 856 

Percent of eligible NA NA 62.5 57.0 66.5 89.1

No. of rental agents 109b 283 203 168 210 303 

interviewed

Mean no. of housing units NA 1.95 2.30 1.75 2.61 2.83 

per rental agent __ ________

'Rental Agent Survey was conducted in 1978, but no data on response are available.

"Of 141 identified.

NA = Not Available.

Sources: Energy Information Administration, Housing Characteristics (1980-1993): Consumption and Expenditures (1979).

The response rates in Table 4.6 represent the proportion of eligible housing units for which 

Rental Agent Survey information was obtained. Because of more focussed and systematic efforts 

to contact rental agents, beginning with the 1987 survey, the response rates achieved for 1987, 

1990, and 1993 all in the 80 to 90 percent range-were substantially higher than those that had 

been achieved in the three prior survey years.

The probable consequence of nonresponse in the Rental Agent Survey is that the information for 

the items covered in that survey will be less accurate for the housing units for which interviews 

were not completed. Data from the Rental Agent Survey are compared with corresponding data 

from the Household Survey, and the former source is generally considered more accurate with 

respect to main fuels used and main heating equipment. Data for survey years 1981 through 1987 

show that one or more changes were made on the basis of this comparison for 30 percent of the 

housing units in 1981, 26 percent in 1982, 32 percent in 1984, and 42 percent in 1987. Most of 

the changes were for main heating fuel and equipment and main water-heating fuel.

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 51
Nomresponse in the Supplier Survey

The Supplier Survey covers households that pay the supplier directly for one or more of f:'v 

fuels: electricity, natural gas, fuel oil, kerosene, and LPG. It does not cover fuels included i 

the payment of rent. Fuels purchased on a cash and carry basis., primarily kerosene, are a.\- 

excluded. For each fuel, eligible housing units are asked 1:0 sign waiver forms allowing irm 

suppliers to provide billing information to the survey contractor.

Table 4.7 shows eligibility and response rates for the Supplier Survey for survey years 19 ; !1 

through 1993. For survey years 1980, 1981, and 1982, a single Supplier Survey form was i;::-;:. 

for fuel oil and kerosene: 'nence the data for fuel oil and kerosene are combined for these su -•>••• r 

years. From 1984 on. the iwo fuels are shown separately. Ii'<eept for kerosene, most oV ]'<•• 

households not eligible for me Supplier Survey occupied rent;;! units in buildings with ":-,\m < 

more units. A large proportion of the households that used kerosene were ineligible for H: 

supplier survey because they purchased it on a cash and ;:arry basis. Respondents for fix v 

households were asked to estimate the amount of kerosene used or purchased during the p,-;.:; 1 : .' 

months. For the 1982 survey year, following a substantial increase in the proportion o 

households using kerosene as a supplemental heating fuel, follrwup telephone calls were ma;:: 

to such households to request estimates of the amounts used during die 1982-1983 heating se;:m: : 

Estimates were obtained from 65 of 96 eligible households (EI ; \ 1984b, p.101).

Table 4.8 shows, for the 1980 to 1984 survey years, eligibility aces for the Supplier Survey :o 

electricity and natural gas by type of structure. For housing units in single-family strurrj.r ; 

eligibility rates were high for both fuels. They were lower for housing units in structures w :r 

2 to 4 units and lowest :n structures with 5 or more units. The corresponding data for fuel ; 

for 1984 (the only year for which separate data were avai. able for fuel oil and kerosene" sh< ! 

an even more pronounced pattern. The housing-unit eligibility rates were 98.0 percent for sing :: 

family structures, 37.6 percent for structures with 2 to 4 units,, arid only 2.2 percent for strueiuje; 

with 5 or more units. Most large apartment buildings that use fuel oil for heating have cent ; 

systems, with no metering of consumption by individual units.

The primary measure of completeness of response for the Supplier Survey is the proporlio i .r 

eligible housing units for which usable records were obtained, A s shown in Table 4.7, these 'mi::: 

have been consistently high, ir the neighborhood of 90 percetr. for electricity and natural u r 

They have been somewhat lower for fuel oil and LPG and very low for kerosene. Except In 

natural gas, the lowest rates for ail fuels occurred in the 1984 survey.

The primary reason for inonresponse in the Supplier Survey for electricity, natural gas, and .,1 r 

has been failure to obtair. permission from respondents to contact their suppliers. For these rue!; 

the proportion of eligible respondents not providing waivers has consistently been between ;; a';:: 

10 percent. Other reasons for failure to obtain usable records include: supplier unknown or r 

contacted; supplier refused to participate (a very small proportion for all fuels); supplier em .:: 

not find household in records; and supplier did not provide records covering a sufficient part r 

the desired reference period. Records for electricity and natunJ gas were considered uriuml Vif they covered less than 5 rronths and included seasonal use (healing or cooling) or covered ! ;- ••.• 

than 2 months. Records for fuel oil. kerosene, and LPG were considered unusable if ,h

Energy information Administration / Energy Consumption Series 

52 Residential Energy Consumption Survey Cualitv Profile
Table 4.7. Supplier Survey Eligibility Response by Fuel and Survey Year: 1980-

Fuel Type

and

Survey Year

Number of

Households

Using Fuel"

. ,..,. 

Percent of Total

Households

Eiigibie

for

Supplier

Survey*1

Eligible

and

Usable

Records

Obtained

Percent of Eiigibie Households

for which:

Usable

Records

Obtained

Unusable Records

| No Waiver I Other

Obtained ! Reasons

Electricity

1980

1981

1982

1984

1987

1990

1993

6,048

6,263

4,721

5,677

6,228

5,094

7,108

Natural gas

Fuel

Fuel

1980

1981

1982

1984

1987

1990

1993

oil/kerosene

1980

1981

1982

oil

1984

1987

1990

1993

3,725

3,850

2,951

3,599

3,990

3,255

4,069

1,132

1,122

863

918

951

700

865

Kerosene

LPG

1984

1987

1990

1993

1980

1981

1982

1984

1987

1990

1993

421

414

278

272

574

627

413

525

543

461

684

93.0

92.2

92.4

91.0

92.5

94.0

95.1

83.9

81.7

82.3

79.0

83.3

85.8

86.2

91.7

93.2

94.7

68.1

75.3

80.9

82.1

50.5

36.5

37.4

45.2

95.7

97.3

94.0

93.7

94.5

95.2

97.1

82.5

80.8

83.4

79.5

83.0

85.1

85.6

75.2

71.7

74.3

70.0

74.0

76.6

77.0

54.6

46.7

48.3

43.2

55.6

58.1

60.5

9.7

11.6

10.1

12.9

65.5

61.3

67.3

58.5

64.5

65.3

71.6

88.7

87.6

90.3

87.4

89.7

90.5

90.0

89.6

87.8

90.3

88.6

88.8

89.3

89.4

59.5

50.1

51.0

63.4

73.8

71.8

73.8

19.2

31.8

27.0

28.4

68.4

63.0

71.6

62.4

68.3

68.6

73.8

6.9

8.9

7.0

8.1

7.2

5.7

5.6

3.8

9.2

8.8

8.1

7.6

5.7

6.2

7.9

7.3

5.6

12.2

8.4

7.5

5.7

8.5

1.4

2.9

5.8

8.0

8.2

6.9

7.9

9.3

6.4

5.2

4.4

3.5

2.7

4.5

3.1

3.8

4.4

3.6

3.0

2.9

3.3

3.6

5.0

4.4

32.6

42.6

43.4

24.4

17.8

20.7

20.5

72.3

66.8

70.1

65.8

23.6

28.8

21.5

29.7

22.4

25.0

21.0

'Includes households for which mail questionnaires were obtained.

bFor kerosene, excludes households for which estimated purchases were reported in the Household Survey.

Source: Energy Information Administration, Consumption and Expenditures (1980-1993).

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 53
Table 4.8, Supplier Survey Eligibility Rates for Electricity and Natural Gas by Structure Typo: 

1984

Fuel Type 

and Year

Ti

Single 

Family

fps of Stnuetu

2-4 

Units

re

>5

Units

Mobile 

Horn a

Electricity

1980

1981

1982

1984

Natural Gas

1980

1981

1982

1984

98.6

98.8

98.4

98.2

98.3

98.4

98.3

98.1

68.4

65.7

69.5

68.5

25.6

17.7

27.5

21.9

99,5

93/;

9:2.8

89.2

78.4

83.2 85.'

88.2

Source: Energy Information Administration, Consumption and Expenditures i198C-1984).

did not cover a full year. The tighter requirement for the latter three fuels has led to some 1 ,'. 1 

higher rejection rates for records submitted by their supplier;-;. For kerosene and L::'C: 

significant part of the nonresponse has occurred because cornprries were unknown or W'=r 

contacted for other reasons.

For each fuel, the proportion of eligible households for \vhich consumption was impined 

estimated was lowest in che 1993 RECS. The high estimation/imputation rates for kerosene 00 

partly because only one-half to one-third of the household::; using kerosene were eligible fo: 

Supplier Survey and partly because Supplier Survey data were successfully obtained for in 

than one-third of the eligible households each year.

When supplier survey information is not obtained for an. 

consumption of the affected fuel for that household is estim 

regression model that: make:; use of reported data for relevant c! 

The same methods are used to estimate or impute consumotioi 

the supplier survey. Consumption and cost information deri 

necessarily 100 percent accurate for every housing unit (see i 

nevertheless, the precision and reliability of such information is 

than that of values imputed for housing units that were rtoi: eli 

for which the supplier did not respond. Based on the data sb 

proportion of user households for which consumption arid c 

kerosene, based largely on user estimates) are:

eligible sample household. 

ated or imputed by a nod inlaracteristics of the housing LIT 

i for households not eligibk: 

ved from billing records :.'; 

urther discussion in Chapter 

likely to be substantially grw? 

gible for the Supplier Survey 

3'Wri in Table 4.7, ranges o: 

:>s(. have been imputed (or.

54

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Fuel Type Years Proportion Imputed

Electricity 1980-1993 14.4 to 20.5 percent

Natural Gas 1980-1993 23.0 to 30.0 percent

Fuel Oil 1984-1993 39.5 to 56.8 percent

Kerosene 1984-1993 87.1 to 90.3 percent 1 '

LPG 1980-1993 28.4 to 41.5 percent

"Includes units for which consumption was estimated by the household respondent.

Summary

This chapter has presented information about unit and item nonrespon.se in the three data 

collection components of RECS: the Household Survey, the Rental Agent Survey, and the 

Supplier Survey. A summary of the highlights follows.

Household Survey

9 Overall (personal interview, plus mail and telephone) unit response rates have varied 

from a high of 9 1.6 percent in the 1981 RECS to a low of 81.2 percent in 1993 (Table 

4.1).

8 Between the 1978 and 1990 survey years, the percentage of eligible housing units for 

which mail questionnaires were completed varied between a high of 5.3 percent in 

1978 and a low of 3.0 percent in 1984. In the 1993 RECS, mail or telephone 

questionnaires were completed for only 2.2 percent of the eligible units (Table 4.1). 

The mail and telephone questionnaires are abbreviated versions of the personal 

interview questionnaires; hence item nonresponse is automatic for all items not 

included on these questionnaires.

^ . The inclusion of a longitudinal component to RECS in survey years 1982 to 1990 led 

unit nonresponse each year for households that had been included in

the prior survey (Table 4.2). The de^fee was greatest for households that participated 

in the Residential Transportation EnergyxConsumption Survey in the interval between 

the RECS survey years. X

The highest unit nonresponse rates have consistently occurred in the Northeast Region. 

in central cities, and in buildings containing more than one housing unit (Table 4.3).

Item nonresponse rates for questionnaires completed by personal interview have been 

relatively low, with only 12 percent of the items having nonresponse rates of 5.0 

percent or more in 1990. The highest nonresponse rate for an item required of all 

sample households was 14.4 percent for family income (Table 4.4).

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
The proportion of interviewed housing units wi-.i: complete information or 

measurements has varied from a high of 82.2 pe -ccr. ^n 1981 to a low of 56.0 pen. 

in 1984. The most common omission has been fail .re of the interviewer to irc.it 

whether measurements were made inside or outside the housing unit (Table 4 f ).

Rental Agent Survey

The proportion of households eligible for the Kcni.il Agent Survey lias varkr. V 

10.7 to 1 5.4 percent of all households for which -.piestionnaires were obtained 

interview or mail. Response rates for eligible aousdiolds have ranged from a !ov 

57.0 percent in 1982 to a high of 89.1 percent if, f ( H 7 and have exceeded 80 ru:n. 

in each of the last three survey years (Table 4 6).

Supplier Survey

The proportion oT households eligible for the Supplier Survey varies by fuel am ' 

of structure. Eligibility rates have consistently been aver 90 percent for electric t\ v 

LPG, and close to or above 80 percent for natural i_.as. Rates for fuel oil have ha: 

somewhat lower. Most of the households not eligibl; for the Supplier Survey for :.f . 

fuels arc living i 7 rental units located in buildings v ah two or more units (TabSe ' !

Low eligibility rates for the Supplier Survey foi ken s:nc occur largely because ^i r 

of the householcs using that fuel purchase i) oniy c.i a cash and carry basis.

Among household!! eligible for the Supplier Sun ;y. generally between 5 arid 

percent have beer, unwilling to sign a waiver to alio's the survey contractor to am i' 

their suppliers (fable 4.7).

Taking into account the joint effects of eligibility and the extent of succa:::- ' 

obtaining usable records for eligible householcs. -jpplier data have been uhui: a: 

consistently for close to or better than 80 perecn. uf all electricity users, o/.r 

percent of natural Lias users, and close to or betxcr thai 60 percent of LPG use/;; 

fuel oik the corresponding rates have varied from 4! percent in 1984 to 60 pcacon 

1993, and for kerosene they have been in the \icini.\ of 10 percent (Table 4.7] a 

this measure., the best results for each of the :1 niaxa' fuels was obtained in the ! < : 

Supplier Survey

Energy Information Administration / Energy Consirnption Series 

56 Residential Energy Consumption Survey Qua |: .y :: rofile
ERRATA

Energy Consumption Series:

Residential Energy Consumption Survey Quality Profile

March 1996

DOE/EIA-0555(96)/1 

The following change should be made to the third bullet on page 55:

Change "decline" to "increase". 

The bullet should read as follows:

The inclusion of a longitudinal component to RECS in survey years 1982 to 1990 led 

to an increase in unit nonresponse each year for households that had been included in 

the prior survey (Table 4.2). The Increase was greatest for households that participated 

in the Residential Transportation Energy Consumption Survey in the interval between 

the RECS survey years.

We regret any inconvenience this may have caused.
This chapter presents information about measurement errors associated with the data collection 

phase of RECS, Measurement error can contribute to the total error of survey estimates in the 

form of bias or nonsampling variance. Direct or indirect quantitative information about 

measurement errors in RECS can be obtained in several different ways.

Special data collection procedures, which generally cost more than standard interviews but are 

believed to provide more precise information, can be used to collect information for selected 

households. These procedures often involve various kinds of direct observation and physical 

measurement, as opposed to merely asking for information from survey respondents. Such 

procedures include energy assessments in which data on household characteristics are collected 

by trained technicians. Other examples are the collection of nameplate data in order to obtain 

more precise information on the characteristics of central air-conditioning units and the collection 

of information on thermostat settings and temperatures by direct observation rather than by asking 

respondents to report them. Another useful procedure has been to conduct personal or telephone 

interviews with "outliers" i.e.. households identified in the data processing phase of the survey 

as having reported unusual or apparently inconsistent values for selected items. These kinds of 

procedures are reviewed in the first section.

Comparisons of data for the same household from different sources provide another kind of 

information about measurement error. Cross-sectional comparisons involve data for a household 

for the same time period from the household, rental agent, and supplier surveys; longitudinal 

comparisons involve data for the same household from successive survey years. Weather data 

assigned to households in a specified geographic area can also come from more than one source. 

Results from these types of comparisons are presented in the second section.

The level of measurement error can also be affected by the design and format of the survey 

questionnaire and by the type of training administered to interviewers. Information on these 

topics is presented in the final section.

RECS estimates of end-use energy consumption within households are obtained indirectly from 

survey data by allocating total consumption to various uses on the basis of a nonlinear regression 

model. These estimates and their evaluation through submetering studies are covered in Chapter 

7, "Estimation and Sampling Error/ 1 Macro-comparisons, that is, comparisons of RECS estimates 

with comparable data from other surveys conducted by EIA and from surveys conducted by other 

agencies and organizations, are discussed in Chapter 8, "Comparisons of RECS Estimates with 

Other Data."

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 57
af Data Collection Procedures 

Energy Assessments

In 1979, following the National Interim Energy Consumption Survey (NIECS), an Er:f::!;: 

Assessment was undertaken by Technology and Economics Inc . of Cambridge, Massachus;d:t : 

in a subsample of 44 of the NIECS sample households. 'rained technicians visitec ':'.' : 

households, all but two of which were single-family households. They measured floor area ; 

counted windows, examined insulation and noted the characteristics of space-conditionir;;: 

equipment and selected appliances. Their observations were compared with the responses ':o i)'i:: 

NIECS interviews (Blumstem. York, and Kemp 1981).

This Energy Assessment was undertaken as a pilot test for a continuing program of assesstnen:.; 

that was being considered a--; a regular part of RECS (Response Analysis Corporation 19>'( 

Part 6). The plan was to perform such assessments for a subsarnple of the households include.I 

in each Household Survey. However, resources available for RECS proved to be insufficient : ' 

implement this plan and there have been no further assessments of this type. The generality r 

the Assessment findings was limited by the use of a small convenience sample, lack of fu.il,' 

standardized procedures, and limited training for the technicians. In. addition, the data colleciio 

instrument was not designed for direct comparisons with corresponding NIECS data items ;n 

there was no followup to reconcile differences between the two sources of informaticr 

Nevertheless, the Assessment provided useful information about possible sources of measurermrri 

errors in NIECS and subsequent surveys.

There were large differences between the NIECS and A c ses'jrnent data on square feet ;r 

floorspace (Table 5.1). For "A of the 27 households that had u ; .able measurements from boil 

sources, differences were 25 percent or more of the Assessment values. NIECS respondent;; 'ha!.. 

been asked to give their best estimates of floorspace; in the Assessment the technicians rr-£.di 

measurements. Some of the discrepancies may have been due i:c z conceptual difference: NII:Xl:i 

respondents were asked to report square feet of living space,, while the Assessment technician, 

were asked to measure "conditioned space." including only room;: aid other enclosed areas v/rl 

some direct means of heating. On this basis, one might expect the 'vECS values to be somev/ha 

larger; nevertheless, for 9 of "he 27 households, the NIECS values v:re 25 percent or more belt: v 

the Assessment measures.

Despite their limitations, the Assessment findings on floorspace demonstrated that responded' 

estimates of floorspace were likely to be subject to unacceptably large errors. Consequently, frorr 

RECS survey year 1980 on, measurement by survey interviewers has been the preferred metier. 

of obtaining information on floorspace. The measurement procedure used in the Assessment ',vi: 

itself not fully satisfactory and has been replaced in RECS by procedures that are believed t: :x; 

easier to use and more reliable.

Energy Information Administration / Energy ConsLtriptian Series 

58 Residential Energy Consumption Survey Quality Profile
Table 5.1. Comparison of NIECS and Energy Assessment Data on Floorspace

Percent

Difference

(NIECS - EA ¥ 100} ~ licfict rm

EA NIECS > EA

0 to 9.9

10

25

50

0 to 24.9

0 to 49.9

0 and over

Total

Number of Households

NIECS < EA Total

8 1 9

2 24

2 46

3 58

15 12 27a

'There was no space information from one or both sources for 17 of the EA households.

Source: Blumstein, York, and Kemp, An Assessment of the National interim Energy Consumption Survey (1981), Table 8.

Some other findings from the comparison of NIECS and Energy Assessment data were:

8 There were many differences between counts of windows, both by type and overall for 

the household.

o The presence or absence of attic insulation was reported accurately, but there were 

substantial differences in reports of the thickness of insulation used.

 Reports of fuel used for heating and other purposes were generally in agreement. An 

exception was fuel used for dryers; of 33 households for which the dryer fuel was 

reported in both NIECS and the Assessment, there were differences for 7, all of which 

reported electricity in the Assessment and gas in NIECS.

8 Several differences were observed in the numbers of refrigerators and separate food 

freezers reported and in the characteristics of refrigerators, such as temperature 

controls and automatic defrost/frost free features.

These findings from comparisons of NIECS and Energy Assessment Data were taken into account 

in the determination of content and formulation of questions for subsequent surveys.

Collection of

In the 1990 Household Survey, interviewers were asked, for single family houses with central airconditioning, to record manufacturer's name, model number, year manufactured, and other 

information from the nameplate of the outside unit (Hall 1992). The main purpose of collecting 

this information was to obtain a measure of rated efficiency for each housing unit's central airconditioning equipment. This was to be done by matching the make and the model year and 

number against semi-annual directories of equipment characteristics issued by the

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 59
Air-conditioning and Refrigeration Institute (ART). For aJ. successful matches, the seas;: 

energy efficiency ratio (SEER) for the equipment was enterec nto the RECS data file.

Table 5.2 shows the results of the attempts to acquire SSER's for central air-conditi 

equipment. No nameplate data were obtained for 26.5 pexerr of the 1,820 households 

central air-conditioning, either because they were located in muhiunit buildings or because 

had responded by mail. Directory matches were attempted for the remaining 1,337 house b 

SEER's were obtained for only 24.8 percent of those households, or 18.2 percent of all sa 

households with central air conditioning. The most frequerr: reasons for failure to find a * 

in the directories were failure to match on manufacturer's nam-:; and failure to match on n: 

number.

A subsequent effort was made, for the 331 households for which SEER's had been obtained 

obtain capacity values from the ARI directories. Values were 1; cated for 279 (84.3 percent: 

these households. In view of the high cost and limited success; i»f the nameplate data collect-< 

and matching operations in the 1990 RECS, they were not undertaken in 1993.

Table 5.2. Results of Matching Nameplate Data Against ARI Directories to Obtain Seasonal Eir 6 re 

Efficiency Ratios: 1990 RECS

Outcome

of Match

Percent

Number 

of Units

Of Total

Households with central air conditioning 

No match attempted

Mail questionnaire 

Multi-unit building

Match attempted 

Successful, SEER obtained 

No SEER obtained

No match on make 

No model year 

No match on model 

No SEER available3

1,820

483

99

384

1,337

331

1,006

574

23

283

126

100.0

26.5

5.4

21.1

73.5

18.2

55.3

31.5

1.3

15.6

6.9

Of Attempts! 

Matches

'Unit manufactured prior to "980 or ro SEER in directory.

Source: Hall, Nameplate Date Collection in the 1990 RECS (1992).

60

Energy Information Administration /Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Checking Thermostat

Since 1981, the RECS Household Survey questionnaires have included questions on average 

temperatures maintained in the home in the wintertime under three conditions: during daytime 

when someone is home, during daytime with no one home, and during sleeping hours. If 

respondents say they cannot report temperatures but can give thermostat settings, the latter are 

accepted. These self-reported temperatures are characterized in the survey reports as follows:

The self-reported temperatures, especially for some respondents, are impressions of 

typical temperatures and may not represent actual temperatures, or the averages of actual 

temperatures in the home. (ElA. 1993a, p. 148)

There have been no attempts in RECS to collect information about indoor temperatures or 

thermostat settings by direct observation. However, a study in a small city in New York State 

provided some information on the accuracy of self-reported thermostat settings (Luyben 1982). 

Data were collected for one sample of households by personal interviews and for another sample 

by telephone. In the telephone survey, respondents were first asked to report their thermostat 

settings and then to go to their thermostats and check the reported values. The mean of the 

checked values was significantly higher than the reported values by 0.6 degrees Fahrenheit.

In the personal interviews, the interviewers recorded observed values of thermostat settings and 

temperature readings. Temperature readings exceeded thermostat settings by a mean of 0.8 

degrees. The mean of the observed settings was significantly higher than the mean of the 

checked settings from the telephone survey households: 68.3 versus 67.0 degrees.

Detection and Evaluation of Outliers

The detection and analysis of outliers can be a useful technique for understanding survey 

responses, identifying and controlling survey errors, and improving survey processes. Outliers 

are reported values that lie at the extremes of a univariate or multivariate distribution of variables 

included in the survey. The analysis of outliers can include recontacts with survey respondents 

to determine whether there were errors in the values initially reported and whether there were 

special circumstances to explain the unusual observations.

In March of 1984, in-depth reinterviews were conducted with eight households that had 

participated in the 1981 RECS and for which data on consumption were available from suppliers 

(ElA 1984b, Appendix G, Erickson 1984). The method used to identify these eight households 

as outliers was to impute their consumption of specific fuels, using the regression models 

normally used to impute missing data on consumption and to compare the imputed values of 

consumption with the values actually provided by the suppliers. A purposive sample of eight 

households showing large differences in either direction for consumption of electricity, natural 

gas, or fuel oil was selected for the interviews. These were households whose consumption 

appeared to be far out of line with what might have been expected on the basis of housing unit 

characteristics and household behaviors that had been reported in the initial interviews.

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 61
There were two sets of four interviews each. Set A was conducted by a pair of interviewers. ::u ; 

Set B by a single interviewer. All used conventional ethnographic interviewing techniques. ' >c I 

primary goal was to arrive at an explanation for the unusually his.'h or low consumption, but Li/; : 

also investigated several broad topics, including family nteractions. recreation, 'no-:- :. 

improvements and attitudes toward utility companies, nuclear [rnver, conservation, rising :: ;s 

of energy, and family finances.

The main findings of the eight in-depth interviews are presented :n Figure 5.1. The interviev-: : 

were successful, for the most part, in finding reasonable exp amdons for the extreme value, ; i 

consumption of specific fue:s. The explanations proved tc b; nore or less equally divide-::: 

between reporting errors in the initial RECS interviews (six of in. eight households) and unu;;u; I 

circumstances affecting consumption, some of which may not h r.ve been fully reflected in ii:;: 

imputation model (also present in six of the eight households).

An important motivation for undertaking these interviews v as to determine whether h 

questionnaire for the 1984 R3CS could be expanded to include r formation that would help i 

explain patterns of unusually high or low consumption. After lie findings were reviewed, i 

questions were added but consideration was given to other changes in survey procedures, ;;i:;: 

as improved interviewer training, additional processing steps, -;.nd followup interviews, On: 

outcome has been the inclusion of "model-based outlier checks' 1 as a standard part of :n-<:- 

processing. In processing the 1990 RECS data, for example there was a manual revjev, 

sometimes involving telephone calls to respondents, of data foi all households for which d.i : 

model-based estimate of fuel, consumption was more than three :imes or less than one-third, o 

the value based on Supplier Survey data (Response Analysis Coynoration 1992b. p.7-14).

Another outlier investigation, .associated with the 1981 RECS had :.c: do with data on temperai:i:.n 

settings, a topic that had been included in the questionnaire for the first time in that survey yea 

(Thompson 1982. Day 1982) The survey contractor made te. ephone calls to 9 respondents ^-i.:>j, 

reported maintaining (with a thermostat, radiator valve, or other ontrol) nighttime temper a. tu^e 

higher than their daytime temperatures (presumably a reversal from normal behavior) and ro ' 

respondents who reported nighttime temperatures substantial'y lower than their daytirii 

temperatures.

In the first group, nighttime higher than daytime, eight of the niyu:- respondents called chang.ec 

their responses in ways that reduced the differences; however, all but two of the group confirmee 

that they purposely maintained higher temperatures at night and provided explanations for :ha 

behavior. In the second group, nighttime much lower than dayuryie. all but one confirmed 'lieu 

original responses, although some said they were uncertain about he precise temperature lev en; 

Most of the explanations involved use of electric blankets or a wacrr combination of non-clary -.<. 

blankets. The findings suggested that responses to question:; ab;:,it temperatures maintained :r 

the housing unit are subject to sizable response errors.

An analysis of outliers in the 1984 RECS by Eatta (1988) suggest:; the potential power of :h ;; 

method. The goal of Latta's analysis was to improve the :non!it ear regression model used, c 

impute missing entries for heated floorspace. He used data for sample housing units v/iyJh 

complete information to estimate the parameters of the proposed ynodel and observed that ii<= ;:

Energy Information Administration / Energy Corns i.m ption Series 

62 Res-dental Energy Consumption Survey Qunl'ty Profile
were several extreme outliers, units with large differences between reported and imputed values. 

An examination of the data listings for these units showed substantial clustering by PSU and 

interviewer, leading to a hypothesis that a few interviewers may have been making systematic 

errors in identifying which portion of total floorspace was heated. One set of outliers consisted 

of four townhouse units, each with three floors whose dimensions had been recorded as 7 by 30 

feet, a rather unlikely set of measurements. Because this analysis was undertaken well after 

completion of the 1984 RECS. there was no followup on these particular cases. However, the 

findings indicate that comparison of reported and imputed values followed by review and 

followup of outliers is a promising technique for quality improvement.

Comparisons of Household

The design of RECS provides built-in opportunities to investigate the nature and size of 

measurement errors through the analysis of multiple observations of the characteristics of 

individual housing units or households. The longitudinal component of the sample design 

provides observations for the same housing units at different times. The collection of overlapping 

data for selected items from three sources households, rental agents and suppliers provides 

duplicate observations for the same housing units at the same time or covering the same time 

period. The interpretation of data from multiple observations is not necessarily straightforward; 

the sources of the observations must be carefully considered to decide what they tell us about the 

effects of response bias or response variability on the survey estimates.

Longitudinal

As described in Chapter 2, the RECS samples for 1982, 1984, 1987, and 1990 each contained a 

subsample of housing units which had been included in the sample in the preceding survey year. 

Because a large proportion of the questionnaire content is repeated in successive survey years. 

responses to comparable items for the same unit in the 2 years can be compared. The 

interpretation of observed differences is not obvious. For some housing unit characteristics, such 

as year built and type of housing unit (mobile home, single-family detached, etc.), there should 

be no differences from one survey year to another, so that differences are almost certainly due 

to errors in data collection or processing. For other housing characteristics, such as appliances, 

types of fuels used, and. even number of rooms, real changes can occur. Real changes in 

household characteristics, like number of persons and family income, can occur whether or not 

a different household occupies the housing unit in successive survey years.

Table 5.3 provides information about differences in selected items for housing units that were 

occupied by the same households in the 1980 and 3982 RECS (Thompson 1985a). In an effort 

to determine the reasons for individual differences, telephone calls were made to households for 

which the responses for 1980 and 1982 differed for one or more of the selected items. Only 71 

percent of the differences were checked in this way: 12 percent were eliminated to reduce burden 

on households with more than three items showing differences and 19 percent were associated 

with households that could not be reached by telephone. Thus, the final column of the table, 

showing the percent of differences "unexplained," includes some differences for which 

interviewers and respondents could not provide any explanation and some which were not covered 

by telephone calls to respondents.

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 63
Table 5.3. Differences ESetwean Responses Reported by the Sams Household in the 1980 arid * 

REGS

Item

1. Number of windows 6

2. Year the house was ouilf

3. Main home heating 

 equipment1"

 A. Number of stories'

5. Year moved in

6. A/C equipment present

7. Number of rooms0

8. Use a home freezer

9. Basement heated or 

 unheated0

10. Number of refrigerators

11. Full basement/part 

 basement1

12. Availability of natural gas':

13. Use a clothes dryer

14. Type of living quarters

15. Number of bathrooms'1

16. Main home heating fuel

17. Main water heating fuel

18. Presence of a basement1

19. Main cooking fuei

Base3

Differences

Number Q/ /o

3 ercent of differences:

Explained 

by 

Re nl 

Change

Explained 

by 

Errors" Unexplai

1,398

1,296

1,394

337

296

206

145

194

179

165

149

110

128

96

32

115

107

91

87

84

45

40

2.4.1 

22.8 

14.8

14.5

13.9

12.8

11.8

10.7

9.9

9.3 

8.6

7.6 

6.5 

6.2 

6.0 

4.1 

2.9

65

12

19

74

21

36

44

23

71

37

64

33

13

63

41

24

19

71

12

"Base excludes households for wh : c.-. 1980 RECS response is imputed or unk.ncvv an: trose for which 1982 REGS respond K 

unknown.

"Some responses are grouped for these itens. For a difference to be counted, : t rnus: 'K >3 windows; the years 1975-79 ",; e <; 

combined into one category; hot water pipes and radiators were combined as one he?,ting tyf.tem; full and 1/2 baths were con3in=.c: 

and each counted as one bathroom; the difference between number of rooms mus: be : '

"Single-family homes.

dSingle-family or mobile homes ihat do not use natural gas.

"Errors by respondents, interviewers, coders, and data entry operators.

Source: Thompson, Utility of Paying Respondents: Evidence from the RECS (1985;

64

Energy Information Administration /Energy Consumption Series 

Resirdenti.il Energy Consumption Survey QusPtv Profi!e
For 9 of the 19 items shown in Table 5.3, real change accounted for at least 10 percent of the 

observed differences. These are minimum values, because some of the unexplained differences 

may have resulted from real changes. Such real changes appear to be largely related to the 

acquisition of new appliances and heating or cooling equipment and to changes in the availability 

of natural gas, making possible changes in the main heating fuel used.

For 6 of the 19 items, the attempt at reconciliation of differences confirmed that at least half of 

them resulted from errors in data collection or processing. These were items for which one 

would expect few, if any, real changes to occur: number of windows, number of stories, type 

of living quarters, presence of a basement, basement heated or unheated. and full basement or 

part basement. The general conclusion to be drawn from these findings is that, at the level of 

the individual housing unit, real changes over time are difficult to distinguish from differences 

due to measurement errors. Because essentially the same data collection procedures were used 

in both years, we can also conclude that estimates for some housing unit characteristics, notably 

number of windows, are subject to high response variability.

Measures of total and heated floorspace were also compared for a subsample of 355 housing units 

included in the 1980 and 1982 RFCS (El A 1984b, p. 114-11 5). The results for 300 housing units 

that had usable square footage data tor both years are shown in Table 5.4. Averages for the total 

and single-family detached units were fairly close for the two survey years. However, the median 

absolute percent differences between values for individual units for the two years were relatively 

large, 11.7 percent overall for total square footage. The}1 were larger, at 15.6 percent, for heated 

square footage, probably because of uncertainties about the interpretation of the concept of a 

"heated area," possibly also in part because of some real changes in this item.

Longitudinal comparisons of 1982 and 1984 data, also with telephone calls to explain differences, 

were undertaken following the 1984 RECS. In this instance, only seven topics were selected for 

analysis: main home heating fuel, main water heating fuel, air- conditioning equipment and fuel, 

clothes dryers, home freezers, dishwashers, and availability of natural gas. Telephone contacts 

were successfully completed for 505 (76 percent) of the 668 differences that were found for these 

seven topics. Real changes explained 42 percent of these differences; virtually all of the rest 

resulted from errors in the 1982 or 1984 values or, in a few instances, errors in both years (EIA 

1987d).

Records for the longitudinal differences for which respondents were successfully contacted are 

included in the public use files for the 1982 and 1984 RECS. There is a separate record for each 

difference showing the topic number, a code for the interpretation of the difference (year 1 

correct, year 2 correct, neither year correct, real change, or cannot determine) and a code 

identifying the reason for the error, if one occurred. As noted below, some longitudinal 

comparisons have been made for 1984-1987, and comparisons are possible for 1987-1990. but 

no followup contacts were made, following the 1987 or 1990 RECS, to determine reasons for 

differences.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 65
Table 5.4. Differences in Square Footage Reported for the Same Household in the 1980 and! 

RECS

Item

Number of cases3

Average Square Feet 

per Housing Unit

1980

1982

Median Percent 

Difference in Square 

Footage

Average Heated Square 

Footage per Housing 

Unit

1980

1982

Median Percent 

Difference in Heated 

Square Footage

Total

Single-Family

Detached

Housinc

Mobile

Home

Type

Multi-unit

Building

H Oil S3 1 HID

Type

Responses;

Differ in

1980 & 198::>

300

1,797

1.821

11.7

208

2,116

2,142

11?

8C 3

70

1,082

1.147

12.2

536

521

15.6

1,780

1,751

16.9

966

1.039

14.4

"Units that had good square foo".age; data for both years.

Source: Energy Information Adminlstrs'ion Consumption and Expenditures (1982)

Table 5.5 shows results of a comparison of 1984 and 1987 EEC'S data on type of housing 'Jrit 

for units that were included in both surveys. The final column shows the index of inconsistency 

for each category. The index of inconsistency is a measure of the percent of total variance lor 

an item that is accounted for by response variance. As a rough ruin of thumb, response variance 

is considered to be low when the index is less than 20, moderate for values between 20 and 50, 

and high when it is greater than 50. (For further discussion and a formula for calculating tru; 

index, see Groves (1989).) The value of the index for the "single family attached" category is 

at the upper end of the moderate range, indicating that there were frequent difficulties :in 

distinguishing such units from single family detached units and from ':hose in apartment buildir.&,;; i 

with two to four units.

Table 5.6 shows a comparison, also based on housing units included in both the 1984 and. 1987 

RECS, for reports on year of construction of the housing unit (Ba~.t!es 1991 a). This tabulation 

is limited to housing units that were occupied by the same househuld in 1984 and 1987 and fo: 1 

which the householder was the respondent in both years. Th?. values of the index ::T 

inconsistency are in the moderate to high ranges, higher fo: the most part than the val'j? ;

66

Energy Information Administration / Energy Consajnpticm Series 

Residential Energy Consumption Survey Quality Pnfife
observed for type of housing unit in Table 5,5. As one might expect, the values are smaller for 

the most recent periods. They are also smaller for units built prior to 1940, presumably because 

the time period covered by that category is much longer.

Table 5.5. Longitudinal Households3 : Housing Type Reported in 1984 and 1987

Housing 

Type 

Reported 

in 1984

Housing Type Reported in 198?

Mobile 

Home

SingieFamiiy

Detached _i

SingleFamiiy 

Attached

Apartment Bldg,

2-4 Units

Mobile Home 115 900 

Single-Family 9 1,285 16 20

5+ Units

Index 

of 

inconsistency

0

1

7.7 

Single-Family 9.5

Detached

Single-Family

Attached

Apt. Bldg. 2-4

Units

Apt. Bldg. 5+

Units

9

0

0

0

1,285

26

10

0

16

53

21

6

20

14

209

10

1

2

10

269

9.5

46.7

19.2

5.9

"Tabulation excludes 15 cases where it was determined that different housing units had been interviewed and one case where 

the basement had been converted to an apartment. 

Source: Energy Information Administration, Housing Characteristics (1987).

Table 5.6. Longitudinal Households 3 : Ysar of Construction Reported in 1984 and 1987 RECS

Year of 

Construction 

Reported in 

1984

Year of Construction

Before 

1940

1940- 

1949

1950- 

1959

Reported in the 1987 RECS

1960=

1969

1970- 

1974

1975- 

1979

1980-

1984

Total 

Units

 

index of 

Inconsis 

tency

Before 1940

1940 to 1949

1950 to 1959

1960 to 1969

1970 to 1974

1975 to 1979

1980 to 1984

Total Units

333

27

23

9

8

3

1

404

42

59

15

11

6

0

0

133

26

19

134

40

6

5

4

236

22

7

34

145

22

3

1

234

12

2

7

25

95

26

3

170

6

0

4

13

17

114

7

161

1

2

2

4

7

9

52

77

442

116

221

247

161

160

68

1,415

30.4

57.7

49.3

47.8

48.2

32.7

29.8

'Housing units occupied by the same household in 1984 and 1987.

Source: Battles, Effects of the Adjustment of 1990 Census Data on the 1990 RECS Control Totals Obtained from the Current Population 

Survey (December 1991).

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 67
Cross-Sectional Comparisons

As a result of the multi-stage clustered sample design used in R!:f S, the sample of housing uir t: 

sometime contains two or more units from the same multiunii siruemre. When this occurs, their 

are some housing unit characteristics, such as the year in v/hi;:h i ;e structure was built anc th-, 

main space and hot water heaung fuels, that one would expect to be the same for every unit, r 

the structure. When differences are found for these characierisl.ii s '..n "inter-case compariscr : 

of different units in the same structure, they can be taken as net. ;;.:.t : ons of response error.

Blumstein, York, and Kemp (1981) used data from NIECS (the l ( )7;< REGS) to make such m.crcase comparisons. There were some difficulties in determining whici'. sample housing units caroc 

from the same structure, but by matching on structure char?nenstics and identifiers, '.''x: 

investigators succeeded in identifying 78 structures with more tl e.n one sample housing ure 

These structures included 30:': sarnole housing units for which :euy., ic\vs were completed. Wlv. r 

different responses were Icurd on items, such as year bitil: for housing units in the sarrrr 

structure, the most frequent response was assumed to be the cot-cut one. On this basis, and 

leaving out responses of "don't know" and those that were missing, nc apparent error rates were:

Number of Resmnses Percent

Other ''hnn Apparently

Item Total Most Free; eon Incorrect

Year Built 274 44 16

Main Heating Fuel 300 ! 5 5

Main Water Heating Fuel 300 24 8

The 16-percent gross error rale for year built is much lower th:m ;hc rate of 42 percent that rao 

be derived for the data on year built shown in Table 5.6. The- ciaci in Table 5.6 were based ec'i 

a longitudinal rather than a cross-sectional comparison and rnduu ;<r all types of housing urirr 

not just those in multiunit structures.

As described in earlier chapters, for housing units in multiuri: structures for which one or more 

fuels are included in the rent, the Rental Agent Survey provide-; dete for selected items that en 

be compared with data for the same items from the Household Sui vey. As part of regular dair 

processing operations, the two sets of data are compared. when, there are differences, the 

response considered more Hkejy to be correct is accepted. EKCOOI !x supplemental heating fueh: 

this is normally the response given by the rental agent.

Table 5.7 summarizes changes made on the basis of responses fro T: the Rental Agent Survey:: 

for survey years 1981 through 1987. These data suggest what errcr rates for these items mi?.1 :-' 

have been if the Rental Agem Surveys had not been conducted rrad the Household Surv-rr 

responses had been accepted. They also provide an indication o!' the I rvel of error for these items 

for housing units that were eligible for the Rental Agent Survey !:u for which no informadoo 

was obtained in that survey. The levels of nonresponse to the Rental f \f^ent Surveys were sho'.'.r 

in Chapter 4, Table 4.6.

Energy Information Administration / Energy Consurr ptioii Series 

68 Re.'jid-sr:t!al Energy Consumption Survey Quality Pr:<ni3
Table 5.7. Changes Resulting Comparison of Rental Agent and Household Survey 

Responses: 1980-1987

Item

Main Heating Fuel

Number of Changes 

Percent of Basea

Main Heating Equipment 

Number of Changes 

Percent of Base3

Supplemental Heating Fuel 

Number of Changes 

Percent of Base3

Water-Heating Fuel

Number of Changes 

Percent of Base8

Air-Conditioning Fuel 

Number of Changes 

Percent of Base3

All Items

Number of Units in Rental

Agent Survey 

Percent with >1 Changes

1980

<

1981

Survey Yea

1982

r

1984

31 

NA

NA 

NA

27 

NA

40 

NA

6 

NA

551

NA

58

15.8

52

14.1

18

4.9

82

21.1

1

16.7

466

30.0

31

12.2

40

15.7

5 

2.0

36

13.2

2 

4.5

308

26.0

68

13.3

103

19.4

549

32.4

62

9.2

206

30.7

29

4.3

120

14.8

61

39.6

356

41.8

"Base for the first 3 items in the number of units whose rental agents paid for the main heating fuel. For the fourth and fifth items, 

it is the number whose agents paid for the fuel in question. 

NA = Not Applicable. 

Source; Energy information Administration, Housing Characteristics (for the years shown).

With two striking exceptions, the proportions of eligible units (those included in the base for each 

item) whose Household Survey responses were changed based on Rental Agent Survey responses 

were relatively stable over the years shown. The proportions were lowest for supplemental 

heating fuel because the household respondent is usually considered to be more knowledgeable 

for that item. The exceptional cases were main heating equipment and air-conditioning fuel, for 

which the proportion of changes in the 1987 RECS was substantially greater than in any prior 

survey year. These changes may have resulted, at least in part, from changes in the questions 

relating to these two items on the Rental Agent Survey questionnaires. On the 1984 Rental Agent 

questionnaire there was a single item for main heating equipment, listing 13 possible alternatives. 

On the 1987 questionnaire, two separate lists of heating equipment were provided, one for units 

using electricity as the main healing fuel and one for units using any other fuel. For main central 

air-conditioning fuel, there was a minor change: the response categories on the 1984 

questionnaire were electricity, gas from underground pipes, and LPG. in that order, whereas on

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 69
the 1987 questionnaire the order was changed to gas from underground pipes, LPG.. ;.;;iu 

electricity. There wrere no changes for these items on the Household Survey questionnaire;.

For the most part, there has been little overlap between the daia hums collected in the Househci! 

and Supplier Surveys. For delivered fuels that are pa;c for directly by the househ:;'r 

consumption and cost data are collected from the suppliers. For uels whose costs are inch: :;:< 

in rent payments, consumption and cost are imputed on the basis .V" housing unit and house-he;.( 

characteristics; this is also done 'when a sample household is eligi *lc for the Supplier Survey '\; 

one or more fuels but a response cannot be obtained from the ;:':.ipplier(s). To evaluate tb:;:y. 

imputation procedures, some data on consumption for whole "".HI i Hi "rigs containing sample rev.: 1 

units were collected in the 1981 RECS. The findings from that study are described in the sec:'.i;:r 

on "Imputation" in Chapter 6.

Following the 1993 RECS.. responses to new Household Survey questions about availability u! 

and participation in demand-side management (DSM) programs .vere evaluated by compar.ng 

them with responses to similar questions that had been included hi t ic Supplier Survey. Seven;;.I 

kinds of DSM programs are offered by utilities to encourage custnr.iers to modify their patt:: ( ;; 

of energy use, the goals being to reduce overall demand or shift some uses away from peak ' o;;r.'. 

periods. The comparisons showed substantial Household Survey underreporting of the availabliii y 

of DSM programs. Of the households interviewed. 36.1 percent reported that at least one lypt: 

of DSM program was offered to them by their electric utility, na'.iral gas utility or some othun 

group. By contrast, 80.6 percent of the suppliers providing electricity to the same housebo us 

reported that they offered, some type of DSM program. The proportion of households actudK 

participating in electric or natural gas DSM programs was much smaller, but again there v.-;-.?. 

considerable disagreement between the response to the Household and Supplier Surveys. The ': 

were many differences in both 'directions, but the net result was t.'iiv participation appears to he; 1 ^ 

been overreported in the Household Survey (EIA 1995d, pp.. '52- [ 53).

Alternative Sources of Weather Data

The data record developed for each RECS sample housing u.i:it includes data on weal he: 

conditions in the vicinity of the housing unit. Of particular irr.por'ance are data on heating arx 

cooling degree-days, both for tie survey reference year and for a recent 30-year period (curn;:n 

and normal degree-days). Such data have several important uses:

 When supplier data are not obtained for a housing unit lor one or more fuels, the c.sif: 

on heating and cooling degree-days are important inputs in the models used to imonir 

consumption oi" those fuels.

 For all housing units, the data are used as inputs to !he models used to estimate e:K 

use consumption.

 In longitudinal analyses, variations in degree-days anc Departures from normal ?r 

important determinants of variations in consumption.

Energy Information Administration / Energy Consumption i Varies 

70 Residential Energy Consumption Survey Quality "'rofile
Ideally, degree-day data for each housing unit would be obtained by measurement of temperatures 

at the site of the housing unit. Because this is not practical, a reasonable alternative is to use data 

from the more than 4,000 individual weather stations maintained by the National Oceanic and 

Atmospheric Administration (NOAA). Two basic methods of using these data are possible: (1) 

For each housing unit, use data from the individual weather station that is closest, in some sense, 

to that unit, or (2) Use average data based on all stations in the NOAA weather division in 

which the housing unit is located. NOAA has divided each of the 48 contiguous States into 

divisions, usually consisting of groups of counties, that have similar weather conditions. As of 

1987, there were 345 NOAA divisions, an average of about seven per State.

A priori reasoning suggests that method (1), using the data from the closest individual weather 

station, would provide more accurate measures of degree-days. Temperatures can vary 

substantially within a multi-county area, especially when influenced by changes in elevation or 

proximity to large bodies of water. However, higher costs and some operating problems are 

associated with method (1). Selection of the "closest" station, taking into account distance and 

other relevant factors, requires manual processing operations which must be repeated for each 

survey as new PSU's or SSU's are introduced into the sample. Data are incomplete for some of 

the individual stations, so that imputation of missing data or substitution of another nearby station 

may be necessary.

Based on these considerations, EIA elected to use NOAA division data on degree-days for all 

survey years through 1984. Two evaluations of the effects of using alternative methods were 

undertaken prior to the 1987 REGS. In a 1982 study by the Energy Resources Group (Blumstein 

et a!.), one site was chosen in each of the 103 PSU's included in NIECS and its station data were 

compared with averages for the NOAA division in which the site was located. The sites were 

chosen to meet two requirements: high population density and presence of an individual weather 

station. The data used in this evaluation were 30-year averages.

This comparison showed a median absolute difference in degree-days of five percent between the 

data for the site averages and the NOAA division averages. Most of the large differences (in 

excess of 13 percent) were in California, where they averaged 30 percent. Reasons for these 

large differences included large divisions, with boundaries drawn to coincide with drainage basins 

rather than areas of homogeneous climate, and climatic patterns that vary substantially over short 

distances. The study investigators recommended that an alternative method be used to derive 

degree-day values for housing units in California and in other locations that showed large 

differences between individual station data and division averages.

The 1986 evaluation (Mooney and Carroll) was undertaken by the main survey contractor, 

Response Analysis Corporation. It was initially limited to the five States that had shown the 

largest differences between station and division data in the 1982 evaluation. Instead of selecting 

one individual station to represent a PSU, a separate selection was made for each SSU. The 

comparisons were based on data for the 1984 survey reference year, April 1984 through March 

1985. The investigators concluded that"... using individual station data on the SSU level, rather 

than NOAA divisional data, more accurately represents local temperature conditions." (Mooney 

and Carroll, p.27)

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 71
The evaluation also examined the effect of temperature data from alternative sources on ilu 

model-based imputation and end-use allocation procedures used ir R.HCS. Based on the dat.j b 

the five States included in ihc initial evaluation, the following conclusions were reached:

... end use models run with division data are biased i i several ways. First, 

because of the fact i:hst we overestimate degree days to ^ larger degree for low 

users of the fuels, the models overestimate the amount :)l fuel used for space 

heating. Second, because the degree to which we mis-cstinac degree days varies 

by household, consumption amounts at the househctc! level are mis-estimated. 

Finally, we underestimate consumption for "imputation'' hnuseholds because the 

division model allocates too little consumption to non-spaci.: heating uses. (Mooney 

and Carroll, p.40)

Subsequent to the five-Stale evaluation, all 1984 RRCS SHT'.s were assigned to indivij.i.vii 

weather stations rather than divisions and the new degree-day vakvs that resulted were assi^r.c J 

to individual households. The models used to impute consumption and to allocate it to end uses 

were rerun with the new degree-day values, and the results were compared with those derive.': 

by using the division averages lor degree-days (Response Ar.aly-is Corporation 1988). The-: 

comparisons showed that:

* As shown in Table f.K. there was a reduction ol' /* o-pcrcent in heating degree-u:.\ -' 

at the national level, with particularly large reduc'-.ons m the Mountain and Pacific 

divisions. Conversely, there was an increase of 12.1 percent in cooling degree-day^ 

at the U.S. level, with increases of 10 percent or mnrc it 7 of the 9 Census Divisions.

* The changes in overall consumption were relative!) small, because only househo'cs 

lacking supplier data are affected.

» The only fuel with a substantial change in consumption \\as fuel oil. for which use u/ 

the station data led to a reduction of 1.6 percent a". \hc I'.S. level.

* End use allocations shifted somewhat. At the national k' vl, the use of station data luc 

to a 1.6 percent decline in space heating consunmtior: v\hich was offset by a /.( 

percent increase in water heating consumption.

As a result of the above findings, RECS degree-day data for Uii: ! \?87 and subsequent 

have been based on records provided by NOAA for individual weather stations. The " 

weather station is identified for each SSU. mainly on the basis of Distance, but also taking into 

account differences in elevation, proximity to large bodies oT ^atu'. and the extent of miss:m.L 

data for the preferred station. L'sers should be aware that, bccausv u/this change, estimates n 

degree-days from the 1987 and subsequent survey years arc nrt directly comparable wi:'" 

estimates from earlier surveys.

Energy Information Administration / Energy Consumption Series 

72 Residential Energy Consumption Survey Quality Prc'ils
Table 5,8. Comparison of Heating Degree-Days Using NOAA Division Method Versus Station 

Method, April 1984 Through March 1985

Census 

Division

Million 

Households

Heating Degree-Days

Division 

Method

Station 

Method

Percent 

Difference

Cooling Degree-Days

Division 

Method

Station 

Method

Percent 

Difference

United States 86.328 4,686 4,518 -3.6 1,153 1,293

New England

Middle Atlantic

East North Central

West North Central

South Atlantic

East South Central

West South Central

Mountain

Pacific

4.269

14.029

15,203

6.414

14.777

5.784

8.764

4.512

12.577

6,398

5,663

6,524

6,619

2,951

3.651

2,443

5,728

3,508

6,331

5,460

6,427

6,499

2,979

3,512

2,444

5,158

3,019

-1.0

-3.6

-1 5

-1.8

0.9

-3.8

0.1

-10.0

-13.9

524

683

685

976

1,768

1,433

2,361

1,102

873

621

822

777

1,076

1,819

1,583

2,431

1,550

1,148

12.1

18.4

20.3

13.4

10.2

2.8 

10.5

2.9

40.6

31.5

Source: Energy Information Administration, Consumption and Expenditures (May 1987).

Questionnaire and Interviewer Effects on Measurement E

Since the inception of RECS, there have been continuing efforts to reduce measurement error by 

making improvements in questionnaires and in the training and supervision of interviewers. For 

each survey year, the Household Survey questionnaire and other survey instruments have been 

pretested and subjected to reviews by EIA and contractor staff and other persons with expertise 

in questionnaire design. As described in Section 5.1, there has been some use of in-depth 

interviews in attempts to explain unusual consumption patterns. This section cites some 

additional examples of relevant activities.

Pretests and Questionnaire Reviews

In preparation for the 1990 RECS. the draft Household Survey questionnaire was pretested by 

three interviewers, one of them an experienced RECS interviewer, in nine households. Each of 

the interviewers completed a detailed evaluation form, with comments on each section of the 

questionnaire, and participated in a debriefing session. In addition to suggestions for clarifying 

specific questions, some of the points raised in the overall report on the pretest (Miksovic 1989) 

were (some, but not all, of these suggestions were adopted in the final version of the 

questionnaire):

* The questionnaire includes some rather abrupt switches from one topic to another. 

Transition sentences should be provided at these points.

9 The interviewers felt that many of the questions were very wordy, especially some that 

included the phrase "... other apartments, condos, households, businesses, or farm 

buildings." It was recommended that the phrase "... and farm buildings" be put in 

parentheses, to be used by interviewers only when it seems appropriate.

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 73
* The format of the questionnaire made it difficult for interviewers to refer to the re 

instructions while they were outside making and recording measurements of bui . dimensions. Several possible improvements were sup:ge ,ted.

* Improvements in format were suggested, such as us:: ol larger print, highlighting ^:k:i: 

instructions in various ways, and using different prim ty ;cs.

A user-needs study prior to the 1993 RECS (EIA 1993c) identified a widespread interest ir .h: 

collection of additional information on lighting, to track use of new lighting equipr:?-:: ! 

technology and to allow estimation of a measure of energy :nd-use intensity for lighli:\; . Collection of accurate information on lighting facilities and use in the home poses so.n,: 

challenging cognitive problems and could require substantial additional time in the inter/cv 

In 1992, contractor staff conducted a series of in-depth interview;;, which were recorded on kn: 

audio and video tape, to explore various means of asking for Ib: desired data. For the firsi: s<; 

of interviews, seven members oTlhe contractor staff served as respondents. Each was intervLv ,? 

twice, with a week intervening. For the second set of interview;. 5 non-staff respondents so' 

recruited. Two of them were s husband and wife who were interviewed separately and '.!:: 

together, to attempt to reconcile differences in their reports. All respondents were asked to r rj< r! 

on use for the day preceding the interview. They were asked ui report on all lights (fixture 

controlled by one switch, as opposed to individual bulbs) thai had been used for at leas: i 

minutes and to report how long each one had been turned on (Daniels 1992).

There were fairly substantial week-to-week percentage differences in reported use for IK 

respondents who were interviewed twice. Some of these diffc:-cnc:.s could have been real; semi 

could have been caused by response variability. Nevertheless, [he relative stability of ranku,;:: 

of the seven respondents in terms of total hours of use suggested that the data could have be:.:; 

used with fair reliability to classify households as high, medium, and low lighting users. T!y 

couple who were interviewee separately and then together had sgnificant differences, wkr:! 

could not be completely reconciled in the joint interview. In general, most respondents fou:i<: 

it difficult to estimate tin: time of use for each light, and it was noi. obvious that allowing 1r.r:~ 

to report time of use in broad categories made it any easier Inan asking for an answer in hccr: 

or fractions thereof. The room-by-room inventory approach used ir: the pretest was estimated : 

require an average of at leasl eight minutes per respondent, even 'vithout additional probing 'ha. 

might be necessary to obtair. reasonably accurate responses.

On the basis of this test and other considerations, two sets of questions on lighting were include, 

in the 1993 Household Survey questionnaire. A short module, :tsk?d for all households, requests: 

them to report the total number oT lights and the number of riuor:scent lights used on a typirn 

November weekday for: more than 12 hours per day. between 4 and 12 hours per day, ;mc 

between 1 and 4 hours per day. A supplementary set of curs.ions was asked only for ;. 

subsample of 474 households. It called for a more detailed accounting covering each indoor ik,hi 

used in the home, on a ivpicai November weekday, for at least i .i minutes. Respondents we:n; 

given options on whether lo report lights by room, activity, or time-of-day usage and w?; c 

allowed to report the time used for each light in actual number of hours or in class i 

based on number of hours.

Energy Mfarmatlon Administration /Energy ConsLmptior Series 

Residential Energy Consumption Survey Quwiity
A comparative evaluation of the quality of lighting data obtained from these t\vo modules will 

be undertaken. For the subsample of households that responded to the supplemental module, it 

will be possible to analyze any differences in their responses to the two modules.

In 1992, at the request of a member of the American Statistical Association's Hncrgy Statistics 

Committee, a survey researcher reviewed the cognitive features of the 1990 R.ECS Household 

Survey questionnaire, with special emphasis on questions requiring respondent recall (Biemer 

1992). A major finding of this review was that many of the questions in mis category did not 

include a reference period as a basis for the response or used a vague, unbounded or ill-defined 

reference period. In response to this analysis, all recall questions planned for use in the 1993 

REGS were carefully reviewed and several changes were made. For example, the 1990 question:

H-10 About how many deliveries [of LPG] does your household usually gel in a year? 

was changed for 1993 to:

J-14 About how many deliveries did your household get in the past 12 months?

Other Questionnaire Effects: Conservation Improvements

In the 1984 RECS, the Household Survey questionnaire included several questions about 

conservation improvements that had been made to the housing unit since September 1, 1982, such 

as storm doors and windows, additional insulation, caulking, weather stripping, and heating 

system improvements. For all such improvements, respondents were asked to report the month 

and year in which they were installed, which could have been any month between September 

1982 and the month of the Household Survey interview, generally in the Fall of 1984.

Following these questions there was a general question asking whether any improvements of this 

kind had been added or installed and paid for during calendar year 1983. This question was 

designed to identify households eligible for the energy tax credit that was permitted on their 

Federal income tax returns for that year. Households answering "yes" to this question were asked 

whether or not they had actually taken the energy tax credit on their returns.

A comparison of the general question about improvements in 1983 with responses to the earlier 

questions about specific improvements that were eligible for the tax credit showed that, of the 

1,328 households that answered "yes to the general question. 567 (42.7 percent) had not reported 

any specific improvements as being added or installed in 1983. This could have been legitimate 

in some cases; the specific questions allowed for reporting of only a single month and year. If 

caulking or weather stripping, for example, had been added at two different times during the 

approximately 2-year reference period, only one of these would have been reported. Also, some 

improvements might have been installed in 1983 but not paid for until 1984. Nevertheless, the 

high incidence of apparent inconsistency suggests that responses to the questions about specific 

improvements or the general question, or perhaps both, were subject to substantial bias or 

response variability. The general question on improvements in 1983 was complex, with several

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 75
subquestions imbedded in it, and interviewers reported difficulties ir administering it (ElA 19fl 

pp.112-113).

Interviewer Training

Typically, about 300 interviewers have conducted persona! interviews for each of the Rl'. 

Household Surveys. P;:rt!v because of the longer interval beV'Veea surveys in recent ye; 

interviewer turnover between surveys has increased. In the I 1 '87 RECS, 57 percent or" 

interviews were conducted by interviewers who had not parti dp? ted in the 1984 survey. In 

1990 RECS. 60 percent of the interviews were done by interviev-ers with no experience in 

1987 RECS. This turnover, along with numerous changes in aues'ionnaire content, means ir.s 

the effectiveness of training and supervision of interviewers can be an important elemenl 

determining the quality of the survey results.

For the 1980 RECS, interviewers were trained for two days i:i srrwll group sessions for 10 t:: ":( 

persons each. For the 1981. 1982. and 1984 RECS, new interviewers were trained in the ?.iv.<.'.:. 

way as in 1980. but the training of experienced interviewer::, consisted of completion of selfstudy materials and practice interviews. For the 1987 RECS, a I ir.ierviewers were trained in fe n 

large group sessions, each lasting two and one-half days.

The cost of training was becoming a substantial element in the total budget for RECS. and mean; 

were sought to reduce training costs for the 1990 RECS. The solution adopted was to use hern:: 

study materials for all interviewers, including a videotaped presentation in several sections, m 

interviewer's manual, a quiz arid practice interviews, the last twc of these to be sent in to tn;; 

survey contractor for evaluation. Use of these training methods resulted in a significant reduction 

in training costs, estimated at about 30 percent in constant collars. Part of the savings v/e;x: 

applied to more extensive office reviews of practice and initial interviews, followed by c-on.te.'. .;; 

with all interviewers to provide feedback from these reviews i'Lei-.ch 1991).

Given the rather substantial change in training procedures tlui w. introduced in 1990. it v••-•.: 

considered important to try to evaluate the relative effectiveness CH' .he old and new procedure,. 

Two methods were used: administration of an evaluation question! aire to the interviewers and 

comparative analyses of the extent of edit changes and impulatio: m the data processing.

Completed evaluation questionnaires were obtained from 257 cr about 290 interviewers wi.:: 

completed the training for the " ! 990 RECS. Most of those responding had had prior exposure '::• 

both large and small-group training sessions for RECS or other surveys. When asked to com pa '.; 

the effectiveness of and their preferences for four kinds of training -small group in-persori, Jan : :e 

group in-person, self-study only, and self-study plus video a large \: roportion, 78 percent, thong'-i. 

small group in-person training was the most effective and 60 percent identified it as their f-r?it 

preference. Self-study plus video was a distant second for both effectiveness (15 percent) ar M 

preference (22 percent). When first and second ratings were pooled, self-study plus vioer; 

received favorable ratings for both effectiveness and preference from 55 percent of "t' f ; 

interviewers.

Energy Information Administration /Energy Consumption Series 

76 Residential Energy Consumption Survey Qua lily Pi-ofMB
The interviewers' overwhelming preference for in-person training may be influenced in part by 

factors other than its effectiveness. For most interviewers, these training sessions provide an 

opportunity to travel away from their home base and to meet fellow interviewers with whom they 

would otherwise not have much contact.

Interviewers were asked how well they felt they understood each of 11 topics covered in the 

video presentation. Most were rated "well understood" by at least 80 percent of the interviewers. 

Exceptions included:

s Fuels and equipment (61 percent understood very well). One written comment suggested 

that the treatment of combination equipment was inadequate.

 Measurement of total and heated floorspace. This has always been one of the most 

difficult aspects of the survey for interviewers. Some of the written comments praised 

this section of the videotape, indicating it was more realistic than what could have been 

done in a classroom training session.

 Recording of air-conditioner nameplate data. This was a new requirement for the 1990 

RECS, so it was difficult to anticipate what kinds of problems might arise and discuss 

them in the training.

In response to a question about the degree of difficulty of the training exercises and final quiz, 

nearly all of the interviewers considered them "about right." However, many interviewers did 

quite badly on the exercises and quiz, especially on topics such as what to measure and what not 

to measure, who are eligible respondents, and households versus group quarters. These same 

topics had caused many problems in training and in actual field work in prior survey years.

In addition to finding out how interviewers reacted to the new training procedures and what 

improvements they had to suggest, it was felt important to seek an objective measure of the 

effects of the new procedures on actual interviewer performance. The method of analysis adopted 

was to compare the levels of edit and imputation changes for 14 RECS variables for the 1987 and 

1990 RECS in total and for experienced and inexperienced interviewers in each survey year. An 

experienced interviewer was defined as one who had participated in RECS in the immediately 

preceding survey year. Inclusion of interviewer identifiers on the RECS file of individual 

household data for each survey year made it possible to distinguish the work of experienced and 

inexperienced interviewers. The 14 variables were chosen from among those that were included 

in the same form in both survey years, had extensive edit checks, and had required the most 

imputation (Response Analysis Corporation 1991).

The indicator of interviewer performance used in the analysis, admittedly an indirect measure, 

was the proportion of sample households for which changes had been made in each of the 14 

variables following data entry, as the result of editing and imputation procedures. Changes were 

detected by comparing initial and final values for each variable; intermediate changes were not 

taken into account. Interviewer errors were only one source of such changes; the}' could also 

result from respondent and data entry errors. Moreover, some interviewer errors were detected 

and corrected in manual reviews prior to data entry.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 77
Table 5.9 shows, for each of the 14 variables selected, the oercent of sample households \vi.! 

changes for each survey year for experienced and inexperienced interviewers. Overall, the c'ai. 

do not reveal a substantial difference between the change ind caiors for the 1987 and "9 

surveys. For a few variables there were fairly large differences in the proportion of chant/.t' 

between the two years: same heating fuel as prior survey (down 5 percent in 1990), year hous 

built (down 6 percent), and presence of a basement (up 4 percent). Hxcept for the possibility c 

insufficient attention in the 1990 training to the item on basements, more detailed analvse? -ii : 

not reveal any obvious reasons for these changes.

As shown in Table 5.9. i:he mean proportion of changes for the 1-! variables was slightly h^ne 

for inexperienced interviewers in both survey years. This fino.ing does not necessarily prove (:hc! 

their performance was not as good as that of experienced interviewers. An alternative explana::u. ; 

would be that interviewer turnover is larger in areas like central cities, where there is likely tr 

be a higher incidence of respondent error and item nonresponse requiring imputation. Table :: i ! 

provides data relevant to this hypothesis. As shown in the .ast column of that table, there ! , clear association between hoc sing characteristics and the extent y' changes subsequent to 

entry. Households in center cities, those living in apartment nui dings, and those who wen. 

owners had the largest number of changes. These explanatory va*-ables are correlated, and som; 

of the differences for renters of apartments may be explained hy changes made to varia:)le: 

related to heating fuel and equipment based on responses to Uie Rental Agent Survey.

However, the data in Table 5.10 on percent of interviews completed by experienced interviewers 

by housing type, provide only moderate support to the supposition that there are highe: 

proportions of inexperienced interviewers conducting interviews \\ ith the types of households f:)i 

which changes subsequent to data entry are most frequent. T"hc proportions of experience: 

interviewers are nearly the same for owner and non-owner occur: cd housing units. They we:'': 

slightly lower for apartments than for single-family units ard It wcr for households living 'r 

metropolitan areas.

Taken overall, the results of the interviewer questionnaire and the analysis of processing c 

did not demonstrate any clear or substantial differences in effect veness between the 1987 a:xl 

1990 RECS training. However, two features of the 1993 RECS sesmed to favor the use of in 

person over self-study training for that survey: first, the inclusion of several new items in tnr. 

Household Survey questionnaire and. second, as a result of th? ] v93 sample revision, 22 oux of 

116 primary sampling units had not previously been included ir 1T1CS, so the proportion of 

interviewers lacking previous RECS experience was likely to be higher than usual. Consequent), 

two and one-half days in-person training sessions were held rbr all interviewers at four si:es 

followed by a small make-up session and some telephone training for replacement interviews s.

Energy 'nfcn'matlon Administration /Energy Consumption Series 

78 Residential Energy Consumption Survey Quality Pi:ifile
Table 5.9. Percent of Household Survey Interviews with Imputation or Edit Changes Subsequent 

to Data Entry, by Interviewer Experience3 ; 1987 vs. 1990

Total RECS19SO . ...-.,......- ............ . 

Question 1987 1990

B-2 Main Heating Fuel 3 2

B-9 Main Heating Equipment 6 5

C-3 Water Heating Fuel 5 5

K-10 Income 11 14

B-3 Same Heating Fuel as 1987b 10 5

1-1 Budget Plan for Main Fuel 2 4

A-6 Year House Built 15 9

P-11 Has Basement 1 5

L-12 Public Housing 1 3

K-7 Race * 2

K-1 Non-Householder Age 1 1

P-1 2 Amount of Basement Heated 3 5

K-1 Householder Age 1 1

K-6 Marital Status 1 2

Mean: 14 Items 4.3 4.5

Number of Interviews 5,856 4,828

1987 REGS 1990 REGS

Exp. Not Exp. Exp. Not Exp.

3 32 2

5 84 5

5 44 5

10 13 16 13

9 10 6 5

2 23 4

14 15 8 9

1 13 6

1 14 3

2 2

1 11 1

3 35 5

1 11 2

1 11 2

4.0 4.4 4.3 4.6

2,530 3,326 1,965 2,873

* = Rounds to less than 0.5 percent.

Interviewers were counted as experienced if they had worked on the immediately preceding RECS 

bThe 1987 question was "Same Heating Fuel as 1984." 

Note: Exp. = Experienced Not Exp. = Not Experienced

Source: Response Analysis Corporation, Quality Assessment of Videotape Training: Conclusions and Recommendations 

(September 1991).

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 79
Table 5.10, Interviewer E-jcperience and Extent of Edit arm 

Household1 : ': ?W REGS

I IT nutation Changes, by Tyj:

Household 

Category

Interviews

r Percent b1/ 

Experienced 

Interviewer 1 -;;'

Mean Proportion 

of Edit and

Imputation 

Changes: 

14 Variables'1

All households

Metropolitan Status 

Center City 

Other Metropolitan 

Nonmetropolitan

Housing Type 

Single-Family 

9 Detached or 

Mobile Home 

° Attached 

Apartment

Home Ownership 

Owner 

Other

4,828

1543

1,994

1,291

3,346

289

1.193

3,201

1627

4.5

5.8 

4.4 

3.8

3.-' 

7.6

"Interviewers were countec: as e;:pe-:enc&c if they had worked on the imrneciaisly p':>cedlng RECS.

D IViean, for 14 selected variables. of;he orooortion of households for which the value war, ::;l'gnged by edit or imputation subsec 

to data entry

Source: Response Analys ;; Corporal on, Quality Assessment of Videotape; "Vsi/i <ift Conclusions and Recommence : 

(September 1991).

80

Energy Information Administration / Energy Cons;i..rnptinr Series 

Residential Energy Consumption Survey Quality P-ofil'e
6. Data

This and the next chapter of the Quality Profile cover the processing operations that follow the 

collection of RECS data from households, rental agents, and energy suppliers. This chapter is 

about the quality aspects of initial manual reviews of questionnaires, coding, data entry, 

computer-assisted edits, and imputation. Chapter 7 covers the weighting procedures used to 

develop sample estimates, the model-based procedures for allocating consumption of each fuel 

to specific end uses, and the estimation of sampling errors.

The primary outputs of the processing operations discussed in this chapter arc the data files 

delivered by the survey contractor to EIA. These data files are of three kinds:

1. Household Files, which contain all information collected for every intervievved 

household

2. The HURS (Household Unit Record Sheet) File, which contains inlonnaljon on type; 

of living quarters and occupancy, sampling information, and data collection outcomes 

for all sample housing units, including those that did not respond to the survey

3. Utility Bill Data Files, which contain individual household billing data, annualized 

consumption and expenditures data, and associated weather data for each of the five 

major fuels.

Unlike the Household and Utility Bill Files, the HURS File docs not include tiic weights thai 

would be needed to produce estimated totals and weighted unit response rates.

Prior to delivering these files to EIA, the survey contractor removes specific identifiers and 

related information that would make it possible for EIA to identify individual respondents. For 

the same reason, individual bill data are inoculated with random errors.

An overview of the processing operations covered in this chapter was provided in the first section 

of Chapter 2, under "Data Processing and Imputation." As noted there, the procedures arc 

intricate and detailed, consisting of a large number of distinct processing steps. Initially, data 

from each of the six major sources Household Survey interview/ questionnaires. Household 

Survey telephone and mail questionnaires, Housing Unit Record Sheets, Rental Agent Survey 

questionnaires. Supplier Survey questionnaires and billing records, and weather data are 

processed independently to make them computer-readable, eliminate as many errors as possible 

and impute values for some of the items that are missing or incorrect (other kinds of imputation 

require matching with records from other sources to provide donor information). Then data from 

the six sources are compared and combined in various ways to produce the three major output 

files.

This chapter describes the general structure of the data processing operations, with emphasis on 

features that affect the quality of the final output. Quantitative data are presented when available. 

Most of the material focuses on the procedures used in the 199(3 and 1993 RHC'S: however, some 

relevant data from earlier surveys are presented and significant procedural changes over the

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 81
history of RECS are discussed. Readers who want a step-by-step detailed description ..;:: " 

processing activities should consult the Data Editing and Manu/a'alion Procedures Manual••• 

the 1990 and 1993 RECS (Response Analysis Corporation 19 ( >2b. 1995c).

Several steps in data processing, especially the manual and computer edits, are designed to det 

and, insofar as possible, to ::educe or eliminate errors. The purpose of imputation is to rer;;; i 

missing entries or those believed to be in error with values that ;:re closer, at least on the aver;:;].. 

to the correct ones. However, errors can also be introduced, at ea;::h stage of data processing. ! 

overall processing system is designed to optimize the quality of the final product. The under vi 

philosophy guiding this effort has been that because the RECS sample of households is relativ; 

small, the use of substantial manual and computer resources to nake the final files as "cle:;.r r 

possible is justified.

Data Processing Other than imputation

In order to establish a context for discussing the quality aspect:; of data processing, this so: 

begins with a general overview of the nature and flow of i! e main processing steps. 

overview is followed by a review of the quality-related lea'ures of manual operation:; 

computer-assisted operations. Finally, some results of a special analysis of changes resulting " 

computer-assisted data processing operations in the 1984 RHCS are presented, along witr. ; . 

summary data for these kinds of changes in the 1987 and 1990 RECS.

Structure of the Processing Operations

Figures 6.1 and 6.2 show the main processing steps, including imputation, for the RECS 

sources other than the weather data obtained from the National Oceanic and Atmosj: 

Administration (NOAA). A.s noted above, questionnaires and other inputs from each of these : i 

sources are first processed independently of each other. Taesi; processing steps are shown : 

each data source in Figure 6.1. For the sake of compk'ceress. imputation, which vA". 

discussed in the following subsection of this chapter, is indudrd in the figure. The fir:;'; ru 

steps receipt and check-in, manual coding and editing, and data entry are primarily clerii 

operations. The edits listed under Step 4 are computer-assisted. Computer-generated listinp. = 

suspect variable values and associated information are reviewed manually by editors and. ',; h 

necessary, by supervisors or specialists, to determine whether and how to change the sii;;oi 

values: In some instances, respondents are recontacted by telephone.

Steps 5 and 6 are computer procedures that apply only to the Household Survey data. Como .it 

generated updates are used for Household Survey interview i. uestionnaires to correct cert; 

common interviewer or respondent errors without manuai examination of the questionnai 

These updates are used on'iy in a few instances where a preliminary review has shown rha 

particular kind of correction is virtually certain to be appropria'?. for all households that ! :-?.vi 

specified combination of entries.

Energy Information Administration /Energy Ccnsurnplion Series 

82 Residential Energy Consumption Survey Sualify Profile
Figure 6.1. Steps in Internal Processing of Data from Each Major Source3

Data Source

Processing 

Step

1. Receipt, check-in

2. Manual editing and coding

3. Data entry

4. Computer-assisted edits

a. Range checks

b. Internal consistency checks

c. Special reports

5. Computer updates

6. Same-source imputations

Household Survey

Interview

X

X

X

X

X

X

X

X

Phone, Mail

X

X

X

X

X

X

HURSb

X

X

X

X

X

Rental 

Agent 

Survey

X

X

V

A

\/ 

A

%/ 

/\

Supplier 

Survey

X

V 

A

X

X

X

"Processing of weather data, which follows a different pattern, is excluded from this exhibit.

"Housing Unit Record Sheet.

Source: Response Analysis Corporation 7990 RECS Data Editing & Manipulation Procedures Manual (September 1992).

Same-source imputations for the Household Survey questionnaires (step 6) are those which do 

not require inputs from other sources. They may be based on the values of other variables for 

the same household or on data for other sample households.

The initial processing of weather data, not shown in Figure 6.1, consists of the extraction and 

manipulation of temperature data from data tapes obtained from NOAA. The first step is to 

associate a weather station with each secondary sampling unit (SSU, see Chapter 2. Figure 2.3). 

taking into account physical proximity and the completeness of data available for the stations. 

For each weather station associated with one or more SSU's, temperature data are extracted from 

the NOAA tapes, missing data are imputed, and long-term and reference year values of heating 

and cooling degree-days are developed.

Figure 6.2 lists the principal processing operations that require comparing or merging data from 

more than one source:

A. The annualization of Supplier Survey bill data for individual housing units starts with 

data reported by suppliers for billing periods, most commonly months, and uses these 

data to develop estimates of total consumption and expenditures for each fuel for the 

12-month survey reference period. As part of this process, which is described later 

in this chapter, the degree-day information developed from the NOAA tapes is used 

to adjust data for billing periods that overlap the start or end of the calendar-year 

reference period and to impute data for parts of the year for which no billing data are 

available.

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 83
Figure 6.2. Processing Operations Involving More than One Daia Source

A. Annualizing bill ng data

B. Comparison of Household and Rental Agent Survey responses

C. Imputation of missinc data for telephone and mail houssho; d:;:;

D. Model-based outlier checks of Supplier Survey data

E. Creation of output files

Source: Response Analysis Corporation 1990 RECS Data Editing & Manipulation Procedures Manual (September

B. As discussed in previous chapters, Rental Agent Survey responses for such variables 

main heating and cooling equipment and fuels are usually considered to be more rel/.a 

than those of the occupants of rental units in multiunit tnictures and are substitutethe latter when disagreements exist.

C. The imputation of data, items not included on the telephone and mail versions n 

Household Survey questionnaire requires matching the avo sets of sample household 

those that responded by interview and those that responded by telephone or mzi'<••••• 

variables that are common to all questionnaire versions TI find interview household t; 

are suitable to serve as "donors" for imputation.

D. The model-based outlier checks of estimates of annual consumption, which were disci, v 

in Chapter 5, require merging of Household and Supplier Survey data.

E. The ultimate goal of all processing steps up to this point is the creation of the output '"il 

especially the Household Files, which, in their final :"orm, include data from si 

sources.

Manual Operations; Quality Considerations

Questionnaires from each of the three surveys, following receipt and control operations ; 

subjected to an extensive manual combined editing and coding operation. For each ty;::;" 

questionnaire, the first st;:t) is to verify the accuracy of the basic clentification information. ' ! 'r 

the editors check each questionnaire item for completeness and k.gical consistency with reso;:.n; 

to closely related items, in preparation for data entry, they ;:n:er codes next to card ;:o T 

numbers on the questionnaire.

The extensive and detailed nature of the editing/coding operations may be seen by examining i 

detailed instructions for processing interview questionnaires from the 1993 Household 8i:r' 

(EIA 1995b). In addition :o the basic tasks of consistency checking and coding, edito.c: ' 

instructed to:

Energy Information Administration / Energy Consumption Series 

84 Residential Energy Consumption Survey Cualily Profile
 Convert numerical entries for such items as amount of wood burned to the desired 

dimensions, rounding when necessary.

8 Keep lists of write-in responses to "other" categories for several questions which have 

this response option. These lists are used later in processing to recede some of the 

"other" responses to other existing or newly created categories.

9 Refer unusual types of entries and other problems to a special coding and editing 

section or to designated technicians.

Editing and coding of the housing unit measurement section of the Household Survey 

questionnaire require certain particularly complex tasks, such as dealing with measurements for 

floors having shapes that are not simple rectangles.

There is no formal verification system for the manual editing and coding operations. The 

subsequent computer-assisted edits provide an opportunity to detect some of the errors that may 

have been overlooked or introduced by the editors. One example of findings from such checks 

is provided in the next subsection.

Data keying is performed by a separate EIA contractor. Batch tapes of keyed data are transmitted 

to EIA and loaded to its main computer, where they are used by Response Analysis Corporation, 

the main contractor, to create unedited data files for each source and perform the computer edits. 

For the 1981, 1982. and 1984 surveys, key Household Survey questionnaire items were 

100-percent verified and the remaining items verified for a 25-percent sample of households. 

However, a review of the changes that had been made during processing operations for the 1984 

Household and Supplier Surveys showed that keying errors were leading to substantial numbers 

of computer edit rejects (Jabine 1987). The costs of processing these rejects were deemed to 

exceed the savings from sample verification of data entry and there was also no guarantee that 

the computer edits and special reports would detect all keying errors. Consequently, beginning 

with the 1987 RECS, all keying has been 100-percent verified.

Computer-Assisted Edits

Figure 6.3 shows the different kinds of computer-assisted edit checks used in RECS. Range 

checks are applied to values for individual variables from all of the survey components of RECS. 

Simple range checks ensure that no illegal or impossible variable values are included in the final 

records. Outlier checks identify, for clerical review, values for continuous variables that may be 

correct but are unusually high or low for that variable.

Internal consistency checks are also applied to data from all of the RECS survey components. 

Most commonly, these checks examine relationships of responses for different variables for the 

same household. Some checks involve comparisons of data for the same household from the 

current and prior rounds of RECS. For example, housing unit area measurements may be 

compared for the current and immediately preceding round. Such checks can be used only in 

those survey years for which the sample includes a longitudinal component and only for the 

housing units that were in the sample both times.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 85
Figure 6.3. Computer-Assisted Edits Used in RECS

A. Range checks

1. Simple rarge checks

2. Outlier check:;

B. Consistency checks

1. Internal to source

a. Same un: t, same survey 

b. Same unit, onor survey 

c. Different mite, same survey

2. Across sources

C. Special reports

Source: Response Analysis Co'por.-jtio.-i, 1990 RECS Data Editing & Manipulation ^rocsdures Manual (September ;!);;/'.

Another kind of internal consistency check compares data foi dire rent households from the eev 

survey component. F'or example, during the processing of-:he Supplier Survey records. -, e ! e 

for each household are compared with those for some of the other sample households. Scree 

these comparisons are with all households using the same fuel arcl others are with all hoiisebril 

obtaining that fuel from the same company. In these between household checks, large differem: 

are treated as outliers arid are reviewed to determine whether changes are necessary.

The primary consistency checks across survey components are iems B and D shown in eie:e 

6.2. Item B, Comparison of Household and Rental Agent Survey responses, is the final ;;rr-; 

processing data collected in the Rental Agent Survey. For the 1 990 RECS, Memo #951 pro'* d 

instructions for resolving iiie differences appearing on computei-generated listings. In ac'ce ti 

to reviewing the Household and Rental Agent questionnaires ler units with differences, ed i.r 

are instructed to review other sources of information. :ir:c:i.H.ha Supplier Survey date. ' 

questionnaires for neighboring households in the same building

Item D, Model-Based outlier checks of Supplier Survey dai;e requires the use of Household e 

Supplier Survey data for the same units. A nonlinear model, wi'h parameter values based ;: ;. J . 

previous survey, uses data on housing and household charae I envies from the Household Sv-'v 

to predict fuel consumption for each unit. The predicted values are compared with annual:;?:- 

estimates of consumption oa;;;ed on bill data obtained in the 

whenever the model-basec. estimate was more than three 

annualized consumption estimate from the bill records, 

checked.

upplier Survey. In the 1990 Ef, 

imes or less than one-third :T 

e d.it.2 .from the two sources '

For the Household Survey records, in addition to the range; ane consistency checks, a sere 

computer-generated .special reports lists information for house-he Ids with unusual responsecombinations of responses on several different topics. Figure 6,4 !:sts the topics for which ;.]x 

reports were prepared in the 1990 RECS. Each of these repots Identifies the househok-e 

qualify for inclusion ami gives their values for variables that :ire relevant to the topic -jj 

report.

86

bnnrgy information Administration /Energy Corsdn'ation Series 

."f&ijidential Energy Consumption Sun/ay (,','ualiir F'rofile
Figure 6.4. 1990 RECS; List of Topics for Special Reports

1. Households with Wood-burning Inconsistencies 14. Swamp Coolers

2. Households Not Using a Heating Fuel 15. Households Using Gas Air-Conditioners

3. Households Reporting That Their Main Heating Fuel 16, Air-Conditioner Section Skip Patterns 

Changed in November 1987 or Later

17 Unusable Measurements

4. Missing Supplemental Fuels and Equipment

18. Households with Unknown Heating

5. incompatible Fuel/Equipment Combinations Equipment and Heating Fuel

6. Family Members'Ages and Relationships 19. Households That Selected Underground

Gas as an Alternative Heating Fuei

7. Central Fuel Inconsistencies

20. Households with Maritai inconsistencies

8. Inconsistencies in the Number of Rooms Cooled

21. Basement Insulation vs. Basement Heating

9. Inconsistencies with the Family Grid

22. Receded Variables

10. Inconsistencies with the Foldout Page

23. Changes Made to the Mail Questionnaire

11. Households That Use Heat Pumps Donor Selection Variables

12. Lighting 24. Various Miscellaneous Checks

13. Households with No Windows, Doors, or Electricity 25. Households That Used the Answer "Other" 

Source: Response Analysis Corporation. 1990 RECS Data Ed/ting & Manipulation Procedures Manual (September 1992).

Processing Computer Edit Outputs

A more or less standard approach, shown in Figure 6.5, was used to resolve rejects and 

questionable values included in the computer-generated outputs of range checks, consistency 

checks, and special reports. In some instances, editors can decide whether to accept or change 

a response on the basis of a review of the information in the computer listing and the original 

questionnaire. Failing this, decisions require examination of other relevant questionnaires. At 

this stage, an editor may be able to determine, for example, that a coding or keying error led to 

the inclusion of an incorrect value in the computer record. As a final resort, in a small 

proportion of cases, editors may attempt to contact a household, rental agent or supplier. In order 

to maintain an audit trail, whenever an editor decides to change a value in the record, information 

about the nature of the change and the basis for making it is recorded in an archive file.

For the 1990 RECS, instructions for processing each of the special reports are contained in a 

series of RECS-90 Memos. Most of the memoranda consist of general rules for making changes 

or for referring certain types of problems to a supervisor. In a few instances, however, the 

memoranda include quantitative information about the number of households included in a report 

and the manner in which apparent inconsistencies and other possible errors were dealt with.

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 87
Figure 6.5. Processing Computer Edit Outputs

Accept

Accept

ccept

Review edit 

listing

Review 

questionnaire

Contact respondent, 

rental agent, or utility

Miange

Assign new value

and reason code.

Update file.

Source: Response Analysis Corporation, 1992b.

88

Energy Information Administration / Energy C;n-isun-;ptiori Series 

Residential Energy Consumption Survey Qualit; Profile
A special report was run to examine consistency and skip checks for the "family information 

grid" (questions K-l and K-3). This report generated lists of households with 14 different kinds 

of potential errors for example, households reporting more than one spouse or households with 

inconsistencies between the number of persons listed in the chart and the variable for the number 

of persons in the household. RECS-90 Memo #602 provides information about the disposition 

of the 292 households that appeared on the 14 listings. Changes were made for 227 (78 percent) 

of these households, in most instances because the editor determined that there had been coding 

or keying errors. The largest number of changes, 127, were made in cases where the family grid 

showed the householder to be married but a separate question on marital status (K-6) indicated 

otherwise. Most of the discrepancies were the result of coding errors: a person identified as 

partner or fiancee in the family grid was incorrectly coded as spouse. The relevant questionnaire 

items were modified in the 1993 RECS in an effort to reduce the frequency of this kind of error.

Another set of special reports dealt with households that reported using gas air conditioners. 

Because the use of gas as a fuel for air conditioning is rare, all of the 117 sample households that 

reported it were listed and their data were reviewed. Some cases were resolved by reviewing data 

for the same household from the 1987 RECS, where similar checks had been undertaken, or from 

the Rental Agent or Supplier Surveys. Respondent contacts were attempted with 53 of the 117 

households. As a result of all of these efforts, the weighted estimate of the number of households 

using gas air-conditioners was reduced from 1.3 million to 0.4 million (Response Analysis 

Corporation 1992b, RECS-90 Memo #306 and EIA 1993a, p. 150).

The percentages of sample households for which telephone contacts have been successfully 

completed at any stage of the manual and computer edit procedures have been as follows 

(Consumption and Expenditure reports for years shown):

Survey Year Percent Contacted

1981 14

1982 10

1984 6

1987 1

1990 4

The gradual reduction in telephone contacts with Household Survey respondents during data 

processing, especially from 1987 on, reflected concerns that such contacts may have been hurting 

response rates for the Residential Transportation Energy Consumption Survey and for households 

in the longitudinal panel for RECS.

Analysis of Processing Changes in the 1984 RECS

As noted above, information about changes made to individual records in the initial unedited 

Household and Supplier data files is systematically maintained in archival files. The archival files 

for the 1984 RECS provided the basis for a detailed analysis of processing changes (Jabine 1987). 

The initial portion of the study was based on tables that had been produced as a matter of routine,

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 89
showing the number of changes and their distribution by "reason" for each of 562 Household 

Survey and 59 Supplier Survey variables. Summary findings for all variables combined for each 

survey are shown in Table 6.1 (the Billing Files contained Supplier Survey data for the five. 

fuels).

Table 6.1. 1984 RECS: Changes to the Household and Billing Files, by Reason

Reason

Changes to Household File

Number Percent

Changes to Billing Files

Number Percent

Keying error3

Coding error3

Clerical error (prior to coding)

Interviewer error

Respondent error

Interviewer or respondent error

Data processing error (after 

keying)

Phone call to respondent 

household

Phone call to utility/supplier 

Other phone call or information 

Rental agent (master meter)

1,868

3,699

NA

1,118

236

422

202

9.1 

18.0 

NA 

5.5 

1.2 

2.1

1.0

information

Kerosene survey information

Editor's judgement

Additional information from 

questionnaire

None of the above

Total

1,251

NA

9,807

545

411

20,472

6.1

NA

47.8

2.7

2.0

100.0

2,066a

374 

NA

122 

NA

1

20

4,134

50. Cf

9.0 

NA 

3.0 

NA

0.5

514 

256 

143

1,251 

NA

9,807

2.5 

1.3 496 

0.7 14

6.1 

NA 

47.8 1,016 

25

12.0 

0.3

24.6 

0.6

100.0

"Changes due to keying errors that could not be distinguished from changes due to coding errors.

-- = None in this category. 

NA = Not Applicable.

* = Less than 0.05 percent

Source: Jabine, Review of Computer Edit & Update Performance Statistics for the RECS, Final Report (December 1987).

90

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Knowledge of certain limitations and other features of the data shown in Table 6.1 is necessary 

for an informed interpretation:

* When review of a computer edit reject led to changes in a string of two or more 

consecutive variables in the same portion of the record, an archival file record was 

created only for the first variable. Consequently, the numbers in the table are 

undercounts of the total changes made.

 Not all of the changes made during processing were made because of errors; some of 

them were built into the processing system. About one-tenth of the changes to the 

Household File were recedes for questions that included an open-end "other" category. 

Recedes of the "other" responses were necessary because the final code structures 

could not be established until after the coding and keying operations had been 

completed and an analysis of the nature of the open-end responses was possible. 

Another one-tenth resulted from a special computer program that was used to insert 

leading zeros in square footage measurement variables for those households in which 

data from the prior (1982) survey were being used.

« The reason codes that were entered in the archival files are a mixture of two different 

dimensions: the source of the (presumably) incorrect value that was changed and the 

source of the information that allowed the editor to determine the (presumably) correct 

value. The reason "editor's judgment," which was assigned to nearly half of the 

Household File changes and one-quarter of the Billing File changes, does not provide 

much useful information in isolation, but its meaning becomes clearer when it is 

associated with a code showing the specific processing step in which the change was 

made (see Table 6.2).

Notwithstanding these limitations, it was clear from the data that keying and coding errors were 

a major source of computer edit rejects, accounting for more than one-fourth of the changes to 

the Household File and one-half of the changes to the Billing Files. The 193 Household Survey 

"key" variables for which data entry was verified 100 percent had an average of only 0.44 

changes, but the remaining 369 variables that were subject to sample verification averaged 4.83 

changes per variable. These findings prompted the decision to revert to 100 percent verification 

of data entry for the 1987 and subsequent surveys. Use of 100 percent verification has led to a 

substantial reduction in keying errors, although not necessarily to their complete elimination.

Overall, changes to the Household File averaged 3.6 per household and changes to the Billing 

Files averaged 0.5 per household (a household was counted once for each fuel for which Supplier 

Survey data for that household were available). Analysis of variables with large numbers of 

changes showed that 25 of the 562 Household Survey variables accounted for 42 percent of all 

changes. Most of these 25 variables were located in two areas of the questionnaire: the portion 

dealing with main and secondary heating equipment (11 variables) and the portion in which area 

measurements of the housing unit were recorded (6 variables). For the Billing Files, 10 of 59 

variables accounted for 81 percent of the changes. Many of these changes were made to 

beginning dates for billing periods.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 91
In the second part of the 1984 study of changes, codes were added to the archival records for th 

Household File to show the stage of data processing at which each change was made and di 

identification number of the interviewer for the household. Table 6.2 shows a distribution of dh 

Household File changes, by reason, for each step of the processing operations. The nature < 

most of the processes shown in the table is self-evident. The recode changes were mad 

primarily for two reasons: to assign initial "other" responses to the final set of categories adopte 

for an item and to make the area measurement data carried over from the 1982 RECS consister; 

with the 1984 format for these variables. Initial imputations of missing data were not include';: 

as changes in the archive files. The "imputation-related" changes shown in Table 6.2 are change;; 

that were necessary to make imputed variables consistent with related variables. The tabli; 

updates reflect changes that were made to eliminate anomalies detected in preliminary tabulation:;;, 

Utility and final updates shown in the last two columns of the table were made at the final stage;; 

of computer processing for a variety of reasons.

Many features of the processing operations are clear from Table 6.2, for example:

 Nearly all of the changes made to items rejected by range checks were to correct 

coding and keying errors;

 Consistency checks frequently required contacts with respondents and other source:;. 

of information or exercise of an editor's judgment to determine the correct values;

 Special reports followed a pattern similar to that of consistency checks, but fewer 

changes were made to correct coding and keying errors, presumably because most of 

them had already been detected by range and consistency checks.

The study report also includes analyses of changes by process for individual variables with large 

numbers of changes. Analyses of changes attributed to interviewer errors were used to guide the 

training of 1984 RECS interviewers who were scheduled to serve as interviewers or supervisor;; 

in the 1987 RECS.

The report included some recommendations for refinements to the archive files: (1) include a 

separate record for each variable changed; (2) include a code to show at what stage of processing 

the change was made; (3) include both old and new values for each change; and (4) replace the 

1984 RECS reason code with two codes, one showing the source of information on which the 

decision to change the value was based and one showing the probable source of the (presumecl'j 

incorrect value.

Archive files have been created for all subsequent survey years, but the "reason" codes were 

replaced by a set of "level-of-effort" codes, putting less emphasis on the source of the error and 

more on the level of effort required to correct it. Each change was assigned a single code: 

reflecting the greatest level of effort needed to reach a decision. Thus, for example, a change 

based both on examination of questionnaires and recontact with respondents would be assignee 

a code reflecting the typefs) of respondent contacts. All decisions about changes required 

reviewing both edit outputs and the original questionnaire on which the edited record was based 

Hence, codes for examination of questionnaires were assigned only when a decision required

Energy Information Administration / Energy Consumption Series 

92 Residential Energy Consumption Survey Quality Profile
Table 6.2. 1984 REGS: Percent Distribution of Changes for Each Process, by Reason

Reason

Process

Range 

Checks

Consistency 

Checks

Round 

1

Round 

2

Recedes

Special 

Reports

Imputation 

Related

Rental 

Agent

Table 

Updates

Utility 

Updates

Final 

Updates

Interviewer Error

Respondent Error

Interviewer or 

Respondent Error

Coding Error

Keying Error

Data Processing 

Error

Respondent Call

Utility Call

Other Call

Rental Agent 

Information

Editor's 

Judgement

Other 

Questionnaire 

information

Other

Total

Number of 

Changes

1.0 10.1

0.1

0.1 4.4

38.6 50.8

58.5 14.5

0.4 0.1

3.4

0.2

0.5

0.2 0.7

1.1 14.4

*

0.1 0.8

100.0 100.0

1,727 4,234

16.3

2.3

4.1

23.5

7.5

5.1

5.2

1.2

0.9

9.9

22.4

""

1.6

100.0

2,998

0.1 6.7

6.5

3.1

0.1 6.3

0.4

0.9

0.1 4.5

6.6

2.9

2.4

87.2 46.8

12.5

12.9

100.0 100.0

4,370 2,539

0.2

-

0.9

0,2

0.2

0.1

0.5 -- -- 61.3

1.0 -- -- 1.6

19.4

5.9 100.0 - 3.2

90.8 -- 100.0 14.5

0.2

100.0 100.0 100.0 100.0

3,664 634 40 124

--

--

-

0.7

--

9.2

0.7

0.7

--

4.2

84.5

~~

--

100.0

142

* = Less than 0,05 percent.

-- = None in this category.

Source: Jabine, Review of Computer Edit & Update Performance Statistics for the RECS, Final Report (December 1987).

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 93
Table 6.3. Changes to the Household File by Source of Information or by Reason: 1987 and 

1990 RECS

Source of Information 

______or Reason

Contacts with:

Number of Changes

1987 1990

Changes per 100 Households"

1987 1990 Change

Household.................................... 129

Fuel Supplier............................... 81

Rental Agent or Interviewer......... 67

Multiple Sources.......................... 22

Examination of:

Supplier Data............................... 86

Rental Agent Data....................... 1,399

Other Information......... ............... 275

Multiple Sources.......................... 668

Application of Inference Editing Rules:

Less Than 5 Minutes................... 5,338

5 Minutes or More........ .............. 342

Other

Correction of Prior

Processing Error........................ 243

Post-Imputation Change.............. 64

Recode of Open-End

Response or Special Rule......... 3,625

Dependent Change0 ..................... 13,130

Total

With Dependent Changes............ 25,469

Excluding Dependent Changes.... 12,339

37

6

196

3

202

568

29

133

6,828

164

210 

b

433

351

9,160d 

8,809d

2.2 

1.4 

1.1 

0.4

1.5

23.9

4.7

11.4

91.2 

5.8

4.1

1.1

61.9

224.2

434.9

210.7

'Number of completed personal interviews was 5,856 in 1987 and 4,828 in 1990.

"Category not used in 1990.

 A different definition may have been used in 1990, see text.

Excludes one change in category "Dummy Editor II Update."

Source: Reason for Change Tabulations, RECS Personal Interview Editing: 1987 and 1990.

0.8 

0.1 

4.1 

0.1

4.2

11.8

0.6

2.8

141.4

3.4

4.3 

b

9.0 

7.3

189.7

182.5

-1

-1.

3

- 0.

2.

-12. 

-4.

50.

-245. 

-28.

94

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
looking at a questionnaire other than the one that the record to be changed was derived from. 

Codes assigned to changes based solely on reviews of the outputs of range and consistency checks 

or special reports identified separately those instances where more than 5 minutes time was 

needed to reach a decision. The level-of-effort codes used in the 1987 and 1990 RECS are listed 

and explained in RECS Memo #007.

Summary tabulations of changes to the Household File records based on personal interviews for 

1987 and 1990 are shown in Table 6.3. In order to account for differences in sample size for the 

two years, the data are presented in terms of changes per 100 households.

The differences between 1987 and 1990 in the figures for changes of all kinds are dominated by 

a precipitous drop in the number of "dependent changes." This probably resulted from a change 

in the procedures for generating archival records and assigning level-of-effort codes, but it has 

not been possible to determine the exact nature of the change.

Aside from the effect of the different treatment of dependent changes in 1987 and 1990. the 

overall frequency of changes on a per-household basis was lower by about 13 percent in the latter 

year. Overall, the frequency of recontacts with respondents was about the same in both years, 

with a decline in the number of contacts with household respondents and suppliers being balanced 

by an increase in contacts with rental agents and interviewers. There was a substantial increase 

in changes based on the application of inference and editing rules, but a decline in the number 

requiring 5 minutes or more to reach a decision. There was a substantial decline in 1990 in the 

number of changes representing recedes of open-end and "other" responses or the application of 

special rules. Much of the decline appears to have been associated with changes made because 

of inconsistencies between entries in the part of the questionnaire that summarized uses and 

methods of payment for each fuel and earlier questions on these same topics.

imputation

At several stages during data processing, interviewers' or respondents' initial entries on 

questionnaires are changed or deleted, or values (including 0) are supplied for items initially left 

blank. In RECS, most of this imputation follows completion of the initial round of computerassisted edits and special reports. Following imputation, consistency checks are repeated to 

ensure that imputed values are consistent with other related variables.

This section discusses the imputation procedures used for the Household Survey, the Supplier 

Survey, and the weather information obtained from NOAA. Items missing on Rental Agent 

Survey questionnaires are not imputed; in general, the Household Survey responses for these 

missing items are accepted. The model-based allocation of energy consumption and expenditures 

to end uses, such as space heating, water heating, and appliances, is considered to be estimation, 

not imputation, because respondents and suppliers are not asked directly for this information. 

These allocation procedures are discussed in Chapter 7 in the section on "End-use Estimation."

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 95
Imputation for the Household Survey: Interview Questionnaires

The frequency of item nonresponse, based on unedited data files from the 1990 RECS, w:-:-s 

discussed in Chapter 4. Excluding variables related to household measurements, 51 of 416 

variables based on questionnaire entries had item nonresponse rates of 5.0 percent or more. Of 

the 10 variables with the highest nonresponse rates (see Table 4.4), only 3-age of hot water 

heater and two items related to household income required entries for more than 10 percent cf 

the households.

The treatment of each missing item requires two decisions. The first is whether or not to irnpu:;: 

a value for it. Since 1982 missing values have been imputed for roughly two-thirds of 'lu: 

Household Survey variables in each survey year (see Table 6.4). Items not imputed are those for 

which it is judged that there is not enough information for related variables to provide the bast;; 

for an imputation procedure that is likely to reduce the effects of nonresponse bias. For the 1990 

RECS, items not imputed included questions on the presence., type and amount of attic and floe r 

insulation, indoor temperatures, and the presence of wall insulation (EIA 1992, p.200).

For each item that is to be Imputed, a choice of the most appropriate method is required. Except 

in 1987, hot-deck imputation, in which the missing value is obtained from a household thai 

matches on variables related to the missing item, has been the most commonly used method. 

Based on an intensive review of imputation procedures prior to data processing for the 198?' 

survey, some variables were shifted from hot-deck to other methods of imputation. For example. 

the entire household grid (demographic characteristics of household members) was shifted fron 

hot-deck to an allocation method and deductive imputation methods were adopted for sonic 

variables related to main heating fuel and equipment. Some items were shifted back frcir 

deductive to hot-deck for the 1990 RECS and hot-deck imputation was used for most of the nev. 

variables in 1993.

Table 6.4. Imputation Methods Used for Household Survey Variables: 1981-1993

Imputation

Method

Percent of Items Subject

1981

Not imputed 23

Imputed 77

Hot-Deck 58

Random 13

Other3 6

1982

35

65

52

9

4

I

to Imputation

1984 1987

32

68

56

9

3

36

64

27

15

22

1990

32

68

42

13

13

1993

32

68

51

7

9

Total 

Number of Items'

100 

356

100 

443

100 

447

100 

422

100 

429

100 

559

'Includes regression, deductive, allocation, and modal imputation methods.

bExcludes items for which missing values, if any, were determined by explicit editing rules during the early stages of processing

Source: Energy Information Administration, Housing Characteristics (1981-1993).

96

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
In 1993, hot-deck imputation was the only procedure used for the 26 most frequently imputed 

variables (EIA 1995a, Table A8). The use of other procedures is limited to variables for which 

there is relatively little item nonresponse.

Other imputation methods that are or have been used include regression, random, deductive, 

allocation, and modal techniques:

 A regression equation, developed from questionnaires with usable data, is used to 

estimate the total square footage of each sample housing unit for which actual 

measurements are not obtained or are unusable. Variables used to predict the area of 

the unit include such housing unit characteristics, as type of housing unit, year built, 

number of rooms, number of bathrooms, and type of heating equipment, and such 

household characteristics as income and number of persons. A full statement of the 

equation and the variable definitions used in the 1993 RECS is given in the Housing 

Characteristics 1993 report (EIA 1995a, pp. 230-231).

 Random-selection imputation procedures are used for two purposes: to supply missing 

dates, such as the year and month a housing unit was occupied, and to supply missing 

values that are conditional on other known values, such as the number of storm 

windows in a house with a known total number of windows. A value is assigned at 

random from the appropriate distribution of values for households that report fully.

 Deductive procedures are used when the amount of missing data is small and other 

available information provides reasonably conclusive evidence of what the missing 

value should be. These procedures are used primarily when information is missing on 

fuels used for specific purposes and methods of payment for fuels used.

 Allocation procedures are used for imputation of missing information on household 

members, such as age, sex, and relation to householder. Rules for assigning missing 

values are based on the configuration of known information on these variables for 

other household members.

 Modal imputation procedures, which were used in RECS only in the earlier survey 

years, assign the most commonly reported value to the missing variable. Typically, 

a negative response would be assigned to a question on the presence of a relatively 

rare,item, such as the use of secondary water-heating equipment. In the more recent 

survey years, modal imputation procedures have been replaced by hot-deck or random 

methods in order to obtain better variance estimates.

Imputation for the Household Survey: Telephone and Mail Questionnaires

Nearly all household survey variables are imputed for the relatively small proportion of 

households for which questionnaires are obtained by telephone or mail rather than by personal 

interview (see Chapter 4, Table 4.1). These imputed values of housing unit and household 

characteristics are combined with the actual Supplier Survey and weather data obtained for these

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 97
households and their localities. Thus, the main purposes of obtaining mail and telephone: 

questionnaires for households not responding to interviews are to identify the energy suppliers 

for the unit, to obtain vehicle information needed to apply sample selection procedures for the 

Residential Transportation Energy Consumption Survey, and to provide the data needed to sele;r: 

a donor interview questionnaire for imputation of housing unit and household characteristics.

Figure 6.6 shows the steps in the modified hot-deck procedure that was used to impute data fc.r 

telephone and mail questionnaires in the 1993 RECS. Most of the procedures were computerized. 

The procedure ensured that no interview questionnaire was used more than once as a donor fir 

a telephone or mail questionnaire. Donor questionnaires were selected manually only for in;; 

small proportion of questionnaires for which a suitable donor was not identified by ii;1; 

computerized scoring rules. In the 1990 RECS, only 3 of the mail questionnaires required donors 

that did not match on all of the sorting variables used in Operation. 1; donors were selected from 

other Census regions for these donees (Response Analysis Corporation 1992b, p.4-22).

Figure 6.6. Imputation Procedure for Household Survey Mail and Telephone Questionnaires: 

1993 RECS

Operation Sorting/Matching Variables

1. Sort both donor (interview) and donee Census region

(mail and telephone) questionnaires into Type of housing unit structure 

groups based on basis of specified Space-heating fuel 

variables. Hot-water fuel

Presence of air-conditioning 

Type of air-conditioning

2. For each donee, pick the best donor from Income

the corresponding sort group, using a Number of persons in household 

scoring procedure based on specified Number of vehicles 

additional variables. Age of householder

Tenure (owned/rented)

Number of rooms

Model year of newest vehicle

Household type (married couple/other)

3. Assign donor values for all Household 

Survey variables, except number of 

vehicles, to the donee household.

Source: Energy Information Administration, Housing Characteristics (1993).

Imputation of Supplier Survey Data: Procedures

The level of imputation for Household Survey questions on housing and household characteristics 

is relatively low, but it has been somewhat higher for consumption and expenditure data for the 

5 major fuels, for which the preferred source of information is the actual bill data obtained from 

energy suppliers. Two kinds of imputation are required. In a process called "annualization," bill 

data obtained from suppliers are used to arrive at estimates of consumption for a 1-year period,

Energy Information Administration / Energy Consumption Series 

98 Residential Energy Consumption Survey Quality Profile
For those households for which usable bill data are not obtained, annual consumption is imputed 

on the basis of housing unit and household characteristics. The extent of need for the latter type 

of imputation depends on eligibility and completion rates for the Supplier Survey. As can be 

seen in Table 4.7, Chapter 4, Supplier Survey completion rates have been relatively high for 

electricity and natural gas, somewhat lower for fuel oil and LPG, and quite low for kerosene. 

Table 4.8 shows that imputation of consumption of electricity and natural gas is seldom needed 

for single family houses but is needed more often for housing units in multiunit structures.

The procedures for estimating annual consumption differ by fuel type. For electricity and natural 

gas, the reporting unit for each household in the Supplier Survey is the billing period, and an 

"annualization" process is used to convert the data by billing period to an estimate for a 365-day 

period. For the 1993 RECS, utilities were asked to provide data for the sample households for 

all billing periods starting on or after December 1, 1992, and ending prior to the date at which 

they were asked to complete the form, generally in the late winter or early spring of 1994. They 

were asked to report the beginning and ending date for each billing period, the amount consumed, 

the cost, and whether the amount was based on a reading by the customer or on a reading or 

estimate by the company. Suppliers were instructed to provide bill data only for the specific 

account for which a waiver was obtained in the Household Survey. Thus, if the sample 

household did not occupy the housing unit for all of 1993, bill data would, in most instances, be 

obtained only for the portion of the year during which they occupied it.

For electricity and natural gas, estimation of annual consumption from billing period data was 

attempted only in the following circumstances:

1. The household paid for some or all uses of the fuel and had 146 or more days of bill 

data; or

2. The household paid for appliance and/or water heating use, did not pay for space 

heating or space cooling use, and had 60 or more days of bill data.

When these criteria were met, the procedure for annualization of a household's bill data followed 

the steps shown in Figure 6.7. First, an annualization period was defined, consisting of 

consecutive billing periods with a start date as close as possible to January 1, 1993 (the first day 

of the reference year for consumption), and with the total number of days as close as possible 

to 365. Second, a consumption year was defined, containing exactly 365 days and matching the 

annualization period as closely as possible. Third, predicted values of consumption for the 

annualization period and the consumption year were derived from a nonlinear model by using 

available information about the household's uses of the fuel, the number of days in the period, 

and the number of heating and cooling degree-days in the period. Finally, the actual consumption 

reported for the annualization period was adjusted by the ratio of the values of predicted 

consumption for the consumption year and the annualization period. Expenditures for the 

consumption year were estimated by applying the unit cost for the annualization period to the 

estimated value of consumption for the consumption year. A special adjustment procedure was 

applied to consumption and cost estimates in those instances where the household paid for some 

but not all uses of electricity or natural gas. In 1993, such households accounted for 0.7 percent 

of total annual electricity consumption and 1.9 percent of natural gas consumption (EIA 1995d, 

Table B7).

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 99
Figure 6.7. Imputation of Annual Household Consumption from Billing Period Data for Electric?!; 

and Natural Gas

Step Criteria/Procedure

1. Select billing periods for (1) Continuous data.

use in estimation. (2) Start date close as possible to January 1, 1993. 

These billing periods (3) Total days close as possible to 365. 

cover the "annualization 

period" (AP).

2. Define the consumption (1) Must contain 365 days.

year (CY). (2) Match AP as closely as possible.

3. Calculate predicted Prediction model based on prior survey Inputs include household

consumption (Cp ) for AP uses of fuel, number of days in period, and number of heating and

and CY. cooling degree days in period.

4. Calculate inputed C, (CY) = Actual consumption for AP x Predicted consumption for CY 

consumption C, for CY. ______________ Predicted consumption for AP

Source: Response Analysis Corporation, 1990 RECS Data Editing & Manipulation Procedures Manual (September 1992).

The inclusion of information on heating and cooling degree-days as part of the prediction models 

for the annualization procedure started in the 1990 RECS. Prior to 1990, the prediction models 

relied only on the total number of days in the annualization period and the consumption year 

(Response Analysis Corporation 1992b, p. A-195).

For fuel oil, LPG, and kerosene, the reporting unit in the Supplier Survey is the delivery. 

Suppliers were asked to report all deliveries from October 1, 1992, through the date at which they 

completed the form. For each delivery they were asked to report the type of fuel, the amouni;, 

the price per unit of volume, and the total price. They were also asked to report the beginning 

and ending dates of the period covered by the recorded deliveries. If the beginning and ending 

dates covered all of calendar year 1993, only those deliveries occurring during 1993 were 

included as part of consumption. If the data on deliveries did riot cover a full year, the Supplier 

Survey data for that household for fuel oil, LPG, or kerosene were not used. It would be 

possible to develop an imputation procedure that made use of part-year data on deliveries, hire 

the number of households with part-year data is so small that the addition of such a procedure 

would have a low payoff.

The Household Survey questionnaire included some questions on deliveries and use of fuel oil. 

LPG, and kerosene. When no usable Supplier Survey data were available, these responses could. 

sometimes be used to estimate annual consumption. This occurred frequently for kerosene, but 

rarely for fuel oil and LPG.

As explained more fully in Chapter 7 in the section on End-Use Estimation, a separate nonlinear 

regression model was developed for each fuel, based on data for sample households that had a

Energy Information Administration / Energy Consumption Series 

100 Residential Energy Consumption Survey Quality Profile
full or nearly full year of acceptable bill data, and was used to allocate total consumption of each 

fuel to 5 use categories: space heating, water heating, air-conditioning, refrigerators, and other 

appliances. This same model was used to estimate total consumption of a fuel when the sample 

household used it and did not have usable bill data from the Supplier Survey or, for kerosene, 

from the Supplier or Household Survey. The regression imputation procedure included the 

addition of a random error component, making it possible to calculate estimates of sampling error 

without separating imputed and unimputed data.

Finally, an imputation adjustment was made for each fuel for any household reporting in the 

Household Survey that some of its bills covered non-household uses of that fuel, for example, 

for a farm or home business or another household. In such instances, total consumption was 

reduced by a scale factor developed on the basis of responses to a Household Survey question 

about the proportion of the bills for that fuel which covered the non-household uses. The 

percentages of estimated total annual consumption for such households for each fuel in 1990 and 

1993 were:

Fuel 1990 1993

Electricity 2.8 4.9

Natural gas 0.6 0.6

Fuel oil 1.3 2.0

Kerosene 0.1 1.0

LPG 3.2 4.4

Imputation of Supplier Survey Data: Quality implications

As noted in Chapter 3, the goal of RECS is to collect data, for a sample of households, on energy 

consumption of each major fuel used for residential purposes during a specified time period (for 

the 1993 RECS, calendar year 1993). Chapters 4 and 5 have revealed several factors that pose 

problems for the acquisition of precise data for each household, most of them related to 

nonresponse or incomplete response to the Supplier Survey. The primary factors, the procedures 

used to deal with them, and their effects on the accuracy of consumption estimates are 

summarized in Figure 6.8.

For electricity and natural gas, the ideal situation would be to have, for each household, metered 

values of total consumption, for household uses only, for the calendar year covered by the survey. 

Because the metering and billing practices of utilities seldom meet these precise requirements, 

various kinds of compromises and approximations are required. For fuel oil, LPG and kerosene, 

direct records of consumption do not exist, so information about delivered amounts during the 

consumption reference period is used as a proxy.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 101
Figure 6.8. Sources of Error in RECS Consumption Data

Fuels Affected and Source of Error

Electricity and natural gas

No separate metering for household

Billing periods do not coincide with 

reference year.

Billing periods cover only part of 

reference year.

Estimated bills.

Household pays for some but not 

all uses.

Fuel oil, LPG, and kerosene

Data available for deliveries, not 

actual consumption.

All fuels

Changes in occupancy during 

reference year.

No supplier survey data obtained.

Nonresidential uses included in 

bills.

Estimation Procedures

Model-based imputation based on household survey 

data.

Annualization procedure.

Annualization procedure.

Influences choice of annualization period.

If bill amounts are annualized, the results are 

adjusted upwards.

Estimation based on delivered amounts.

Household generally treated as if occupied for full 

year.

Model-based imputation based on household survey 

data.

Amounts scaled back based on household survey 

estimate of proportion nonresidential.

For all fuels, bill data from the Supplier Survey normally only cover the period during which the 

sample housing unit was occupied by the household that was present at the time of the Household 

Survey interview. In cases of part-year occupancy, the imputation procedures treat such housing 

units as though they were occupied and consumed fuels at the same rate for the entire reference 

year. Because some of these units were actually not occupied or even not ready for occupancy 

for part of the year, this approach to imputation overstates their consumption. However, this 

overstatement may be at least partly offset by the failure to obtain consumption data for units that 

were occupied for part of the consumption year but were vacant during the interview period for 

the Household Survey.

102

Energy Information Administration I Energy Consumption Series 

Residential Energy Consumption Survey Qualify Profile
Table 6.5 shows, for 1990 and 1993, the proportion of total annual consumption of each fuel that 

was estimated or imputed by the various methods just described. For fuels other than kerosene, 

bill data for all or most of a year were the basis for roughly two-thirds to four-fifths of the 

estimated amounts. For electricity and natural gas, less complete bill data accounted for about 

8 or 9 percent of the total amounts. The proportion of consumption based on regression estimates 

varied from one-tenth for electricity to slightly more than one-third for fuel oil.

Table 6.5. Basis of Estimates of Annual Consumption: 1990 and 1993 RECS (Percent of Total

Consumption of Each Energy Source)

Source of 

Consumption 

Estimates

Fuel and Year

Electricity

1990 1993

Natural Gas

1990 1393

Fuel Oi!

1990 1993

Kerosene

1990 1993

LPG

1990 1993

Actual Billing Records

Covering All Uses 

330 or more days* 

146 to 329 days 

60 to 145 days

Covering Some Uses

Estimate from Supplier or 

Household"

Regression Estimate 

Total

Percent of Total 

Consumption 

Accounted for by Fuel

80.5

7.5

0.2

0.7

NA

80.2

8.5

0.1

0.7

NA

74.3

6.4

0.1

2.0

NA

74.1

6.3

*

1.9

NA

64.7

NA

NA

NA

0.1

67.4

NA

NA

NA

0.9

28.5

NA

NA

NA

43.0

27.8

NA

NA

NA

34.4

71.6

NA

NA

NA

0.3

79.1

NA

NA

NA

0.1

11.0 10.5 17.3 17.7 35.3 31.7 28.5 37.8 28.1 20.9

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

32.5 32.8 53.1 52.7 10.6 10.2 0.7 0.5 3.0 3.8

"For fuel off, kerosene, and LPG, billing records were used only if they covered 365 days.

bFor kerosene, She estimate was supplied by the household.

* = less than 0.05 percent.

NA = Not Applicable.

Note: Because of founding, percents may not sum to 100.0.

Sources: Energy Information Administration, special tabulations of RECS data files for 1990 and 1993.

For kerosene only, a substantial proportion of total consumption (more than two-fifths in 1990 

and about one-third in 1993) was estimated from information on deliveries and purchases supplied 

by household respondents. Slightly more than one-fourth was based on bill data from the 

Supplier Survey and the remainder was based on regression estimates.

The basis for estimates varied substantially by type of living quarters. For electricity in 1993, 

for example, the percent of consumption based on regression estimates by type of structure was:

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 103
Type of Structure Percent Based on Regression

Estimate

Mobile home 10.8

One-family detached 7.5

One-family attached 12.6

2 to 4 housing units 25.7

5 or more housing units 22.9

This kind of variation occurs because many of the households in multiunit structures are living 

in rental units for which some of the utilities are included in the rent and are therefore not 

eligible for the Supplier Survey. Similar patterns with more pronounced differences by type of 

structure can be observed for natural gas, fuel oil, and LPG.

As shown on the last line of Table 6.5, the percent of total consumption accounted for by each. 

fuel varies substantially, from 52.7 percent for natural gas in 1993 to 0.5 percent for kerosene 

in the same year. Using these percents and the data in the body of Table 6.5, it is possible to 

calculate the basis for estimates of annual consumption for all fuels combined:

Source of consumption estimate 1990 1993

Bill data for all or most of 

year 74.8 75.4

Partial bill data 7.3 7.4

Estimate from supplier or 

household 0.3 0.3

Regression estimate 17.5 17.0

Imputation of Missing Weather Data

Weather data obtained from the National Oceanic and Atmospheric Administration (NOAA) for 

use in RECS include daily temperature data for each of NOAA's \veather stations. These data, 

which are used primarily to estimate heating and cooling degree-days for sample housing units, 

are sometimes missing for one or more days. Through the 1984 RECS, average temperatures for 

all weather stations in a NOAA division were used for this purpose. Starting with the 1987 

RECS and subsequently, an individual weather station has been selected to provide temperature 

data for each cluster of sample bousing units. The extent of missing temperature data is one of 

the factors considered in selection of a weather station to be associated with each cluster; data 

quality is considered acceptable if data are missing for fewer than 15 days of the consumption 

reference year. Once the stations are selected, missing temperature data are imputed by making 

use of the relationship between division temperatures and station temperatures for the previous 

year. For each survey year, for those clusters that remain in the sample from earlier years, the

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104 Residential Energy Consumption Survey Quality Profile
selection of the associated weather stations is reviewed to take into account changes in the extent 

of missing temperature data (Response Analysis Corporation 1992b, pp. 8-31 to 8-35).

Evaluation of Imputation Procedures

Because of concern about the high proportion of imputed consumption data for housing units in 

apartment buildings, a special study was undertaken as part of the 1981 RECS. Permission was 

obtained from selected apartment building managers to obtain actual fuel records for their 

buildings, each including one or more RECS sample housing units. Total consumption for each 

building was allocated equally to the apartment units in that building, and the estimates for the 

sample units were compared with imputed values assigned by the regression modeling procedures 

used for units lacking Supplier Survey data. These comparisons indicated biases in some imputed 

values (EIA 1983b, p. 102):

Adjust. 

Households Using Imputed Values Are: Factor

Electricity with air-conditioning Too low by 50 percent 1.84

Electricity, no air-conditioning Too high by 10 percent None

Natural gas for space heating About right None

Natural gas, but not for space 

heating Too low by 50 percent 2.04

Records of use of fuel oil and LPG in apartments were insufficient in number to make reliable 

estimates of bias in their imputed values. As a result of this study, the adjustment factors shown 

above were applied to imputed values of electricity and natural gas consumption in apartments. 

The same adjustment factors were used in the 1982 RECS. For the 1984 RECS, the regression 

imputation model was revised to reflect differences between apartments and other units more 

explicitly, so that these final adjustments were no longer necessary.

RECS questions about temperature setting behavior in the household have been among those for 

which missing values have not been imputed. Battles and Harrison (1992), using 1990 RECS 

data, experimented with several regression models in an attempt to identify some of the household 

and housing unit characteristics that relate to temperature setback behavior when natural gas, 

electricity, or fuel oil is used for space heating. They also hoped to develop discrete temperature 

models that could be used to impute missing temperatures. Some of their findings were:

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Residential Energy Consumption Survey Quaiity Profile 105
 Low income was a significant factor in a household's decision to reduce temperatures, 

in homes heated with fuel oil, but not in those heated with natural gas or elecuricky.

 For all three fuels, homes with higher daytime temperatures were likely to have higher 

setback temperatures as well. Homes that had recently installed a clock thermostat 

were likely to have lower setback temperatures.

 The colder the climate, the higher the proportion of households that set bar:-;: 

temperatures.

In spite of these and other significant findings, the fit of the models developed was not though;': 

to be good enough to use them to impute missing values, so they were not imputed for these 

items in the 1993 REGS.

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Residential Energy Consumption Survey Quality Profile
7* and Error

This chapter includes sections on the weighting procedures used to develop sample estimates, the 

model-based procedures for allocating total consumption of each fuel to specific end uses, and 

the estimation and presentation of information about sampling errors of the estimates. Each 

section begins with a description of the procedures used in the 1993 RECS. Information about 

current procedures is followed by an account of procedural and design changes aimed at 

improving the quality of the estimates that have been introduced since the first survey (NIECS) 

in 1978.

Weighting Procedures

Weighting Procedures for the 1993 RECS

The sample weighting procedures used in RECS closely parallel those used in other U.S. national 

household surveys, such as the Census Bureau's Current Population Survey. Figure 7.1 provides 

an overview of the sample weighting procedures that were used to produce estimated totals for 

the 1993 RECS target population from the sample data, following completion of all editing 

operations and imputation for item nonresponse.

The overall weight assigned to each household is equal to the product of the weighting factors 

assigned to it in the four steps shown in Figure 7.1.

Figure 7.1. REGS Sample Weighting Procedures: 1993

Step Description Purpose Auxiliary Data

Apply sampling weights

Adjust for unit nonresponse

Ratio estimate, Stage 1

Ratio estimate, Stage 2

Reflect sample selection 

probabilities

Reduce effects of 

nonresponse bias

Reduce between-PSU 

sampling variance

Reduce mean square error

None

None

Uses data from 1990 

Census

Uses Current Population 

Survey estimates as 

control totals

Source: Energy Information Administration, Housing Characteristics (1993).

Step 1: Application of Sampling Weights. Each household record is assigned a weight equal 

to the reciprocal of its overall probability of selection. The overall probability of selection is the 

product of the selection probabilities at all stages of sampling: selection of primary sampling 

units (PSU's), selection of secondary sampling units (SSU's), selection of listing segments within 

SSU's, and selection of addresses from the listings. In some instances, the selection probability

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 107
at one or more stages can be one., as in the case of large metropolitan area PSU's that are selectee: 

with certainty.

Sampling weights vary across SSU's for two reasons. The first is that the allocation of sample 

SSU's to Census divisions is not directly proportional to the number of households by division, 

Proportionately more sample SSU's are assigned to divisions with fewer households in order to 

ensure that estimates of acceptable reliability can be made for each division.

The second source of variation in sampling weights is oversarnpling of targeted population 

groups. In the 1993 RECS, two groups of special interest were oversampled: low income 

households and new houses. For the first group, oversarnpling was accomplished by using highsv 

sample selection rates in SSU's determined by interviewers to be in low-income areas, especially 

in areas where the main heating fuel was something other than natural gas. For the second group. 

a supplemental sample of SSU's was selected from Census tracts or block groups with a high 

percentage of units constructed in the 6-year period prior to the 1990 Census.

In the 1993 RECS, for the first time, sampling weights could also vary within SSU's. Housing 

units judged to be new by field workers during the listing operation were sampled at a higher rate 

than other units.

Step 2: Adjustment for Unit Nonresponse. The basic procedure for 1993 was to form a set of 

weight-adjustment cells consisting of sample households with similar attributes. For each. cell. 

an adjustment factor was calculated by dividing the total number of assigned sample households, 

including households not interviewed, by the number of interviewed households. If the factor 

was greater than 2.0, similar cells were collapsed, according to predetermined rules, to form a 

cell for which the weighting factor was 2.0 or smaller.

The variables used to form the weight-adjustment cells for the 1993 RECS were as follows:

Geographic Domains. These included the nine Census divisions, with Alaska and Hawaii treated: 

as separate domains. Within the four largest Census divisions, the large metropolitan areas that 

had been selected with a probability of one were also treated separately.

Weighting Classes. Within the geographic domains, subdomains were formed consisting of 

SSU's and individual housing units that had the same basic sampling weights. As noted 

previously, basic weights varied primarily because of procedures for oversarnpling newly 

constructed housing units arid those in low-income areas.

Weather Zones. These domains were based on long-term heating and cooling degree-day 

averages for counties.

Housing Unit Type. Individual housing units were grouped by type of structure: single family 

detached, single family attached, multifamily with two to four housing units, and multifarnily 

with five or more units.

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108 Residential Energy Consumption Survey Quality Profile
A large number of weight-adjustment cells were formed on the basis of these four characteristics. 

When collapsing was necessary, it was done in the reverse order of the characteristics listed 

above, that is, starting with the combination of cells representing different housing unit types.

Step 3: Stage 1 Ratio Adjustments. The stage 1 ratio adjustment factors for the 1993 RECS 

were based on 1990 Census data for the PSU's in strata that were not self-representing; that is, 

they did not consist of a single PSU that was selected with certainty. A separate adjustment 

factor was created for each of 36 groups of non-self-representing strata, defined by the 4 Census 

regions and 9 space-heating fuel categories. The adjustment factor for each group was the ratio 

of the 1990 Census count of households for all PSU's in the group to an estimate of that count 

based only on the sample PSU's in the group.

A restriction was placed on the calculation of the adjustment factor that if the denominator, that 

is, the estimate of Census households in a region and fuel category, was less than 3 million, that 

fuel category had to be combined with one or more other categories so that the denominator of 

the calculated adjustment factor was at least 1 million.

Each of the adjustment factors was applied to the weights for all RECS sample households in the 

corresponding region and space-heating fuel category or categories. Adjustment factors for the 

1993 RECS varied from 1.1688 for natural gas in the Northeast Region to 0.7897 for LPG in the 

Midwest Region.

In sampling terminology, the goal of the first-stage ratio adjustment is to reduce the between PSU 

component of the sampling variance by using Census information on heating fuels that is 

available for all PSU's. It can be looked at as a method of compensating for chance factors that 

may lead to the selection of samples of PSU's whose proportion of households using specified 

heating fuels at the time of the 1990 Census was higher or lower than the corresponding 

proportion for all PSU's. Since the distribution of households by heating fuel does not change 

rapidly, one can expect that these samples of PSU's would deviate in the same direction with 

respect to the distribution at the time of the survey.

Step 4: Stage 2 Ratio Adjustments, For the 1993 RECS, the second-stage ratio adjustments 

consisted of four separate steps. In each of these steps, the sampling weights were ratio adjusted 

so that the sum of the weights for specific categories agreed with the control totals obtained from 

the Current Population Survey (CPS). Because estimated household counts are available only 

from the March CPS each year, the control totals for the survey reference month (July 1993 for 

the 1993 RECS) were derived by linear extrapolation from the CPS estimates for March 1992 

and March 1993. The first of the stage 2 ratio adjustment steps started with the weights resulting 

from Step 3 (stage 1 ratio adjustments). The next three steps in stage 2 started with the weights 

resulting from the previous step.

The rationale for these adjustments is the expectation that the mean square error of the RECS 

estimates can be reduced by benchmarking them to the more precise estimates available from the 

CPS. The CPS estimates are believed to be more precise than the RECS estimates prior to 

benchmarking for two reasons: the CPS uses a sample of households that is roughly 10 times 

the size of the RECS sample, and the CPS sample estimates have themselves been benchmarked

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 109
to post-censal projections of Census household counts. There is considerable evidence from tb'r: 

CPS and other surveys that survey coverage, especially for some population subgroup;;, is 

significantly below the coverage of the population that is obtained in the decennial censuses (for 

further discussion of this point, see Cox. 1995).

The four steps in the stage 2 ratio adjustments were as follows:

Step 4.1 Weights derived from Steps 1 to 3 were adjusted so that their sum equalled theextrapolated CPS household counts for each of 4 large States-California, New York, Texas and 

Florida and for each of the 9 Census divisions.

Step 4.2 Weights derived from Steps 1 through 4.1 were adjusted so that their sum equalled the. 

extrapolated CPS counts for 12 categories defined by the 4 Census regions and 3 "MSA 

(metropolitan statistical area) status" classifications: central city of MSA, remainder of MS A, and 

non-MSA.

Step 4.3 Weights derived from all preceding steps were adjusted so that their sum equalled the: 

extrapolated CPS counts in three categories: one-person households occupied by males, oneperson households occupied by females, and all other households.

Step 4.4 Step 4.1 was repeated, so that the final weights resulted in exact agreement with the 

CPS-based household counts for the four large States and nine Census divisions.

Step 4, with its series of successive adjustments to different sets of marginal totals, can be 

regarded as a raking procedure designed to minimize differences between RECS and CPS 

estimates of the distribution of households by geographical and other classifiers.

Changes in Sample Weighting Procedures

The basic structure of the sample weighting procedures, as shown in Figure 7.1, has been, the 

same for all survey years. However, there have been several changes in the details, aimed mostly 

at improving the precision of the survey estimates.

The basic sampling weights (Step 1 in Figure 7.1) have varied as necessary to reflect the specific 

sample selection procedures used in each survey year. Oversampling of households in lowincome areas occurred in the 1981, 1984, 1987, and 1993 survey years. Oversampling of new 

housing units occurred for the first time in 1993.

Because of the procedures used to oversample new housing units, 1993 was the first survey year 

for which it was possible for an SSU to have households with different basic sampling weights, 

This change led to a significant revision of the procedure for calculating, the factors used to adjust 

for unit nonresponse. In all prior survey years, a separate adjustment factor was calculated for 

each SSU by dividing the total number of assigned sample households in the SSU by the number 

for which interviews had been completed. If the factor was greater than 2.0, the effect of the 

adjustment was spread across other SSU's in the same PSU. As noted above, for the 1993

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110 Residential Energy Consumption Survey Quality Profile
RECS, the initial weight-adjustment cells were formed across SSU's, combining housing units 

with the same basic sampling weights from a group of SSU's with similar characteristics. This 

procedure also made it possible to take account of another characteristic of individual housing 

units, namely type of structure, in deriving the nonresponse adjustment factors.

The stage 1 ratio adjustment (Step 3 in Figure 7.1) has been essentially the same for all survey 

years, with minor changes made to conform with changes in the space heating fuel categories 

used in the most recent Census of Population and Housing. The stage 2 ratio adjustment 

procedure (Step 4 in Figure 7.1) has been modified to make use of a successively larger number 

of control totals from the CPS.

For survey years 1978 through 1982, a single set of adjustments was made to 12 geographic 

control totals consisting of CPS estimates of housing units for the 4 Census regions and 3 location 

categories central city, remainder of metropolitan statistical area, and nonmetropolitan. 

Examination of the resulting RECS estimates of the number of one-person households for these 

years showed that they were consistently about 3 percent below comparable CPS estimates 

(Response Analysis Corporation 1983). Consequently, for the 1984 RECS, an intermediate ratio 

adjustment was introduced using national CPS estimates as controls for the number of households 

in three categories: one-person, male; one-person, female; all others. One more stage was 

introduced in the 1993 RECS. As explained above, the first step was based on CPS estimates 

for four large States and the nine Census Divisions, and this step was repeated following the 

intermediate steps, ensuring exact agreement between RECS and CPS estimates for these 

geographic domains.

Application of the second-stage ratio estimate procedures is dependent on the availability of the 

control totals from CPS. Because of uncertainty as to whether March 1991 CPS estimates would 

be adjusted for undercoverage in the 1990 Census, the necessary CPS data were not available 

when the 1990 RECS weights were first developed in September 1991. Consequently, the control 

totals for the survey reference month, November 1990, were developed by forward extrapolation 

from the March 1989 and March 1990 CPS estimates. In November 1991, the March 1991 CPS 

estimates were released and revised second-stage ratio adjustments were developed by 

interpolation between March 1990 and March 1991. For most of the 12 region/location cells, the 

change in the RECS estimates was 1 percent or less, but the RECS estimates in the Northeast 

Region increased by 3 percent for the central city metropolitan domain and by 2 percent for the 

nonmetropolitan domain (Battles 1991b).

Special Estimation Procedures for New Homes

Initial estimates of average energy consumption per household by year built from the 1990 RECS 

showed a striking reversal of a previously consistent trend for newer homes to consume less 

energy. The estimated average consumption for homes built in the 1988-1990 period was 103.1 

million Btu, 53 percent above the estimate of 67.6 million Btu for homes built in the 1985-1987 

period. Both estimates were based on relatively small samples of households, 225 for 1985-1987 

and only 138 for 1988-1990 (EIA 1993a, Table 11, p.28).

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 111
In an attempt to better understand the factors associated with the apparent trend reversal, date 

from the Census Bureau's Survey of Construction and Survey of New Mobile Home Placements 

were used as ancillary data to produce new estimates of average consumption per household for 

new homes. Two different estimation procedures were developed: a post-stratification procedure. 

and a ratio-adjustment procedure. They are described in detail in the Consumption am' 

Expenditures Report for 1990 (EIA 1993a, pp. 173-181).

The post-stratification procedure used nine strata defined by a combination of Census region, type: 

of home, and main space-heating fuel. The post-stratified estimate was a weighted average o 

the RECS estimates of average consumption per household for the nine strata, with the weights; 

being the Census Bureau estimates of the proportion of housing units in each stratum. The ratioadjustment procedure was based on Census Bureau estimates of the distribution of new hom.e:; : 

by Census region and the increase in average heated floor space by region for homes built in 

1988-1990 compared with those built in 1985-1987. Ratio-adjusted RECS estimates of average 

consumption per household were based on adjustments that eliminated or reduced RECS-Census 

differences for these two characteristics.

Both of the revised estimates showed a substantially smaller increase in average energy 

consumption for homes built In 1988-1990 compared with those built in 1985-1987:

Estimate Average Consumption Percent

(millions of Btu) Increase 

1985-87 1988-90

Original RECS estimate 67.6 103.1 53

Post-stratified estimate 74.5 89.7 20

Ratio-adjusted estimate 70.6 90.3 30

Standard errors of the post-stratified estimates were appreciably smaller than those of the original 

RECS estimates for both periods (Latta 1993, p. 14). Standard errors were not computed for the 

ratio-adjusted estimates.

The estimation procedures used in this instance were designed to improve the precision of £ 

specific class of RECS estimates, and it is doubtful whether their application across the board for 

all estimates would be feasible or desirable. However, they illustrate the potential for using pos> 

stratification and allied techniques for improving estimates used in specific kinds of analyses.

Special Weighting Procedures for Buildings

Appendix B to the Consumption and Expenditures 1990 report presented, for the first time, some 

tabulations of RECS data that used the building, rather than the household or housing unit, as the 

unit of analysis (EIA 1993a, Table B6, p. 152). Additional data on residential buildings were 

presented in a 1995 EIA report, Buildings and Energy in the 1980's (EIA 1995b). According

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112 Residential Energy Consumption Survey Quality Profile
to the building definition used in EIA's Commercial Buildings Energy Consumption Survey, most 

housing units correspond to separate buildings; however, this was not the case for units in 

multiunit apartment buildings.

Estimates of the number of buildings were obtained by dividing the sampling or base weight for 

each RECS sample housing unit in a multiunit building by the total number of housing units in 

that building. This information had been collected in the Household Survey for sample housing 

units in buildings with five or more housing units, but had not been collected for sample 

households in buildings with two to four housing units. Therefore, for the latter group, a constant 

divisor was used in each Census region, based on data from the 1990 Census of Housing and 

Population.

RECS estimates of the number and total floorspace of residential buildings are subject to two 

kinds of biases:

« The number of buildings is underestimated because RECS interviews are not 

conducted in vacant housing units. The amount of underestimation is likely to be 

similar to the housing unit vacancy rate, which was estimated by the Census Bureau's 

American Housing Survey to be about 9 percent in 1989.

 The size of multiunit buildings is understated because the floorspace of common areas, 

such as hallways, stairwells, elevators, and lobbies is not accounted for.

RECS estimates of energy consumption for multiunit residential buildings are probably also 

understated because they are made by applying appropriate weights to metered or estimated 

consumption for individual units in those buildings. Consumption for heating, cooling, and other 

uses in common areas of these buildings is not accounted for.

No attempt has been made to adjust the published estimates for these sources of bias. Their net 

effect on estimates of energy intensity (thousand Btu per square foot) is unknown.

End-Use Estimation

Introduction

In addition to knowing the total residential energy consumption for each of the five major fuelsnatural gas, electricity, fuel oil, LPG and kerosene energy analysts and policymakers need 

information about the allocation of these amounts to different end uses, such as space heating, 

water heating, air-conditioning, and appliances. However, utility bills, the primary source of data 

on total consumption, are not broken down by end use, nor is there any practical means by which 

such information could be obtained directly from each sample household. Consequently, an 

indirect, model-based nonlinear regression technique is used in RECS to provide estimates of the 

consumption of each fuel by end use for each sample household. The same technique is used to 

estimate total consumption for those households and fuels for which no usable utility bill data 

have been obtained.

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Residential Energy Consumption Survey Quality Profile 113
There are three main steps in the modeling and estimation process:

1. For each fuel, parameter values in a preliminary model for end-use allocation are 

estimated by using data only for sample households that used the fuel, have usable 

billing data, do riot have imputed values for key independent variables in the mod*;]., 

and meet other quality requirements. The process is iterated, eliminating (with some 

exceptions) independent variables whose estimated coefficients do not differ from zero 

by at least four standard deviations and, if necessary, removing outliers, i.e.. 

households with large differences between estimated and reported consumption, from 

the data base. Model parameters for natural gas are estimated first, because certain 

relationships in the natural gas model are carried over to other fuels.

2. The final model from step 1, with estimated parameter values, is used to impute: 

missing values for total consumption for each fuel.

3. For all sample households, the final model is used to estimate total consumption o. 

each fuel by end-use category. For each household, the end-use consumption estimate::! 

are "normalized"; that is, they are adjusted proportionately to sum to the reported 01 

imputed value of total consumption.

The RECS end-use estimation techniques have been gradually developed and refined since the: 

first survey in 1978. Through the 1982 RECS, the techniques were considered experimental and 

the results were published in special reports and articles (ElA 1983c, Thompson 1987). Startiru: 

with the 1984 RECS, the estimates of end-use consumption by fuel have been published routinely 

in the Consumption and Expenditures reports (the data for 1984 were published in Volume 2. 

Regional Data, of the report). However, refinements have continued, as described in more detail 

below. The nature of the estimation procedure is such that the regression equations used in each 

survey year are unlikely to be identical to those used in the preceding survey year.

The specific estimation equations for each end use depend on the kinds of information collected, 

in the Household Survey or from other sources that are relevant to that end use. For space 

heating, for example, such variables as heating degree-days, type, size, and age of the housing 

unit, amount of heated space, thermostat settings, type of heating equipment, and amount and type 

of secondary space heating are all likely to be associated with variations in consumption. V/Mle 

many of these variables are of interest in their own right, the inclusion of questions needed to 

provide inputs to the end-use estimation equations has always been a major consideration in the 

choice of content for the RECS Household Survey questionnaire.

Although the full equations used for the 1984 survey were presented in the regional supplement 

to the Consumption and Expenditures report and the full equations for the 1987 and 1990 survey;; 

were presented in the corresponding national Consumption and Expenditures reports, data user:; 

are cautioned with respect to interpretation of the coefficients that are associated with the 

independent variables.

As with any large regression, care should be taken in interpreting the individual coefficients in 

the equations. The variables that are used in the equations may be highly correlated. wit.Ii

Energy Information Administration / Energy Consumption Series 

114 Residential Energy Consumption Survey Quality Profile
variables, which are not used in the equations. Thus the value of the coefficients will reflect both 

the impact of the included variables and the impact of any correlated excluded variables. For 

instance, the natural gas equations did not contain variables that used the type and R-value of 

insulation directly, but the impact of the type and amount of insulation is included through 

variables which indicate the presence of insulation (EIA 1993a, p. 198),

End-Use Estimation for the 1993 RECS

The 1993 end-use estimation model consisted of five nonlinear regression equations: one for 

each of the major energy sources. In each equation, the dependent variable was the total 

consumption for that fuel for the survey reference year. The equation expressed total 

consumption for the fuel as the sum of three or more components, corresponding to the end uses 

for which separate estimates were to be obtained, plus an error term. Each of these components 

was expressed, in turn, as a complex nonlinear function of household variables available from 

RECS.

For each fuel, the estimation equation included a space-heating and water-heating component. 

For fuel oil, LPG, and kerosene, there was one additional component, called "appliances," 

covering all other uses of that fuel. For natural gas, there was an additional component for airconditioners and a residual category for appliances. For electricity, there were additional 

components for air-conditioners, refrigerators, freezers, lighting, cooking, dishwashers, clothes 

dryers, and all other appliances.

To illustrate the structure of the nonlinear regression model and its components, Figure 7.2 shows 

the basic equation used in the 1993 RECS for electricity and the formulation for one of its 

components, the one covering electricity consumption for freezers. The units of measure in 

Figure 7.2 are thousands of Btu's. Although there are many variations in the variables by 

component and fuel, the basic structure of all components is similar. Typically, a component, 

such as the electricity freezer component shown in Figure 7.2, consists of a base term and one 

or more multiplicative adjustment terms. The base term models the energy consumption for a 

"standard" situation.

For the freezer component, the base term (FZBASE x CDDBASE) is a function of the number 

of freezers and cooling degree-days. There are four adjustment terms. The first two adjustment 

terms (MANUADJ and UPRTADJ) are functions of the type .of freezer (manual defrost versus 

frost free and chest versus upright). The third adjustment term (AGEADJ) is a function of the 

age of the freezer. For the few households with two or more freezers, the adjustment terms are 

determined by the type and age of the largest freezer. (In effect, the model assumes any 

additional freezers are of the same type and age as the largest freezer. To save interviewing time, 

only the type and age of the largest freezer was recorded.)

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 115
Figure 7.2. Selected Components of the Nonlinear End-Use Consumption Model for Eleci:rid! 

Used in the 1993 RECS

Regression equation for electricity:

Y Y + y 4- y 4-Y 4- Y 4-Y +Y 4. Y 4-Y I.Y 4.0 EL ~ "SH ^WH ^'AC ARFG AFZ "LOT ^CK ADW /VCD " AOTAPL "

where YEL = actual annual consumption of electricity

XSH> XWHI XAC. XRFG , '^FZ> XLGT , ^cKi ^Dw> ^cD> ^^ XOTAPL =" ^ end-use components for spacii! 

heating, water heating, air conditioning, refrigerator freezer, lighting, cooking, dishwashing, 

clothes dryer, and all other appliances, respectively

and e = Y1M EL - Y1M EL

with YEL being the estimated annual consumption of electricity, 

Details for the Electricity Freezer Component: 1993 RECS

XF2 = FZBASE x CDDBASE x MANUADJ x UPRTADJ x AGEEADJ x TOTADJ

where TOTADJ is an adjustment factor applied to all electricity components, based upon the price 

of electricity, demographic characteristics of the household, geographic location, and type ;::- : 

housing unit

and FZBASE :: 2345 x (Number of freezers)

CDDBASE = 1 + (0.0170 x (CDD65) 1 '2)

MANUADJ = 1 - (0.2019 x MANUFZ)

UPRTADJ = 1 + (0.1123 x UPRTFZ)

AGEADJ = 1 + (0.2718 x FZ20PLUS) - (0.3203 x FZ4MNUS)

and CDD65 :: cooling degree-days to the base 65 degrees Fahrenheit

MANUFZ = 1 if largest freezer is a manual defrost freezer and 0 otherwise 

UPRTFZ =- 1 if largest freezer is an upright freezer and 0 otherwise 

FZ20PLUS =: 1 if largest freezer is 20 years old or more and 0 otherwise 

= 1 if largest freezer is 4 years old or iess and 0 otherwise.

The last adjustment term (TOTADJ) is used for all components in the electricity model. If 

includes variables that should have an effect on all electricity components. Examples of this a;i:;: 

the price of electricity, the family income level, and other demographic characteristics of th: 

household. The model assumes that the effect of variables used in TOTADJ is the same for a.'.! 

components. For instance, high income may imply bigger homes with bigger freezers, biggc::t 

appliances of other kinds, and more appliances. Thus, high income may be associated wr;b 

higher electricity consumption for all end-uses.

Using a nonlinear formulation of the model, the freezer component requires the estimation of on y 

six coefficients to model the effect of climate (number of cooling degree-days), type of freezer, 

and age of freezer on the electricity consumption of freezers. The model assumes that facto::. 

interact proportionally. For example, the effect of the age of a freezer on its electricity 

consumption is proportionally the same for all freezer types and for all climates. The resulting

Energy Information Administration / Energy Consumption Series 

11Q Residential Energy Consumption Survey Quality Profile
equation projects that, for freezers of the same type located in the same climate, the newest 

freezers use 32 percent less electricity than freezers in the next age group, while the oldest 

freezers use 27 percent more.

The model-fitting procedure was designed to minimize the sum of the squared error term over 

all households included in the analysis for each fuel, For the 1990 and 1993 REGS, as shown 

in Figure 7.2, the error term was equal to the difference between the fourth root of the actual 

consumption and the fourth root of the estimated consumption. The error term defined in this 

way was found to be more nearly normally distributed with a constant variance than alternative 

formulations of the error term, such as the simple difference or the difference between logarithms 

or square roots of the actual and estimated consumption.

Because the regression equations are nonlinear, the parameter values cannot be estimated with 

standard multivariate linear regression techniques. They were estimated by using a nonlinear 

regression procedure in the statistical computer package, SAS. 1

As noted in the introduction to this section, the regression analysis for each fuel was based on 

a subset of the sample households using that fuel. Households were excluded from the analysis 

for many reasons, the principal ones being: they did not pay the supplier directly for all uses of 

the fuel (so that usable billing data were not available); other problems with the consumption 

data, such as data covering only part of a year or inclusion of nonresidential uses in the billing 

data; or imputed values of key independent variables, such as occurred for many variables when 

the Household Survey data were obtained by mail.

Table 7.1 shows the number and percents of sample households used in the regression analysis 

for each fuel type in the 1990 RECS. Of the households using each fuel, the proportion included 

in the analysis varied from slightly less than one-half for fuel oil to about two-thirds for 

electricity. A draft Technical Note on the 1990 regression analysis (Harrison 1993) provides 

additional detail on the data sets included in the analyses by housing unit type and major end-use 

category. That report identifies several fuel/end-use categories for which the number of sample 

households used was small, for example, natural gas air-conditioning (only eight observations 

were available), use of all fuels as secondary fuels for water heating, and use of fuel oil or 

kerosene for any purpose other than heating. Although separate models were not developed for 

each housing type (adjustment terms were developed to model differences by housing type), the 

report also notes that "Because there were more observations for households living in singlefamily detached homes, the regression analysis should give the best estimates for [these 

households]."

This relatively brief description has covered the highlights of the complex nonlinear regression 

models used in the 1993 RECS to allocate consumption of each fuel to end-use categories and 

to impute total consumption of the fuel when necessary. Substantial additional detail, including 

the equations used for each fuel and end use, is provided in: Appendix D, "End-Use Estimation 

Methodology," of the Consumption and Expenditures 1990 report (EIA 1993a); Appendix C, 

"End-Use Estimation and Methodology," of the Consumption and Expenditures 1993 report (EIA 

1995d); and in the Technical Note cited above (Harrison 1993).

Statistical Analysis System (SAS) (Gary, NC).

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 117
Table 7.1, Number and Percent of Sample Households Used in Regression Analyses, by Fuel Typs 

1990 RECS

Category

Households Using Fuel 

Used in Analysis 

Not Used, by Reason3

Didn't Pay for Some 

Used Directly

Quality of

Consumption

Data Not Acceptable

Key Independent

Variables

Imputed

Other0

Fuel Type

Nat.

No.

3,255

1,917

Gas

Pet.

Electricity

No.

100.0 5,094

58.9 3,392

Pet.

Fuel Oil"

No. Pet.

LPG

No.

100.0 700 100.0 461

66.6 336 48.1 257

Pet.

Kerc

No.

100.0 278

55.7 163

Pet

:CO.C

612

506

220

NA

356

15.5 1,028

6.8

NA

318

NA

7.0

20.2

6.2

NA

145

50

20.7

23.0

7.1

1.1

26

152

22

5.6

33.0

4.8

0.9

81

15

18

"Each household not used is counted under the first applicable reason.

''Data for Model A only, see Source, p. 45.

 Did not purchase or, for kerosene only, did not use for space heating.

NA = Not Applicable.

Source: Latta, Poststratification Estimation (May 1993).

Changes in End-use Estimation Methodology: 1978-1993

The RECS energy consumption end-use estimation procedures and models have never beer. 

precisely the same from one survey to the next. Changes occur for several reasons. First, "here 

have been many changes in the content of the Household Survey questionnaires and hence h the 

data items available for use as independent variables in the regression analyses. Many of the 

questionnaire changes have been motivated by the desire to collect new information that coui.c 

be used either to reduce the size of the error term in the basic equations or to introduce new enduse categories for which separate estimates could be made. In the 1990 RECS, the appliance 

category for electricity was subdivided into refrigerators, freezers, and other appliances.

In the 1993 RECS, the addition of new questions on lighting and electric appliances made h 

possible to further subdivide the appliance category for electricity to provide separate end-uii';: 

estimates for lighting, cooking, clothes dryers, and dishwashers.,

Second, even if the questionnaire content and the model specification were to remain unchanged 

from one survey to the next, the estimates of the model parameters would change, due in par; :o 

sampling variability of the estimates and in part to real changes; in the underlying relationships 

of the independent variables. It is also possible that some parameter estimates that met the basic 

test for significance in one survey year might not qualify in e. subsequent survey year. However,

118

Energy Information Administration / Energy Consumption Series 

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the initial significance test criterion has been loosened somewhat, in order to improve 

comparability of end-use estimates over time and to avoid eliminating variables that appear to 

have intrinsic validity as part of the model.

Third, there have been some basic changes in the structure of the model used for end-use 

allocation of energy consumption, A major change occurred in the 1984 RECS when the linear 

model used in prior surveys, for which parameter values could be estimated by standard 

multivariate least squares regression, was replaced by a nonlinear model requiring a different 

estimation method. The decision to adopt a nonlinear model was reached after extensive 

experimentation with and evaluation of both linear and nonlinear models and associated 

procedures to estimate end-use consumption by fuel, using data from the initial survey years (see 

following subsection on "Evaluation of End-use Estimation Procedures").

There were three major reasons for the change to a nonlinear model: (1) the ability to formulate 

a more realistic model; (2) the ability to formulate the model in a way that avoids negative 

estimates for households that have a combination of factors all pointing to lower energy 

consumption; and (3) the ability to formulate the error term in a way that results in its 

distribution being approximately normal, with a constant variance.

To understand these advantages, it may be useful to consider a possible linear formulation of one 

component of the nonlinear model now in use. Figure 7.3 shows a linear formulation of the 

model for electricity and its freezer component. Like the nonlinear formulation, the linear one 

uses actual annual consumption of electricity as the dependent variable. The error term for the 

linear model, following the usual practice, is the difference between actual and estimated annual 

electricity consumption. The coefficients for the terms of the freezer component a,, a2 , a3 , a4 , 

a5 , and a6 would be estimated using linear regression. Knowledge of the characteristics of 

freezers gives the expectation that a,, a2, a4 , and a5 would be positive and that a3 and a6 would 

be negative.

The model in Figure 7.3 does not include interaction terms for the climate, type, and age of the 

freezers. Interaction terms could be added. The model does not include terms for the effects of 

the price of electricity or the family income on the freezer component. (Lower electricity prices 

and/or higher income could be associated with larger freezers.) Again, interaction terms could 

be added.

The use of many interaction terms in the linear model may result in a formulation that looks 

more realistic, but the actual estimated coefficients may result in unrealistic estimates for some 

combinations of type of freezer, age of freezer, climate, income level, and electricity price. Some 

estimates could even be negative. The use of a nonlinear model allows the formulation of a more 

realistic model using far fewer terms than would be needed with a linear model. The use of 

fewer terms in the model reduces the possibility of unrealistic estimates for some combinations.

Analysis of the residual terms from the linear model previously used shows that the error terms 

were not normally distributed with constant variance. In fact, the error terms were skewed in the 

positive direction and the variance of the error terms increased as the projected energy 

consumption increased. The use of weights can alleviate the effect of trends in the variance of 

error term with either linear or nonlinear regression. However, weights alone do not alleviate the 

effect of the skewness of the error terms.

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 119
Figure 7.3. An Alternative Linear Formulation of the Model Components Shown in Figure 7.2

Regression equation for electricity:

YEL = XSH + XWH + XAC H- XRFG + XFZ + XLGT + XCK + XDW + XCD + XOTAPL + e 

where YEL = actual annual consumption of electricity

XSH> XWH , XAC , XRFG , XFZ, XLGT , XCK, Xow, Xco , and XOTAP[ are end-use components for spa:;*: 

heating, water heating, air-conditioning, refrigerator, freezer, lighting, coo.<fr-.;i, 

dishwashing, clothes dryer, and all other appliances^ respectively

and e = YEL - ^EL

with ^EL being the estimated annual consumption of electricity. 

Details for the Electricity Freezer Component: 1993 REGS

XFZ = a, x (Number of freezers)

+ a2 x {Number of freezers) x (CDD65) 1 '2 

+ a3 x (Number of freezers) x MANUFZ 

+ a4 x (Number of freezers) x UPRTFZ 

+ a5 x (Number of freezers) x FZ20PLUS 

+ a6 x (Number of freezers) x FZ4MNUS

where CDD65 = cooling degree-days to the base 65 degrees Fahrenheit

MANUFZ = 1 if largest freezer is a manual defrost freezer and 0 otherwise

UPRTFZ = 1 if largest freezer is an upright freezer and 0 otherwise

FZ20PLUS = 1 if largest freezer is 20 years old or more and 0 otherwise

FZ4MNUS = 1 if largest freezer is 4 years old or less and 0 otherwise.

The error term adopted for the nonlinear model introduced in 1984 was the difference betweei i. 

the logarithms of actual and estimated consumption. This error term was closer to being 

normally distributed, but its variance was still not constant for all energy and household Type:-,, 

This problem was dealt with by using a weighted regression method in which households in 

categories with high error variances were given lower weights. For example, for natural ga:: ,;i 

weight of 1.0 was given to most households, but a weight of 0.2 was assigned to household-; 

using natural gas which:

1. Did not use it as a main space-heating or water-heating fuel.

2. Did not use it as a main water-heating fuel, did use it as a main space-heating fu-::l, 

and the main equipment was a natural gas floor furnace, wall furnace, pipeks; 

furnace, or room heater.

In the 1990 RECS, the logarithmic error term used in 1984 and 1987 was replaced by the ci-r: 

shown in Figure 7.2 that is, the difference between the fourth roots of actual and estimate 

consumption of each fuel. Investigation of four alternative error terms linear, logarithmic, squar; 

root, and fourth root had shown that the last of these came closest to meeting the ba:-;ji

Energy Information Administration / Energy Consumption Series 

120 Residential Energy Consumption Survey Quality Profile
requirements for normality and constant variance. With the introduction of the new error term, 

the weighted regression procedures used in 1984 and 1987 were no longer necessary.

There have also been some conceptual changes involving the definition of certain end-use 

components of the model. In the 1984 RECS, electricity used to run fans for central forced-air 

heating systems was assigned to the space-heating component for electricity. In subsequent 

survey years, electricity used for this purpose was assigned to the appliance component rather 

than the space-heating component. This change was made so that households which did not use 

electricity for space heating would not have any consumption of electricity assigned to the spaceheating component. A similar change was made for electricity used to operate whole-house fans, 

ceiling fans, window fans, and evaporative (swamp) coolers. In 1984, electricity used for these 

purposes was included in the air-conditioning component: since 1987, it has been included in 

the appliance component.

Evaluation of End-Use Estimation Procedures

In recent years, new technologies have made it possible to measure the consumption of electricity 

for individual appliances and other uses within the home, a process often referred to as 

submetering (Windell 1986). Conceivably, similar technologies could also make it possible to 

measure amounts of natural gas used for space heating, hot water heating, cooking, and other 

uses. An ideal means of evaluating the RECS end-use estimation models for electricity and 

natural gas would be to measure end-use consumption of these fuels in a subset of RECS sample 

households and compare these direct measurements with estimates generated by the nonlinear 

regression models for the same households. However, there are two obstacles to such a project: 

the high cost per household of installing the monitoring equipment and the difficulty in enlisting 

an acceptably high proportion of households in a national probability sample to agree to 

participate in such a study. Consequently, efforts to evaluate the RECS end-use estimation 

procedures have so far relied on less direct methods. Three studies that have been undertaken 

are described in this subsection.

As noted earlier in this section, end-use allocation for the first five survey years, 1978 through 

1982, was based on a linear model, with the nonlinear model being introduced in the 1984 

survey. The independent variables included in the linear model varied during the five survey 

years for which it was used, as new items were added to the Household Survey questionnaire.

Following the 1984 RECS, an exploratory study was undertaken to examine the effects on the 

end-use estimates of using different models (Carroll 1987). The study also looked at the effects 

of the models on estimates of total consumption for each fuel, because the same models were 

being used to impute total consumption for households for fuels for which direct data were not 

available. For the survey years 1978, 1980, 1981, 1982, and 1984 (the 1979 survey was not 

included in the study), estimates of total consumption and consumption by end use were 

developed by using two different nonlinear models: one (called the NIECS-based model) using 

only those variables, which were available from all five surveys, and the other (called the 1984 

RECS-based model) using all of the variables available from the 1984 RECS, with substitution 

of proxy variables for those for which data were not collected in earlier surveys. For example,

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 121
the study report states that "Income dummy variables proved to be effective proxies for tin;: 

[unavailable air-conditioning] use data in the 1978 and 1980 surveys."

The only results available from this study are from a preliminary report which does not includ;: 

all of the basic tabulations, Some of the author's conclusions were as follows:

 Compared to the linear models, the nonlinear models consistently allocated mor>: 

consumption to space cooling and less to appliances.

 The overall predictive power of the NIECS-based nonlinear model (which included 

fewer independent variables) was slightly lower than that of the 1984 RECS-basec 

nonlinear model, accounting for 10 percent less of the total variance. However, then: 

was relatively little difference in mean consumption amounts estimated by the twc 

models.

 Differences between the end-use allocations estimated by the NIECS-based and REC3- 

based nonlinear models were small, except for space cooling.

The other two studies were about end-use allocation of residential consumption of electricity, and 

both of them made use of residential submetering data collected by electric utilities. Battles 

(1990) reports on a comparison of nonlinear model-based estimates of electricity consumption by 

end use from the 1987 RECS with estimates based on submetering data collected for various 

studies by eight electric utilities. The comparisons covered four end uses of electricity: space: 

heating, room air-conditioning, central air-conditioning, and water heating. The method of 

comparison for each of the eight utilities was to select a subset of RECS sample households frcrr 

the same Census division that matched as closely as possible on known characteristics of the: 

households for which the utility had obtained submetering data. All households in the study were: 

in single family housing units. Other characteristics taken into account for all or some of the 

utilities were heating and cooling degree-days, tenure, floor area, and .the use of certain 

appliances. The RECS end-use estimates for this subset of sample households were then 

compared with the corresponding estimates for the households that had been submetered by the 

utility, taking into account the sampling errors associated with the RECS estimates.

Given the large sampling errors associated with the RECS estimates and the fact that the. 

households studied by the utilities did not constitute a probability sample of the same population, 

the results of the comparisons can only be roughly indicative of possible biases in the RECS 

model-based estimates. Battles concluded that the RECS model-based estimates were "reasonable 

estimates" when compared to the utilities' submetered estimates. However, she stated that:

This study does, though, reveal some areas where further investigation may be 

warranted. All of the submetered estimates for both air-conditioning and space 

heating are lower than the RECS CD A [conditional demand analysis] comparative 

estimates and all water-heating submetered estimates are higher than the RECS 

CDA comparative water-heating estimates. The consistency in differences is 

important. (Battles 1990, p. 12)

Energy Information Administration / Energy Consumption Series 

122 Residential Energy Consumption Survey Quality Profile
The other study that made use of submetered data on consumption of electricity (Response 

Analysis Corporation 1992c,d) compared the utility data with RECS estimates of end-use 

consumption based on the model used in the 1990 RECS. As explained earlier in this section, 

the 1990 RECS was the first to use an error term based on the fourth roots of estimated and 

actual consumption, as opposed to the logarithmic error term used in 1984 and 1987. The study 

used submetering data and information on household and demographic characteristics that had 

been obtained for samples of households by five utilities. Only those households for which both 

kinds of information were complete were included in the study: sample sizes by utility varied 

from 13 for the City of Austin to 182 for Pacific Gas and Electric. All of the utilities provided 

end-use load data on water heating and all provided data on one or more of the following: central 

air-conditioning, room air-conditioning, space heating, total HVAC (heating, ventilation and airconditioning), refrigerators, and total appliances.

Because the utilities did not collect all of the household and demographic information that is 

available for RECS sample households, a separate modified end-use estimation model was 

developed for each of the five utilities, making use only of the variables that were available for 

that utility. The submetered end-use data for that utility were then compared with three sets of 

estimates:

1. Estimates for the utility's sample households based on the modified RECS model

2. Estimates for a selected set of RECS sample households, similar in their 

characteristics to the utility's sample households, based on the modified RECS 

model

3. Estimates for the same set of RECS sample households, based on the full RECS 

model.

Assuming that estimates based on the modified RECS model do not differ significantly from 

those based on the full model, the comparison of the submetered data with set 1 provides the best 

indicator of how well the statistical end-use model allocates consumption. In some instances it 

appeared that the assumption was not valid, so adjustments were made to the estimates in set 1 

on the basis of the relationship between sets 2 and 3.

Table 7.2 shows the percentage differences between the submetered end-use data and the adjusted 

model-based estimates for the same households (set 1). The findings for four of the five utilities 

were consistent with the indications from the previous study that the RECS end-use estimation 

model might be overestimating consumption for central and room air-conditioning and space 

heating and underestimating consumption for water heating. Findings for the Bonneville Power 

Administration were in the opposite direction. The authors of the report suggest that the RECS 

end-use models might be improved by developing a separate model for each region, on the 

grounds that the factors that determine space-heating and air-conditioning consumption in 

different climates may be quite different.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 123
Table 7.2. Percentage Difference* Between Modeled and Submetered End-Use Estimates

End-Use Estimate

Central Air-Conditioning

Room Air-Conditioning

Space Heating

HVAC

Water Heating

Refrigerators

Appliances

Austin BPA" F

40% 

NA 

54% 

84%

-25% 

NA

-27%

NA 

NA

-10%

-22% 

4%

-23% 

5%

Utility

PGEC

370% 

17% 

NA 

NA 

-23% 

0% 

NA

Santee 

Cooper SCE

13% 

NA 

33% 

27%

-8% 

NA

-1%

33% 

69% 

NA 

NA 

NA 

18% 

NA

Tercent difference = (Modeled estimate - Submetered estimate) x 100

Submetered estimate. 

bBonneville Power Administration. 

'Pacific Gas and Electric. 

^Southern California Edison. 

NA = Not Available. 

Source: Response Analysis Corporation (1993).

Sampling Errors

The sampling error of each published statistic is estimated by using the balanced half-sample 

replication method. The estimated sampling errors are used to check the validity of statement:: 

made in the text of survey reports and as the basis for suppressing estimates whose relative 

standard errors are 50 percent or more. Due to space limitations, the estimated sampling error:; 

for the individual table cells are not published, but the estimates provide the basis for tin;: 

derivation of generalized variance functions, which are published and permit users to compute 

an approximate relative standard error for each published estimate.

This section describes the procedures used for the estimation and publication of sampling errors 

in the 1993 RECS, followed by information about changes in the methodology used in earlier 

surveys. The section concludes with a discussion of the accuracy of sampling error estimates ant: 

the extent to which RECS has achieved its goals for the precision of key estimates of energy 

consumption. Readers who would like additional detail about the derivation and use of sampling 

errors in RECS may refer to the introductions and appendices of the Housing Characteristics anc 

the Consumption and Expenditures reports for each survey year for example, pp. 18-20 and 231 

236 in the Housing Characteristics report for 1993 (ElA 1995a).

Estimation of Sampling Errors for the 1993 RECS

The half-sample replications were formed from 78 "super strata," each containing pairs of sample; 

households:

124

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
 Thirty-eight of the super strata consisted of pairs of non-self-representing strata from the 

same Census Division. Strata from the four most populous States (California, New York, 

Texas, and Florida), which had been formed so as not to cross State lines, were always 

paired with other strata from the same State. Within Census Divisions and also within 

the four most populous States, strata for Metropolitan Statistical Areas (MSA's) were 

paired with other MS A strata and non-MSA strata were paired with other non-MS A strata. 

The pairs in each of the 31 super strata consisted of the sample households in the primary 

sampling units selected from each of the two strata,

 Thirty-one of the super strata consisted of large metropolitan areas that had been selected 

with certainty. The pairs consisted of sample households in two sets of the secondary 

sampling units that had been selected from the metropolitan area.

 The nine remaining super strata each consisted of a single non-self-representing stratum. 

The pairs consisted of sample households in two sets of the secondary sampling units that 

had been selected from the sample PSU in that stratum. These non-self-representing strata 

were not combined with other non-self-representing strata because of restrictions on 

combining strata with differing attributes, for example, strata in different Census divisions.

Ninety-six half samples were formed from the 78 super strata by selecting, in each instance, one 

of the pairs of sample households from each super stratum. The selection was balanced that is, 

it was carried out in such a way that each pair member from a super stratum was included in 48 

of the 96 half samples. To produce sample estimates from each of the 96 half samples, the 

sampling weights were ratio-adjusted upwards so that the sum of the weights was equal to the 

control totals (housing-unit counts derived from the Current Population Survey) for each of the 

nine Census divisions and four States California, New York, Texas, and Florida.

The estimated variance for each sample estimate was the mean squared deviation of the 96 halfsample estimates from the full sample estimate. Because the ratio adjustments to control counts 

were applied to each half sample, the estimated housing-unit counts for the nine Census divisions 

have zero variance. For estimates of housing-unit counts that are close to the control counts, such 

as the number of housing units using electricity or the number with refrigerators, the sampling 

errors are very small.

Generalized Variances

Showing the estimated sampling error for each published statistic would roughly double the space 

required for publication of tabulations. As an alternative, generalized variance functions, which 

permit users to determine an approximate value of the relative standard error (RSE) for each table 

cell, are included in the publications. Figure 7.4 shows an example of a 1990 RECS publication 

table with "row and column factors" which can be used as shown in the example to determine 

the relative and absolute standard errors and a confidence interval for any cell in the table.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 125
Figure 7.4. Example of the Use of RSE Row and Column Factors to Derive ApproximHife 

Standard Errors

Characteristics

RSE Column Factors

Major Energy

Sources

0.9

Total U.S. Households 12.0

Urban Status

Urban 11.9

Electricity

0.8

23.6

24.6

Natural

Gas

0.7

Fuel

Oil

0.7

Kerosene

1.5

Liquified

Petroleum

Gas

1.9

RSE Row

Factors;

5.6 7.8 9.4 11.2 1.3

5.7 7.8 9.5 11.4 1.7

Central City 11.2 24.3 5.8 7.3 9.8 14.3 3.1

Suburban 12.4 24.8 5.6 8.0 9.2 11.2 1.3

Rural 12.2 20.8 5.2 7.8 9.4 11.0 2.6

Climate Zone

Under 2,000 CDD and

Over 7,000 HDD 10.2 21.6 5.0 7.7

5,500 to 7,000 HDD 10.1 24.6 5.3 7.9

4,000 to 5,499 HDD 12.0 23.5 6.3 7,8

Under 4,000 HDD 13.8 24.3 5.8 8.0

2,000 CDD or More and

Under 4, 000 HDD 15.9 23.0 5.9 Q

Type of Housing Unit

Single-Family 11.9 23.3 5.5 8.0

Detached 11.8 23.2 5.4 8.0

Attached 12.9 24.5 6.1 8.1

Mobile Home 13.0 21.6 5.3 8.2

Multifamily 11.9 25.6 6.1 7.2

2 to 4 Units 10.7 26.5 6.1 8.1

5 or More Units 13.4 24.9 6.0 5.9

9.5 10.1 3.5

9.2 11.2 2.3

9.9 11.2 3.6

9.0 11.5 2.5

10.3 12.9 5.2

9.5 11.0 1 .5

9.5 11.0 1.(>

9.8 Q 3.(i

9.4 11.7 2.<)

8.5 13.5 3.fi

Q 13.1 4.3

Q Q 3.:i

Row Factor (Urban) = 1.7

Column Factor (Electricity) = 0.8

Approximate RSE (Average Electricity Expenditure in the

Urban Area) = (1 .7) * (0.8) = 1 .36 percent.

Approximate Standard Error (Average Electricity

Expenditure in the Urban Area) = (.0136) * (24.6) =

Dollars per Million Btu.

0.33

Approximate 2 Standard Errors (95 percent confidence

interval) = (1.96) * (0.33) = 0.6 Dollars per Million Btu.

Therefore, with 95 percent confidence, the average

electricity expenditure in the Urban area is between

and 25.2 Dollars per Million Btu (24.6 0.6).

24.0

Source: Energy Information Administration, Office of Energy Markets and End Use, 1990 Residential Energy Consumption Survey,

126

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
The publication also explains how to use the RSE's for the appropriate table cells to determine 

the RSE's for percentages based on household counts and for the ratios and differences of two 

statistics (under the assumption that they are independent). The row and column factors for each 

publication table are derived by using the estimated RSE's for the table cells to estimate the 

parameters of a log-linear model,

log(RSEy) = m + a, + bj.

The row factor for the ith row is the geometric mean of the RSE's in that row and the column 

factor for the jth column is an adjustment factor with geometric mean equal to one. Special 

procedures are used for cells with very large or very small RSE's or missing values.

The row and column factors are derived separately for each publication table. Consequently, an 

estimate that appears in more than one table may have different RSE's arrived at by using 

different sets of row and column factors. Any of these values should provide a useful 

approximation to the relative standard error for that item as estimated by the replication method 

(EIA 1986b).

Estimates of Change Between Surveys

When comparing statistics between survey years, assuming independence of the estimates from 

different surveys will, in most instances, lead to an overestimate of the sampling error of a 

difference or ratio of estimates of the same variable for different years. This occurs because the 

samples for different years are not in fact fully independent. For most survey years, the sample 

PSU's have been the same as those used in the previous survey year, and even in those years 

when new samples of PSU's were selected, the selection procedure was designed to maximize 

the overlap between the old and new samples. In addition, the samples for survey years 1982 

through 1990 included longitudinal components, so that in each of these years approximately onehalf of the sample housing units had also been included in the sample for the preceding survey 

year (except for 1982, when the overlapping units had been included in the 1980 survey). For 

most survey variables, one would expect estimates for different years from these overlapping 

samples to be positively correlated, leading to reductions in the sampling errors of their 

differences or ratios.

Better estimates of the sampling error of change between survey years under these conditions are 

possible with the balanced half-sample replication method, provided that the same sets of half 

samples are used for the two years in question and the differences are estimated from each half 

sample. Sampling errors were estimated by this method and compared with sampling errors 

estimated under the assumption of independence for selected variables and pairs of survey years 

from 1978 through 1984 (EIA 1987c, pp. 217-225). Some of the results are shown in Table 7.3. 

For virtually all of the variables and pairs of years shown, the more precise estimates of sampling 

errors of differences (Method 2) are substantially smaller than sampling errors calculated under 

the assumption of independence (Method 1). The reductions for the 4-year interval, 1980 to 

1984, are less than those for either of the 2-year intervals, 1980 to 1982 and 1982 to 1984. The 

reasons for these smaller reductions are not entirely clear, because the pattern of sample overlap

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 127
for these periods was relatively complicated. However, a reduction in the correlation over tiim;: 

for longer intervals may have been a contributing factor.

Table 7.3. Comparison of Standard Error of Difference Estimated by Two Methods for Changesin Average Consumption per Household Between Survey Years

Years and Fuel

Average Consumption'1

Year 1 Year 2 Difference11

Standard Error

Method 1 C Method 2d

Percent 

Difference8

1980 and 1982

All fuels 

Electricity 

Natural gas 

Fuel oil/kerosene 

LPG

1982 and 1984

All fuels 

Electricity 

Natural gas 

Fuel oil/kerosene 

LPG

1980 and 1984

All fuels 

Electricity 

Natural gas 

Fuel oil/kerosene 

______LPG__________

'Average consumption per household using fuel, in millions of Btu.

b Due to rounding, may not be consistent with values shown in table for Years 1 and 2.

 Assumes estimates for Years 1 and 2 are independent.

dReflects correlation between estimates for Years 1 and 2.

'Percent reduction from using Method 2. Due to rounding, may not be consistent with values shown for Methods 1 & 2.

Source: Energy Information Administration (1987).

114.2

30.1

95.7

100.8

47.6

102.9

28.9

88.1

73.4

39.4

114.2

30.1

95.7

100.8

47.6

102.9

28.9

88.1

73.4

39.4

104.7

28.8

89.9

71.9

40.1

104.7

28.8

89.9

71.9

40.1

-11.2

-1.2

-7.6

-27.0

-8.2

1.7

-0.2

1.8

-1.5

0.7

-9.5

-1.4

-5.8

-28.9

-7.5

2.3

1.0

2.2

3.5

3.4

2.2

0.9

2.1

3.4

3 ^

2.3

0.8

2.2

3.4

3.4

1.2

0.4

1.4

2.0

2.1

1.1

0.5

1.4

2.7

2.6

1.7

0.4

1.8

3.4

3.2

49

59

36

42

38

48

49

32

22

21

25

45

20

1

6

Changes in Methodology

The same basic method of estimating sampling errors, balanced half-sample replication, has beer 

used in all survey years. There have been several changes in estimation procedures and the 

methods of presenting information about sampling errors in RECS publications:

 The number of half samples used has varied. From 1978 through 1982, 32 half 

samples were used, except in 1979, when there were 72. In the 1984, 1987, and 199(1 

surveys, 128 samples were used and, as noted earlier. 96 were used in the 1993 RECS.

 In the 1978 NIECS. the same overall weights were used for each half sample, so that 

the effects of nonresponse adjustments and ratio estimates to control totals were not 

reflected in the estimated sampling errors. In the following year and subsequently, 

separate weights were developed for each half sample.

128

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
The composition of the "super strata" used to form half samples has varied, mainly 

because of changes in the design of the RECS sample. In the first three surveys, all 

non-self-representing strata were paired to form super strata. Subsequently, it was 

decided that certain restrictions should be placed on pairing, such as not pairing strata 

from different Census divisions. Non-self-representing strata that could not be paired 

under these restrictions were treated as separate super strata, with the half samples 

being formed from two sets of secondary sampling units in the sample primary 

sampling unit for each super stratum.

In the 1978, 1979, and 1980 survey reports there were two separate sets of tables, one 

containing the sample estimates and the second containing the estimated sampling 

errors corresponding to many of the estimates shown in the first set. Various methods 

were suggested for estimating sampling errors not shown in the second set of tables. 

Tables of individual sampling errors were dropped from the reports after 1980 and a 

series of procedures, based on various methods of estimating generalized sampling 

errors, was provided in one of the appendices to each report. The Consumption and 

Expenditures report for 1984 introduced the method of showing row and column 

factors in the data tables, and this approach has been followed since then.

Limitations of Sampling Error Estimates

Estimates of sampling error are themselves subject to sampling error. Their sampling error would 

be minimized if all possible half samples were used in the balanced half-sample replication 

estimates, but the cost of doing so would be prohibitive, so a subset is used. The larger the 

subset, the closer the estimated sampling errors will be to the value obtained by use of all 

possible half samples.

For the super strata formed by collapsing non-self-representing strata, sampling errors are 

overestimated because the reduction in sampling error resulting from stratification is not fully 

reflected. On the other hand, sampling errors for the super strata that consist of a single non-selfrepresenting stratum are underestimated, because the estimates do not reflect the between primary 

sampling unit component of the variance for these strata. No data are available on the net effect 

of these biases, which are inherent in the estimation of sampling errors for a sample design that 

selects a single primary sampling unit from each stratum.

Sampling errors for estimates of energy consumption and expenditures by end use are understated, 

because the parameters of the nonlinear end-use allocation model are not estimated separately for 

each half sample. Thus, the estimated sampling errors do not reflect the error of estimation of 

the model parameters. Sampling errors for the 1990 end-use estimates were calculated, but not 

published, for this reason. Sampling errors or row and column factors have been published for 

1984, 1987, and 1993. In the Consumption and Expenditures report for 1993 (EIA 1995d), the 

row and column factors for end-use estimates were footnoted to indicate that they were 

underestimates.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 129
The generalized estimates of sampling errors that have appeared in the published RECS reports 

since 1984 are approximations to the values estimated directly for published data cells. As noted 

above, the direct estimates were presented only for selected items in the 1978, 1979, and 198C 

RECS reports and have not been published in subsequent reports. A detailed analysis of the 

differences between the direct estimates of sampling error and the approximations based on row 

and column factors was undertaken for the 1983 Nonresidential Buildings Energy Consumption 

Survey (EIA 1986b, pp. 200-203). The measure of accuracy chosen for that analysis was the root 

mean square, along a table column, of differences of the base-10 logarithms of the approximate 

and direct estimates of the relative standard errors. For most table columns, these values were 

found to correspond to percentage differences between 20 and 60. The differences from "he 

direct estimates were fairly evenly distributed between positive and negative values.

Sampling Error Targets for Key Estimates

The 1993 RECS sample was designed to produce estimates of average energy expenditures with 

sampling errors no greater than specified target levels: 1.25 percent for the national estimate, 

2.75 percent for estimates by Census region and 4.50 percent for estimates by Census division. 

As shown in Table 7.4, actual sampling errors, whether estimated directly or by using the 

appropriate row and column factors, were all below these target values. Achievement of values. 

that were well below the targets in some instances resulted in part from the supplementation of 

the 1993 core sample with, special samples designed to strengthen sample coverage of newly 

constructed housing units and low income households.

Design Effects

Notwithstanding best efforts to develop a sampling frame consisting of heterogeneous clusters, 

the sampling errors of estimates based on a complex multistage cluster sample like the one used 

in RECS are usually greater than the sampling errors that would have been obtained if a simple 

random sample of the same size had been used. The cluster design is, of course, preferred, 

because it can produce the desired level of reliability at a lower cost than a simple random sample 

can.

A recent analysis of the RECS sample design produced estimates of the optimum number of 

primary sampling units (PSU's), secondary sampling units per primary sampling unit, and 

households per secondary sampling unit for several categories of RECS variables (EIA 1994), 

Table 7.5 shows the design effects, expressed as the ratio of the variance or standard deviation 

to the variance or standard deviation for a simple random sample of the same size, for the 

optimum size clusters and for those actually used in the 1993 RECS. Number of PSU's and 

cluster sizes for the optimum designs were based on core samples of 5,095 housing units. As the 

table shows, the design effects on the standard deviation ranged from 1.21 to 1.26 for the designs 

using optimum cluster sizes and from 1.39 to 1.60 for the actual 1993 sample design. Design 

effects for estimates of consumption and expenditures were somewhat higher than those for other 

types of variables.

Energy Information Administration / Energy Consumption Series 

130 Residential Energy Consumption Survey Qualify Profile
Table 7.4. Sampling Errors for Estimates of Average Consumption per Household: 1993 RECS

Ares

Relative Standard Error (Percent)

Target Approximation3

Direct 

Estimate

United States......................................................................... 1.25 1.04 1.08

Census Region

Northeast................................................................... 2.75

Midwest..................................................................... 2.75

South......................................................................... 2.75

West.......................................................................... 2.75

1.92

1.84

2.00

2.08

2.61

2.24

2.10

2.19

Census Division 

New England....................................

Mlddie Atlantic..................................

East North Centrai............. ...............

West North Centrai,..,.,...............,.,.,.

South Atlantic...................................

East South Central...........................

West South Central...........................

Mountain...........................................

Pacific...............................................

......................... 4.50

......................... 4.50

......................... 4.50

......................... 4.50

......................... 4.50

......................... 4.50

........................ 4.50

......................... 4.50

......................... 4.50

3.28

2.32

2.00

3.68

2.80

3.44

3.68

3.44

2.64

4.23

3.14

3.04

2.88

3.16

3.83

4.17

3.89

2.72

"Approximate values based on row and column factors.

Sources: Energy Information Administration, Consumption and Expenditures (1993a); Energy Information Administration (1994).

Table 7.5. Design Effects for RECS, Using Optimum and Actual Cluster Sizes

Sample Design and Type of Variable

Number of 

PSU's 

m

SSU's per

PSD 

n

Housing Units 

per SSU' 

q

Design Effect on

Variance*" Standard Error

Optimum Design

Consumption and Expenditures 201.2 16.83 

Housing Unit Characteristics 152.8 23.10 

Appliances 92.9 30.60 

Demographic 94.6 31.14

1.50

1.44

1.79

1.73

1.586

1.472

1.492

1.480

1.259

1.213

1.221

1.217

Actual 1993 Design

Consumption and Expenditures

Housing Unit Characteristics

Appliances

Demographic

116

116

116

116

13.88

13,88

13.88

13.88

3.06

3.06

3.06

3.06

2.558

2.337

1.937

1.974

1.599

1.529

1.392

1.405

'For actual 1993 design, includes only the base sample. 

bDEF = h, fl q + 1 + hz (q -1) where h, = within PSU measure of homogeneity.

h2 = within SSU measure of homogeneity. 

Source: Energy Information Administration (1994).

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 131
8. Comparisons of RECS Estimates with Other Data

The previous chapters have presented information about sampling errors and various sources of 

nonsampling errors associated with RECS estimates. Whenever possible, quantitative information 

about nonsampling errors has been included, based on operating statistics, pretests, 

methodological experiments, and special evaluation studies in which the accuracy of individual 

responses has been evaluated by reinterviews or other means. The subject of this chapter is a less 

direct but nevertheless useful source of information about the quality of RECS estimates: 

comparisons of aggregate estimates from RECS with data from other sources believed to be at 

least roughly comparable with regard to population coverage and definition of variables.

Typically, comparisons of aggregate data from different sources proceed as follows:

 The analyst looks for differences in design that may cause the estimates to differ. 

These might include different definitions of the target population, different reference 

dates or periods, and different definitions of the variables to be estimated. If there are 

reasonable grounds for doing so, the analyst may adjust one or both of the estimates 

to make them more nearly comparable with each other.

 If one or both estimates are based on probability samples, the analyst develops 

confidence intervals for differences between the (adjusted) estimates from the two 

sources.

 If (adjusted) estimates are significantly different, the analyst will look for additional 

factors that may explain the differences.

When significant differences are observed, it is sometimes not readily apparent which of the 

estimates is more accurate. Nevertheless, such comparisons are often valuable. In some 

instances, such comparisons have suggested ways of strengthening the RECS survey design and 

procedures. Results of the comparisons are presented in RECS publications in the belief that they 

will help users to understand the strengths and limitations of the survey data and thus to use them 

more effectively.

Two kinds of comparisons will be discussed. The next section is about comparisons between 

RECS estimates of end-use consumption and estimates from surveys of fuel suppliers, mostly 

conducted by El A, of amounts of energy supplied to the residential sector. The following section 

covers comparisons of RECS data on housing unit and household characteristics with data from 

the decennial census and from other household surveys, such as the Current Population Survey, 

the American Housing Survey and the Consumer Expenditure Survey. Comparisons between 

RECS estimates and administrative counts of program participants are also presented.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 133
Comparisons of RECS and Supplier Survey 

Estimates of Consumption

The collection of data from energy suppliers is an important component of RECS. However, the 

RECS Supplier Survey collects billing data only for households that are in the RECS sample. 

In addition to conducting surveys of end-use consumption in the residential, commercial, and. 

manufacturing sectors, El A conducts several surveys of energy suppliers who provide various 

types of fuels for consumption by these and other sectors of the economy. In EIA's supplier 

surveys, respondents are asked to provide data on total amounts of fuel supplied to all customers 

during specified time periods and, to the extent possible, to disaggregate these amounts by class 

of customer.

There have been several studies comparing estimates of consumption by fuel type from ElA'! s 

end-use consumption surveys with supplier survey estimates of amounts supplied to the residential 

and commercial sectors. Our focus here will be on the comparisons for the residential sector, 

Results of these comparison studies have been published in several special reports (EIA 1986a, 

EIA 1990, Miller 1995, Allied Technology Group 1995). For the 1990 and 1993 RECS, 

comparisons of RECS and EIA supplier survey data have been published in an appendix to the 

Consumption and Expenditures report (EIA 1993a, Appendix C. EIA 1995d, Appendix D).

Three EIA supplier surveys have been the primary basis for the comparisons:

 The Annual Electric Utility Report, Form EIA-861 (prior to 1984, Form EIA-826 -was 

used)

 The Annual Report of Natural and Supplemental Gas Supply and Disposition, Form 

EIA-176

 The Annual Fuel Oil and Kerosene Sales Report, Form EIA-821 

The first two of these annual surveys cover all known suppliers; the third is based on a sample.

Differences in Defining the Residential Sector

Each of the three supplier surveys asks respondents to report separate estimates for several sectors 

or classes of customers, one of which is the residential sector. However, the supplier survey 

definitions of the residential sector differ, both conceptually and operationally, from the one thai 

is used in RECS. The electric utilities reporting on Form EIA-861 are allowed to use discretion 

to determine which of their end-use customers are classified as residential. In practice, the 

determination is likely to be based on the utilities' rate structures, which, in turn, are based on 

customers' relative rates of consumption. As noted in a 1990 report:

The utility specifies how much fuel it supplied to residential, commercial, 

industrial, and other customers by totaling the quantity supplied under these rate 

classes. Utilities are. not required to maintain records on the economic activities

Energy Information Administration / Energy Consumption Series 

134 Residential Energy Consumption Survey Quality Profile
of their customers, so their rate structures may not correspond to economic 

definitions of the end-use sectors. To the extent there is not a one-to-one 

correspondence between the economic activity of the customers and the rate 

schedule at which they are billed, there will be a misclassification of end-use 

sector supply data. (ElA 1990, p. 13)

The same report points out that an individual customer's classification or rate-schedule can vary 

during the year as its consumption varies. Similar considerations apply to natural gas distribution 

companies reporting on Form El A-176. Fuel oil distributors reporting on Form EIA-821 are 

specifically instructed to exclude farms and large apartment buildings from the residential sector.

The difference in their definitions of the residential sector is only one of several ways in which 

RECS and the three supplier surveys identified above differ with respect to coverage, timing, and 

definition of data items collected. Consequently, one should not necessarily expect RECS 

estimates of residential consumption to agree closely with estimates of amounts of fuel supplied 

from any of the supplier surveys. Differences also occur because of sampling and nonsampling 

errors in the estimates. Figure 8.1 summarizes the main features of RECS and the supplier 

surveys that affect the comparisons.

Other Differences In Coverage

RECS coverage, as described in Chapter 3, is limited to U.S. housing units occupied as primary 

residences. Vacant units and units used seasonally or occasionally as second homes are excluded. 

Suppliers, on the other hand, are asked to report total amounts supplied to customers, without any 

exclusions. As shown in Table 3.1, Chapter 3, the vacant and seasonal housing units excluded 

from RECS have accounted for between 9.2 and 11.5 percent of total U.S. housing units between 

1981 and 1993, according to biennial estimates from the Census Bureau's American Housing 

Survey. Their proportionate share of total residential energy consumption is probably somewhat 

smaller.

The classification of some master-metered apartments as commercial in the supplier surveys 

works in the opposite direction-that is, it leads to supplier survey estimates that are lower than 

the RECS estimates for the residential sector. For electricity and natural gas, the effects of this 

factor are hard to quantify, because suppliers are not consistent in their classification of 

apartments. In a study undertaken in the mid-1980's the issue was explored for natural gas by 

contacting public utility commissions and large utilities in 5 midwestern States Illinois, Indiana, 

Michigan, Ohio, and Wisconsin-where there is substantial use of natural gas for heating. Based 

on limited data that these sources were able to provide, it was estimated that 3.4 percent of 

natural gas supplied to the residential sector in this 5-State area was being reported in other 

sectors in the supplier survey (EIA 1986a, Table 59, p.72). For fuel oil, the situation is 

somewhat clearer, because the supplier survey instructions specifically request that respondents 

exclude apartments from the residential sector. The 1993 RECS estimated that multifamily 

housing units accounted for 17 percent of all fuel oil consumed by the residential sector (EIA 

1995d, Table 5.2).

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 135
Figure 8.1. Sources of Differences Between REGS Estimates of End-Use Consumption and ElA 

Supply Survey Estimates of Energy Supplied to the Residential Sector

Source

Differences in Coverage

Occupancy 

Apartments

Farm and Other 

Residences with 

Business Uses

Differences in Timing

Reference Period

Storable Fuels 

Sampling Error

_L RECS

Vacant and seasonal units excluded. 

Included.

Included in survey, business uses 

excluded from consumption.

Different from calendar year through 

1984.

Measures amounts used for metered 

fuels; amounts supplied for others.

Estimates of sampling error available.

Supply Surveys3

No exclusions.

May be excluded if commercial rate 

applies.

Household may be excluded if commercial 

rate applies. If included, no basis for 

eliminating consumption for business 

uses

Calendar year for all surveys.

Measure amounts supplied during 

reference period.

None for electricity and natural gas. Fuel 

oil based on sample survey but sampling 

errors of estimates used in comparisons; 

are not available.

'This figure covers the following ElA annual supply surveys: Electricity: Form EIA-861 (Form EIA-826 prior to 1984); Natural Gas: 

Form EIA-176; Fuel Oil and Kerosene Form EIA-821.

Some customers of energy suppliers combine residential and nonfarm or farm business uses of 

fuel in the same account. In RECS, business uses are excluded from estimates of residential 

consumption on the basis of respondents' answers to questions about the proportion of their to'Cal 

consumption of each fuel that is used for business. For the electricity and natural gas supplier 

surveys (Forms EIA-861 and EIA-176), respondents are asked to classify consumers who use 

fuels for both residential and commercial purposes according to their predominant use. so the r.e: 

effect of such mixed uses is difficult to determine. For the fuel oil supplier survey (Form EIA821), farms are excluded from the residential sector.

Differences in Timing

Through survey year 1984. the reference period for RECS consumption and expenditures data ran. 

from April of the survey year through March of the following year. Thus, for the 1984 RECS. 

estimates of consumption were for the 12 months from April 1984 through March 1985. For 

subsequent survey years, RECS consumption data have been collected for a calendar year. All 

of the EIA supply surveys collect data on a calendar year basis.

Consequently, for RECS survey years through 1984, one might expect to see consumption/supply 

survey differences in amounts of heating fuels used/supplied in parts of the country for which 

heating degree-days for January through March varied appreciably from one year to the next. 

Comparisons of expenditure data would be most affected in periods when there were rapid

136

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
fluctuations in energy prices. In a special study of consumption and supply estimates for survey 

years 1978 through 1982, procedures were developed to adjust the data by Census division for 

natural gas and fuel oil from both sources for these differences in timing, as well as for the 

different treatment of apartments in RECS and the supply surveys. These procedures were 

successful in reconciling differences for fuel oil, but only partially successful in reconciling 

differences for natural gas (EIA 1986a, Part 7).

A 1990 study that compared measures obtained from consumption and supply surveys noted that 

"Since fuels (except electricity) can be stored, the amount of product supplied to a sector in a 

given period is not necessarily equal to the amount consumed" (EIA 1990, p.2). However, this 

"storability" factor would not be likely to have significant effects on consumption/supply 

comparisons for the residential sector. For electricity and natural gas, metered amounts are 

reported both in RECS and in the relevant supplier surveys. For the other fuels, since it would 

be impractical for households to report their actual consumption in RECS, deliveries are used as 

a proxy for consumption. Thus, the RECS data for all fuels are comparable in this regard to 

those obtained in the supplier surveys.

Sampling and Nonsampling Errors

The RECS estimates of total consumption of each fuel are subject to sampling error, and 

estimates of their sampling errors have been calculated. The supplier surveys for natural gas and 

electricity include all known suppliers, so the results of these surveys are not subject to sampling 

error. The supplier survey for fuel oil and kerosene is based on a sample of distributors, and 

sampling errors of direct sample estimates have been calculated. However, for the comparisons 

presented below, the sample survey data have been benchmarked to supply data from a different 

source, and sampling errors for these benchmarked estimates have not been calculated.

All of the estimates of end-use consumption and amounts supplied are subject to various kinds 

of coverage, nonresponse, measurement, and data-processing errors, Nonsampling errors of 

RECS estimates have been discussed at length in Chapters 3 through 7 of this report.

Comparisons of Consumption and Supply Data at the National Level

Table 8.1 shows comparisons of RECS and supplier survey data at the U.S. level for electricity, 

natural gas, and fuel oil for all RECS survey years except 1979 and 1981. The key item in the 

table for each year and fuel is the ratio of the supply estimate to the consumption estimate. The 

ratios differ from 1.000 by more than twice their standard errors for 7 of the 21 yearly 

comparisons.

Energy information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 137
Table 8.1. Residential Consumption and Supply of Electricity, Natural Gas, and Fuel Oil: 1978-198:!!.

Consumption & Supply Survey Estimates 1978' 1980 1982 1984 1987 1990 1993

ELECTRICITY

RECS Consumption (billion kWh) 

EIA Supply Data (billion kWh) 

Ratio of Supply to Consumption 

Standard Error of Ratio

RECS Consumption (billion ft3) 

EIA Supply Data (billion ft3 ) 

Ratio of Supply to Consumption 

Standard Error of Ratio

724

671

0.927

0.043

721

717

0.994

0.019

710

730

1.028

0.029

728

778

1 .069*

0.026

808

850

1.052*

0.017

888

924

1.041

0.027

G62

S95

1 .034

0.019

NATURAL GAS

5,461

4,891

0.896*

0.055

4,840

4,752

0.982

0.038

4,680

4,633

0.990

0.039

4,830

4,555

0.943

0.033

4,687

4,315

0.921*

0.034

4,737

4,391

0.927*

0.032

5.131

4.957

0.966

0.03'!

FUEL OIL"

15,802

15,091

0.955

0.075

11,220

10,290

0.917

0.057

8,230

8,274

1.005

0.058

9,080

7,502

0.837*

0.054

8,850

8,106

0.916

0.054

7,100

6,050

0.852*

0.058

7.38C

6,590

0.893

0.032

RECS Consumption (million gallon)'1 

EIA Supply Data (million gallon)" 

Ratio of Supply to Consumption 

Standard Error of Ratio0

* = Ratio differs from 1.000 by more than twice its standard error. 

"Totals for 1978 do not include data for Alaska and Hawaii. 

bFor 1978, 1980, and 1982 includes kerosene.

'Underestimate; does not reflect sampling error of supply survey estimate. 

kWh = Kilowatthours.

Sources: Consumption: Energy Information Administration, Consumption and Expenditures (for years shown); Supply: St&te 

Energy Data (for years shown).

 For electricity, the supplier survey estimates were below the RECS consumption 

estimates in 1978 and 1980; subsequently they have been moderately higher than the 

RECS estimates. They were significantly higher than the RECS consumption 

estimates in 1984 and 1987.

 For natural gas, the supply estimates were below the RECS consumption estimates in 

all years and were significantly lower in three of the seven years.

 for fuel oil, the supply estimates were below the RECS consumption estimates In all 

years except 1982, when the ratio was 1.005. The supply estimates were significantly 

lower in two of the seven years.

The largest change in the supply/consumption ratio between RECS survey years was for fuel O:L , 

where the ratio declined from 1.005 in 1982 to 0.837 in 1984. Form EIA-821 was used for lie 

first time in 1984; it succeeded Form El A-172, which had been used from 1979 through 1982, 

The statistical procedures arid methodologies associated with the new form differed from those 

used earlier; consequently, the supply estimates for 1984 and subsequent years are not considered 

directly comparable with those for prior years (EIA 1995g, p. 348).

138

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Table 8.2 compares RECS consumption and supplier survey estimates at the U.S. level for 

kerosene and LPG for 1990 and 1993. As the table shows, the 1993 RECS consumption estimate 

for kerosene was significantly below the supplier survey estimate. None of the other three 

differences was statistically significant.

Table 8.2. Residential Consumption and Supply of Kerosene and LPG: 1990 and 1993

RECS

Supply

Kero

Consumption and Supply 

Survey Estimates 1990

Consumption (Quadrillion Btu) .07

Data (Quadrillion Btu) .06

Difference (RECS - Supply) .01

Two Standard Errors (REGS Standard Error) .02

sene LPG

1993 1990 1993

.05 .28 .38

.08 .36 .40

-.03* -.08 -.02

.01 .06 .07

* = Absolute value of difference exceeds twice its standard error.

Source: Energy information Administration, Consumption and Expenditures (1990 and 1993).

Of the five major fuels, kerosene and LPG are the least frequently used and together accounted 

for only about 4 percent of total residential consumption in 1993. RECS estimates of their 

consumption are subject to large relative sampling errors, so that comparisons of consumption 

and supply estimates cannot determine whether small observed differences are statistically 

significant. In addition, as was shown in Table 6.5, Chapter 6, for RECS sample households, the 

proportion of kerosene use derived from supplier billing records was less than 30 percent in 1990 

and 1993, compared to much higher proportions for the other four fuels. The primary source of 

the kerosene supply data is the same as for fuel oil, the Annual Fuel Oil and Kerosene Sales 

Report, Form EIA-821. EIA does not survey suppliers of LPG; the supplier data for LPG 

appear annually in the State Energy Data Report and are based on data provided by the American 

Petroleum Institute.

Comparisons at the Census Division Level

A 1995 report (Allied Technology Group 1995) compares RECS consumption estimates by 

Census division with supplier survey estimates for all five major fuels (data for fuel oil and 

kerosene were combined) for the years 1984, 1987, and 1990. Because of the relatively large 

sampling errors of RECS estimates at the Census division level, only large estimated differences 

generally more than 10 percent of the supply estimate, and often more than 20 percent for smaller 

divisions and less frequently used fuels are statistically significant.

The most consistent differences found in this study occurred in the Middle Atlantic Division in 

the comparisons for fuel oil plus kerosene and for LPG. The data for these comparisons are 

shown in Table 8.3. For fuel oil plus kerosene (kerosene is only a small part of the total for the 

two fuels), the Middle Atlantic Division accounts for roughly one-half of total U.S. consumption. 

The RECS consumption estimates were well above the supplier survey estimates in all three 

years. As noted above, supplier survey respondents for Form EIA-821 were specifically

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 139
instructed to exclude apartments and farms from the residential sector. In 1990, an estimated 24 

percent of the consumption of fuel oil in the Middle Atlantic Division was by households that 

were in buildings with two or more housing units.

Table 8.3. Residential Consumption and Supply of Selected Fuels, Middle Atlantic Division: 1984 

1987, and 1990

Consumption & Supply Survey Estimates 1984 1967 1990

RECS Consumption (trillion Btu) 

EIA Supply Data (trillion Btu) 

Ratio of Consumption to Supply 

Standard Error of Ratio"

RECS Consumption (trillion Btu) 

Supply Data (trillion Btu) 

Ratio of Consumption to Supply 

Standard Error of Ratio

FUEL OIL. AND KEROSENE

650 510

410.1 457.0

1.585* 1.335*

0.178 0.092

LPG

10

22.2

0.450* 

0.171

10.0

26.3

0.380*

0.122

513.1 

340.1 

1.590* 

0.109

12.0

27.3

0.440*

0.198

* = Ratio differs from 1.000 by more than twice its standard error. 

"Underestimate; does not reflect sampling error of supplier survey estimate.

Source: Allied Technology Group, Revised Analysis Report: Comparison of Data from Energy Consumption and Supply Survey; 

(March 1995).

Table 8.4. Estimates of Electricity Consumption per Residential Unit from RECS and the Edison 

Electric Institute (EEI): 1970-1984

RE 

Year (kWh per h

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

CS EEI 

lousehold) (kWh per customer)

7.066

7.380

7.691

8.5303 8.079

7.907

8.630a 8.176

8.360

8.693

9.450 8.849

9.150 8.843

8.840 9.025

8.750 8.825

8.480 8.743

8.814

8.440 8.978

Ratio 

RECS/EEI

1.C6

1.C6

1.07

1.03

0.98

0.99

0.97

0.94

"Data from predecessor surveys to RECS that were conducted by the Washington Center for Metropolitan Studies. 

Sources: Energy Information Administration, Consumption and Expenditures (1984); EI5I data are from the Statistical Yearbook 

of the Electric Utility Industry.

140

Energy information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
For LPG, total consumption was much smaller, and the Middle Atlantic Division accounts for 

less than 5 percent of total U.S. consumption. The RECS consumption estimates were 

consistently below the supply estimates, which are based on data provided by the American 

Petroleum Institute.

Supplier Data from Non-EIA Sources

The Consumption and Expenditures report for 1984 includes a comparison of RECS estimates 

of average electricity consumption per household with a data series on average residential 

electricity consumption per customer compiled by the Edison Electric Institute (EEI) (ElA 1987a, 

pp. 288-89). The data from the two sources are shown in Table 8.4. The EEI data were based 

on quarterly surveys of investor-owned utilities, Tennessee Valley Authority distributors, some 

State and Federal projects, and large municipal utilities, supplemented by data from secondary 

sources to complete the coverage (EIA 1989c, pp. 24-25). It is likely that many of the factors 

that were relevant to comparisons of data on total residential consumption of electricity from 

RECS and EIA's supplier surveys (including sampling error of the RECS estimates) would also 

contribute to differences between the RECS and EEI data series. One additional factor might be 

differences in the denominators. For RECS, the denominator is always a single household; for 

EEI, some of the customer accounts may have included more than one household.

Given these differences in the sources of data, the differences between the two sets of estimates 

are relatively small However, there is a clearly evident trend for the ratio of the two series to 

decline between 1978 and 1984. The EEI estimates were relatively stable during this period, at 

the same time that the RECS estimates of consumption per household declined by about 10 

percent.

Comparisons of RECS Data on Housing Unit and Household 

Characteristics with Data from Other Sources

Data items identical or roughly comparable to those included in RECS have been collected in 

several surveys conducted by other agencies, especially the Census Bureau. The existence of such 

comparable items does not mean there is unnecessary overlap among the statistical programs. 

The surveys in question and the decennial census have purposes that are quite different from 

those of RECS. RECS provides in-depth information about residential energy consumption and 

expenditures, whereas the Census Bureau's American Housing Survey covers a broad array of 

characteristics of the nation's housing stock and provides more detailed data for subnational areas. 

The Decennial Housing Census provides small-area data for a few basic housing items. Some 

data that are potentially comparable to RECS estimates are also provided by administrative data 

systems, such as those established for the Food Stamp and Low-Income Home Energy Assistance 

(LIHEAP) Programs. The comparisons discussed in this section are organized by data source, 

starting with the American Housing Survey, continuing with other surveys and the Housing 

Census, and concluding with administrative record sources.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 141
The American Housing Survey: Comparisons with NIECS

Prior to 1980, the American Housing Survey (AHS) was conducted annually and was called the 

Annual Housing Survey. The most systematic comparison of RECS and AHS data, undertaken 

by the University of California's Energy Research Group, used data from the 1978 RECS 

(NIECS) and the 1978 AHS (Blumstein et al, 1982). There were 18 variables that were 

essentially the same in both surveys:

Year structure built Have thermostat

Main heating equipment type Have air-conditioning

Main heating fuel Have hot running water

Cooking fuel Have roof insulation

Household income Have storm windows

Property value Have storm doors

Tenancy type Have complete plumbing

Water heating fuel Number of AC units

Number of household members Number of rooms

Some additional variables were similar but provided data for different time periods in the two 

surveys.

When the comparisons were made, estimates of sampling errors were only available for a few 

of the NIECS variables, so it was often not possible to determine which of the NIECS/AHS 

differences were statistically significant. Unlike the AHS, the NIECS did not cover Alaska arid 

Hawaii, but the study report does not mention whether the AHS data were adjusted to take 

account of this difference in coverage. For a few of the variables compared, the AHS estimates 

included vacant units, which were excluded from NIECS. Some highlights of the comparisons 

were:

 At both the national and regional levels, there was a clear tendency for the NIECS 

family income distribution to show a higher proportion of families in the upper income 

categories. This tendency was especially pronounced in the South region, which 

showed the following distribution:

1977 family income class Percent of families

NIECS AHS

Below $5,000 14.7 20.9

$5,000 - 9,999 22.0 22.5

$10,000 and over 62.3 56.5

However, in the comparisons of RECS and CPS income data, presented later in this 

section, the difference was in the opposite direction. The CPS uses more detailed 

income questions than either RECS or the American Housing Survey.

Energy Information Administration / Energy Consumption Series 

142 Residential Energy Consumption Survey Quality Profile
« A similar tendency was noted for the distribution of property values for owneroccupied housing units.

 The proportion of households with one member was smaller for NIECS (18.8 percent) 

than for the AHS (22.2 percent).

The general conclusion of the study was that for most variables there was reasonably good 

agreement between the NIECS and AHS estimates.

The American Housing Survey: Other Comparisons

The Consumption and Expenditures report for 1993 (EIA 1995d) includes a comparison of the 

distributions of occupied housing units by year built, as estimated from the 1993 RECS and the 

1993 AHS. The results are shown in Table 8.5. The two distributions are in reasonably good 

agreement, but the proportion of units built between 1970 and 1979 as estimated by RECS was 

significantly below the corresponding AHS estimate.

The Current Population Survey (CPS)

As explained in Chapter 7, Section 7.1, estimated household counts from the annual March 

supplement to the CPS are used to derive the benchmark values for the stage two ratio 

adjustments that are part of the RECS estimation procedure. Hence, for the categories used as 

benchmarks, RECS and CPS estimates are in close agreement. For the first 5 survey years, 12 

control totals were used, defined by the four Census regions and three location categories 

central city, remainder of metropolitan statistical area, and nonmetropolitan. However, 

comparisons of RECS and CPS estimates of the number of households by number of persons for 

1980, 1981, and 1982 showed that the proportion of single-person households in RECS was 

consistently low for both owners and renters (Response Analysis Corporation 1983).

Consequently, for the 1984 RECS stage 2 ratio-estimation procedure, additional benchmark 

categories were introduced for one-person households occupied by males, one-person households 

occupied by females, and all other households.

Data on household income are also collected annually in the March supplement to CPS. The CPS 

procedures for collecting data on income are more elaborate than those used in RECS. The 

RECS questionnaire asks respondents whether or not any family members had income in each of 

several categories (earnings, self-employment, Social Security, etc.) and then asks them to assign 

their total family income to one of a large number of income class intervals. Income of persons 

living in the household who are not members of the family is supposed to be excluded. The CPS 

questionnaire calls for actual dollar amounts in each of several income categories separately for 

each household member age 15 and over. The time references also differ: RECS asks for 

income in the 12 months preceding the interview date (generally in the fall of the year), whereas 

the March CPS asks for income in the prior calendar year.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 143
Table 8.5. RECS/AHS Comparisons of Occupied Housing Units by Year Built: 1993

Year of 

Construction

Percent of Housing Units8

AHS REGS REGS - AHS

1939 or earlier

1940 to 1949

1950 to 1959

1960 to 1969

1970 to 1979

1980 to 1984

1985 to 1989

1990 to 1993

21.0

8.0

13.0

15.2

22.0

7.6

8.4

4.8

21.0

7.1

13.5

15.5

18.8

8.8

9.1

6.1

o/:

-0.8 

0.5 

0.3

-3.2" 

1 2 

07 

1.2

"Percents may not add to 100.0 due to founding.

b Difference is statistically significant at the 95 percent confidence level.

Source: Energy Information Administration, Consumption and Expenditures (1993). Appendix B.

Detailed comparisons of RECS and CPS income data for 1980, 1984, and 1990 were undertaken 

by Response Analysis Corporation (1994) as part of an analysis of alternative measures of energy 

burden that is, the share of income used to pay energy bills. Estimates of median income for 

the 3 years were as follows:

Year

1980

1984

1990

RECS

$16,172 

$19,488 

$26,364

CPS Percent difference

(RECS - CPS)/RECS

$17,434 -7.8

$22,200 -13.9

$29,306 -11.2

The values shown for CPS represent total income of all household members. For 1990, it was: 

possible to calculate median family income for CPS; that value, $27.915, was closer to the RECS 

estimate, the difference being -5.9 percent of the RECS value.

Table 8.6 shows comparisons of RECS and CPS income distributions for 1989 and 1990. The 

RECS distributions are based on family income and the CPS distributions are based on household 

income. Compared to RECS, the CPS distributions place a significantly higher proportion of 

households in the two top income classes. The differences might have been smaller if the CPS 

distributions had been based on family income, excluding nonfamily members in the sample: 

households.

144

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Table 8,6. RECS/CPS Family Income Comparisons: 1987 and 1990

I

198

Income Category" RECS

Less than $5,000 6.8

$5,000 to 9,999 12.7

$10

$15

$20

$25

$35

$50

,000 to 14,999 13.9

,000 to 19,999 10.0

,000 to 24,999 9.7

,000 to 34,999 17.9

,000 to 49,999 14.8

,000 and over 14.3

3ercent of Households 11

7 1990

CPS RECS CPS

6.9 5.6 5.2

11.5 11.4 9.7

10.6 12.1 9.5

10.0 9.0 8.8

9.2 9.6 8.9

16.1 16.2 15.8

17.2 17.8 17.5

18.5 18.4 24.7

'Income of family members for RECS, household members for CPS.

bPercents may not add to 100.0 due to rounding.

Sources: Energy Information Administration, Housing Characteristics (1987), Appendix C; Consumption and Expenditures (1990), Appendix C.

The Decennial Housing Census

Most housing characteristics that appeared in both the 1980 RECS and the 1980 Census of 

Housing were in reasonably good agreement. One exception was the number of households using 

wood as their main heating fuel (Carlson 1985). Estimates from the two sources were as follows:

Data Source Households Using Wood as Main Heating Fuel 

Estimate Two Standard Errors

RECS 

(Nov. 1980)

Census 

(April 1980)

4,700,000

2,575,560

800,000

7,060

The 1980 Annual Housing Survey estimated that 1,377,000 housing units ( 101,000) used wood 

as their main heating fuel in 1980. However, unlike the RECS and Housing Census inquiries on 

main heating fuel, which were quite similar, the AHS inquiry did not provide a separate response 

category for wood.

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 145
Possible reasons for the difference between the RECS and Housing Census estimates include:

 Timing. According to RECS estimates, the proportion of households using wood z,- 

their main heating fuel rose steadily from 2.5 percent in 1978 to 6.4 percent in 198.. 

As noted above, the reference date for the 1980 RECS was 7 months later than tin 

Census date.

 The RECS questionnaire gave greater emphasis to the use of wood as a fuel. It ha-: 

several specific questions about wood, covering all types of uses and amounts user 

Questions about secondary heating fuels and equipment were included. Wood is ofte 

used in conjunction with other heating fuels.

The Consumer Expenditure Survey (CES)

Since 1980, the CES, which is conducted by the Census Bureau for the Bureau of Labor 

Statistics, has provided annual estimates of household expenditures in a large number cl: 

categories, including natural gas, electricity, and fuel oil. Branch (1994) has compared CES 

estimates of expenditures for these fuels with RECS estimates for 1984, 1987, and 1990. Th>:; 

results are shown in Table 8.7. There was an apparent error that affected the published ratios for 

electricity and the total for all major fuels for 1987; the values shown in the table differ from 

those published by Branch.

The CES estimates of expenditures on electricity were above the RECS estimates for all 3 year:;;, 

Because electricity accounts for more than half of the total for the three fuels combined, the CE:'!:> 

estimates of totals for all major fuels also exceed the RECS estimates in each year. Tru; 

publication that was the source of the CES estimates does not provide sampling errors but, based 

on the RECS sampling errors, the 1984 and 1987 ratios for electricity are clearly significantly 

different from 1.00 at the 95-percent confidence level. The ratios shown for the other fuels an:! 

for electricity in 1990 are probably not significantly different from 1.00.

The RECS estimates used for these comparisons were estimates of consumption of each fuel, bhouseholds that paid for all of their uses of that fuel. Branch stales that this population "... mon; 

closely matches the population covered in CE estimates for energy expenditures," but does not 

explain what differences, if any, there are. The CES estimates used for the comparisons wen;: 

adjusted to eliminate energy expenditures associated with vacation properties.

Other factors that might be associated with differences in the two sets of estimates include:

 CES estimates are for the calendar year in each of the 3 years. The RECS estimates 

for 1984 covered the period from April 1984 through March 1985.

 CES estimates may include some expenditures by households that do not pay for i:.\{ 

of their uses of a particular fuel. These households were excluded from the RECS 

estimates that were used for the comparisons.

Energy information Administration / Energy Consumption Series 

146 Residential Energy Consumption Survey Quality Profile
 Military households on post are included in RECS but not in the CES.

 For 1984 and 1987, the CES fuel oil expenditures were compared with RECS 

expenditures for fuel oil and kerosene combined.

 About 15 percent of households use budget plans to pay their suppliers; these plans 

allow them to spread their costs more evenly over the year. RECS consumption 

estimates are based on amounts actually supplied, whereas CES estimates are based 

on amounts paid.

Table 8.7. Comparison of Aggregate Expenditures for Selected Fuels, Consumer Expenditure 

Survey (CES) and RECS: 1984, 1987, and 1990

Expenditure 

Category

CES3 (in billions)

1984 1987 1990

RECS (in billions)

1984 1987 1990

Ratio: CES/RECS

1984 1987 1990

Natural Gas

Electricity

Fuel Oil"

Major Household 

Fuels, Total

$26

58

7,

91.

.5

.0

.4

.9

$21.8

64.7

5.4

91.9

$23.8

73.0

6.2

103.0

$25.0

51.8

7.4

84.3

$21.7

58.5

5.8

86.0

$23.3

68.6

6.5

98.4

1

1

1

1

.06

.12

.00

.09

1.00

1.11

0.93

1.07

1.02

1.06

0.95

1.05

"CES estimates were adjusted to exclude expenditures for owned or rented vacation property. 

"For 1984 and 1987, RECS estimates for fuel oil include estimates for kerosene.

Sources: Branch, The Consumer Expenditure Survey: A Comparative Analysis (1994); Energy Information Administration, 

Consumption and Expenditures (for years shown).

Comparisons of RECS and Administrative Data

As part of its income inquiry, RECS asks respondents about receipt of food stamps. In the 1981 

and 1982 surveys, they were asked about receipt during the calendar year prior to the survey; 

subsequently they have been asked about receipt during the 12 months prior to the survey 

interview. Since most interviews take place in the fall of the survey year, the latter approach is 

roughly equivalent to asking about receipt during the fiscal year that runs from October of the 

year preceding the survey year to September of the survey year.

Table 8.8 shows the results of a comparison, for selected survey years, of RECS estimates of the 

number of households receiving food stamps with counts based on records maintained by the 

Agriculture Department's Food and Nutrition Service, which administers the Food Stamp 

Program (Thompson 1994b). Estimates from RECS were below the program counts for all years 

shown and, except for the 1982 and 1984 RECS, the survey estimates were significantly lower.

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 147
Table 8.8. Comparison off Number of Households Receiving Food Stamps, RECS Estimates an 

Program Counter Selected Years

Year Food Number of Hoi

Stamps 

Received3 RECS

1980 6,777

1981 6,724

FY

FY

FY

1984 7,348

1987 5,568

1990 6,010

jsehotcfs (000) Ratio:

RECS/USDA

USDA

7,718 088';

7,249 0.93

7,580 0.97

7,122 07S': '

7,787 0.7T

"The 1981 and 1982 RECS asked about receipt of food stamps during the prior calendar year. Subsequent surveys asked a be .1: 

receipt during the past 12 months, which is roughly equivalent to the fiscal year because interviewing is done in the fall.

* = Ratio differs from 1.00 by more than twice its standard error.

Sources: Energy Information Administration, RECS: Survey data for 1981,1982, 1984,1987, and 1990; USDA: Food and Nutrticr 

Service, Public Information Data Bank and National Data Bank, January 1993.

A similar comparison with program data has been made for RECS estimates of the number of 

households receiving assistance under the Low-Income Home Energy Assistance Program, which 

was authorized by 1981 legislation and is currently administered by the Administration for 

Children and Families, Department of Health and Human Services (Thompson 1994b). Th;: 

results of the comparison, which is based on assistance for home heating costs only, are show::i 

in Table 8.9. In this instance, the RECS estimates are significantly below the program cou.n::: 

for all years. For the 3 years shown in both tables, the observed ratio of RECS estimates: :o 

program counts was lower for energy assistance than it was for food stamps.

These findings for RECS are consistent with experience from other household surveys which ha.vft 

attempted to collect data on income recipiency from public income transfer program:, 

Comparisons with administrative data for 1983 and 1984 showed that the Census Bureau's Survey 

of Income and Program Participation, which uses a considerably more detailed set of income 

questions, was identifying about 90 percent of the households receiving food stamps and that a. 

somewhat smaller proportion of the total amounts disbursed was being reported. For calendar 

1983, the Current Population Survey estimate of the total value of food stamps received w;i;:; 

about 71 percent of the figure provided by the Food and Nutrition Service (Jabine 1990. Table 

10.1).

Possible reasons for such underreporting in surveys include respondent reluctance to report receiot 

of welfare payments, respondent misclassification of the source of income, and survey 

undercoverage of low-income households. Differences in the frequency and method of receiol 

may affect the level of reporting. Households receive food stamps every month and take them 

or a debit card to the store where they buy their food. Energy assistance, on the other hand, i;; 

received once a year and the payment may be sent directly to a utility with only a notice to tin:: 

household recipient (Thompson 1994b, p.6).

Energy Information Administration /Energy Consumption Series 

148 Residential Energy Consumption Survey Quality Profile
Table 8.9. Comparison of Number of Households Receiving Low-Income Home Energy 

Assistance, RECS Estimates, and Program Counts: Selected Years

FY 1982

FY 1984

FY 1987

FY 1990

Year Heating Number of Hoi

Assistance """l" " ' Recevied" RECS

3,908

5,293

4,770

4,156

jsehoids (000) Ratio:

Kbuo/HHb

HHS

5,990 0

6,444 0

6,495 0

5,460 0

65*

82*

73*

76*

'The RECS questionnaire asks about receipt of LIHEAP assistance during the fiscal year preceding She survey interview. 

* = Ratio differs from 1.00 more than twice its standard error.

Sources: Energy Information Administration, RECS: Survey data for 1982,1984,1987, and 1990; HHS: Low Income Home Energy 

Assistance Program reports to Congress for the fiscal years shown.

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 149
9. Summary

The goal of the Residential Energy Consumption Survey is to provide periodic high-quality 

national and regional data about household energy consumption and expenditures and related 

characteristics of housing units and households. The National Interim Energy Consumption 

Survey (NIECS) in 1978 initiated an ongoing program to solve the many challenging problems 

that confront attempts to collect accurate survey data on these topics.

A noteworthy feature of RECS from the beginning has been its use of several different sources 

of data to provide the most accurate information that can be obtained at a reasonable cost. The 

Household Survey is the central component of the RECS design, but most of the direct 

information about energy consumption and expenditures of sample households is obtained from 

their energy suppliers and distributors. For households living in rental units, the accuracy of 

information about some housing unit characteristics is improved by contacts with their rental 

agents. Local weather information obtained from the National Oceanic and Atmospheric 

Administration serves several important purposes. A full understanding of the quality of RECS 

data requires awareness of how these different sources relate to each other and how the data are 

integrated to provide a comprehensive picture of the residential energy sector.

Another unusual feature of RECS has been the determination of the total and heated floor space 

of sample housing units through direct measurement by the survey interviewers. It became 

evident early on that survey respondents could not provide accurate estimates of floor space (see 

Chapter 5, section on "Special Data Collection Procedures" and Table 5.1), so the measurement 

procedure was adopted in the 1980 survey. This is just one example of many procedural 

improvements that have been introduced over the life of RECS in a constant effort to improve 

the quality of the survey data and the efficiency of the survey design and procedures.

A few innovations have not been successful. In the 1990 RECS, for example, interviewers 

attempted to record name-plate information from central air-conditioning equipment so that 

measures of rated efficiency for the equipment could be obtained by matching against directories 

of equipment characteristics. The desired information was obtained for fewer than one-fifth of 

the sample households with central air-conditioning, so the procedure was not repeated in the 

1993 RECS (see Chapter 5, section on "Special Data Collection Procedures").

For the analysis of energy consumption, it is important to know not only the total consumption 

of each fuel, but how consumption is allocated to major end-use categories, such as space and 

water heating, cooling, and appliances. It has not been feasible to collect such information 

directly, so an indirect model-based approach has been developed to allocate total consumption 

of each fuel to these different end uses in each sample household. Much developmental effort 

has been devoted to improvement of the end-use estimation procedures, both by refinements in 

the structure of the model and by the introduction of new survey items whose use as independent 

variables in the model has potential for improving the accuracy of the estimates.

The next section of this chapter summarizes the information about sources and types of errors in 

RECS estimates that was presented in Chapters 3 through 8. The following section identifies 

some methodological research currently in progress and some design and procedural changes that

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 151
are being considered for the 1996 REGS and beyond. The final section presents some 

suggestions to data users for making effective use of REGS data, taking into account what i ; 

known about their quality and how it has been affected by design and procedural changes duririg 

the life of the survey.

Principal Sources of Error

Coverage Error

Vacant units and units occupied only on a seasonal or occasional basis are deliberately excluded 

from REGS. Such units have accounted for between 9 and 12 percent of all housing units (:=;:<= 

Table 3.1) and probably for a smaller proportion of total residential energy consumption. Groupquarters and institutions are not considered part of the residential sector and are also exclude: d. 

Prior to the 1980 REGS, there was no sample coverage of Alaska and Hawaii, and individual 

housing units on military bases were not covered.

Consistent with experience In other surveys and censuses, there is evidence that REGS does r:c": 

achieve full coverage of households in its target population. From 1980 through 1990, REC!:: 

sample estimates of the number of U.S. households, following the application of sample weight,1 

and adjustments for unit nonresponse, have been adjusted upward by between 6.6 and 9.7 percerr 

in order to agree with benchmark estimates derived from the Census Bureau's Current Popula.ti:::n 

Survey. For 1993, the upward adjustment declined to 4.2 percent (see Table 3.3). The CPS 

benchmark estimates themselves have been benchmarked to agree with projections based ::r 

decennial census counts, but do not reflect known undercounts of households in the census 

Except in the 1980 and 1993 REGS, these adjustment factors have been substantially higher fo:: 

the South than for the other three Census regions.

There is also evidence of differential undercoverage in REGS of housing units classified by yeabuilt, with the most recently constructed units being most likely to be missed (Table 3.4). Thi.;.; 

problem may be due in part to the need to complete the list updating and sample selection 

operations prior to the survey reference date and in part to errors in carrying out the procedure;; 

for updating the sampling frame.

Certain kinds of energy consumption associated with households in the target population an: 

deliberately excluded from the REGS estimates. Some but not all of the energy consumption :';K 

outdoor uses, such as lawn mowers and outdoor grills, is excluded. Wood energy consumptier 

is not included in the formal estimates of total energy consumption; however, sufficient 

information about wood consumption is collected to provide the basis for a rough estimate of 

what it might contribute if included in the total. No estimates are developed for fuel equivalent;! 

of solar energy. Finally, data users should be aware that RECS measures energy consumption 

at the point where it enters the residence (site consumption); therefore, the survey estimates or 

consumption of electricity for years prior to 1993 do not reflect the total amount of energy u:;e:;i 

to generate it (primary consumption). The Consumption and Expenditures 1993 report include::; 

two tables--5.2 and 5.4 which show both site and primary consumption of electricity, classifi;:" 

by several housing unit and household characteristics.

Energy Information Administration / Energy Consumption Series 

152 Residential Energy Consumption Survey Quality Profile
Nonresponse: Household Survey

Unit response rates for the RECS Household Survey have two components: the proportion of 

eligible households for which acceptable questionnaires were completed in personal interviews 

and the proportion for which questionnaires were obtained by mail (in 1993 by mail and also by 

telephone) following unsuccessful efforts to conduct personal interviews. The latter group can 

be regarded as partial responses, because the mail and telephone questionnaires contain only a 

few key items from the interview questionnaire, their main purpose being to get permission from 

these households to contact their suppliers in order to obtain consumption and expenditure data.

As can be seen in Figure 9.1 and Table 4.1, unweighted interview completion rates remained 

fairly steady, around 85 percent, from 1978 through 1982. For the next three survey years, they 

were at a lower level, between 81 and 82 percent, and in 1993 they declined to their lowest level, 

79 percent. The percent of questionnaires completed by mail varied in a fairly narrow range, 

between 3.0 and 5.3 percent, between 1978 and 1990, but in 1993, the percent completed by mail 

or telephone was only 2.2 percent. As a result, the combined response rate in 1993 was 81.2 

percent, nearly three full percentage points below the previous low in 1984. One factor that 

probably affected response rates for survey years 1982 through 1990 was the presence of a 

longitudinal component in the sample for those years, with roughly half of the households having 

been asked to participate in an earlier survey and some also to participate in the Residential 

Transportation Energy Survey that followed the earlier survey. There is clear evidence that 

response rates were lower for these "recycled" households (see Table 4.2). However, this was 

not a factor for the 1993 RECS, whose sample did not include a longitudinal component.

Some fairly consistent patterns have been observed in the relative response rates for different 

subgroups of the RECS target population (see Tables 4.3a, b, and c). The Northeast region has 

consistently had the lowest overall response rates. The South has had the highest personal 

interview response rates, but the lowest mail response rates for most years. Households in urban 

areas have had the lowest overall response rates and those in rural areas have had the highest 

rates. Except for 1993, households in structures with five or more housing units have had the 

lowest personal interview response rates.

As explained in Chapter 7, the estimation procedures for RECS include adjustments for unit 

nonresponse that are designed to minimize the effects of bias resulting from differential response 

rates by Census region and urban/rural status. The effectiveness of such adjustments depends on 

the level of nonresponse and the extent to which the characteristics of responding and 

nonresponding households within each control group are similar.

Item nonresponse has been relatively low for most items included in the Household Survey. The 

item nonresponse rate for an item is calculated as the percent of those questionnaires requiring 

responses to the item for which no response or a refusal was recorded. In the 1990 RECS, only 

51 of 416 survey variables had item nonresponse rates of 5.0 percent or more (questionnaires 

completed by mail were not included in these rates). Not surprisingly, nonresponse rates to the 

basic question on household income were relatively high for all survey years for example, they 

were 14.4 percent in 1990. However, about four-fifths of the households that refused or were 

unable to respond to the basic income question were willing to say whether their income was

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 153
Figure 9.1. Household Suivey Completion Rates, 1978-1993

100

95

90

§ 85

80

Overs'

Person;!

Inter/ie;

o

19781979198019811982 1984 1987 

Source: Energy Information Administration, Residential Energy 

Comsumption Survey, 1978-1993. 

Note: No surveys done in 83, 85, 86, 88, 89, 91, and 92.

1990 1993

154

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Residential Energy Consumption Survey Quality Profile
above or below a single cutoff value ($35,000 in 1990). Other kinds of items that have had 

relatively high nonresponse rates include those relating to presence and amount of insulation and 

those relating to ages of equipment and appliances. Nonresponse for some of these items, such 

as age of heating equipment, tends to be concentrated among households living in rental 

apartments. Most of the nonresponse for these topics probably comes from respondents' inability 

to answer questions about them accurately.

A significant part of each Household Survey interview is devoted to the measurement and 

recording of information about total and heated floor space. The proportion of housing units for 

which no usable measurements were obtained has been consistently low, with a maximum of 6.4 

percent in the 1984 RECS. However, the proportion of units with only partial information has 

ranged from 15 to 38 percent (Table 4.5). The most frequent omission is failure to state whether 

the measurements recorded have been taken inside or outside of the housing unit. (Outside 

measurements are preferred.)

Imputed values are substituted for most missing items, the main exceptions being questions 

relating to insulation and temperatures maintained in the home. Hot-deck imputation procedures, 

in which values for the missing items are randomly selected from groups of households that 

match on related variables, are used for most items.

Nonresponse: Supplier Survey

Unlike the unit response rates for the Household Survey, the corresponding rates for the Supplier 

Survey have remained relatively stable since the beginning of RECS (see Table 4.7). Unit 

response rates for the Supplier Survey are defined as the percent of eligible households for which 

usable billing records were obtained. The major variations in eligibility and response rates are 

by fuel.

Supplier Survey response rates for electricity and natural gas, which together currently account 

for about 85 percent of the total consumption of the five major fuels, have remained consistently 

high, in the neighborhood of 90 percent. For fuel oil and LPG, which account for most of the 

remaining consumption, the rates have varied between 60 and 75 percent, and for kerosene the 

rate has varied from 19 to 32 percent (separate data for fuel oil and kerosene are available only 

from 1984 on).

Ineligibility rates for the supplier survey for households using a particular fuel are also a factor 

affecting the quality of data on consumption and expenditures for that fuel. Households are 

ineligible either because they do not pay separately for all uses of the fuel or because they 

purchase it mainly on a cash and carry basis. Thus, their suppliers would not have records 

containing the desired information. As shown in Figure 9.2, eligibility rates are highest for 

electricity and LPG, somewhat lower for natural gas and fuel oil, and lowest for kerosene.

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Residential Energy Consumption Survey Quality Profile 155
Figure 9.2 Supplier Survey Eligibility and Completion Rates, by Fuel: 1993

Natural Gas

Electricity

0% 20% 40% 60% 80% 100%

(3 Eligible & Usable Eligible & Not Usable a Ineligible

Source: Energy Information Administration, Residential Energy Consumption Survey, 1993.

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For electricity and natural gas, supplier billing data for part of a year can be used to impute a full 

year's consumption and expenditures. For fuel oil, kerosene, and LPG, billing data are only 

considered usable if they cover the full year. For households that were not eligible for the 

Supplier Survey and those for which usable billing data were not obtained, responses to 

household survey questions on deliveries of fuel oil, kerosene, and LPG are sometimes used to 

estimate consumption. When this cannot be done, the end-use consumption model is used to 

estimate total consumption of the fuel, as well as its allocation to different end uses.

As shown hi Figure 9.3 and Table 6.5, the proportion of total consumption that was imputed by 

the end-use model was lowest for electricity (about 10 percent in 1990 and 1993) and highest for 

fuel oil and kerosene (each between 25 and 35 percent in those years). Figure 9.4 shows that 

natural gas and electricity account for about 85 percent of total consumption of the 5 major fuels. 

Thus, for all major fuels combined, about 75 percent of estimated total consumption was based 

on Supplier Survey billing data for all or most of the year. The proportion of total consumption 

imputed by the end-use model was lowest for households living in single family structures and 

highest for those living in structures with five or more housing units (see Table 4.8).

Measurement Error

In REGS the dividing line between nonresponse error and measurement or response error is not 

always sharply drawn. The role of the Rental Agent Survey illustrates this point. For households 

in multiunit structures that have one or more fuels included in their rent payments, information 

about selected housing characteristics is collected from their rental agents, because experience has 

demonstrated that the agents can often provide more accurate information about items like year 

of construction and main heating fuels and equipment. Failure to obtain information from rental 

agents (nonresponse in the Rental Agent Survey) does not amount to item nonresponse, but it 

does mean that a response from the Household Survey that is more likely to be in error takes the 

place of information from the preferred source. Similar considerations apply when no usable 

information on consumption and expenditures is obtained from suppliers of households that are 

eligible for the Supplier Survey. As shown in Table 4.6, the proportion of eligible households 

for which the rental agent survey was completed varied from 57 to 89 percent between 1981 and 

1993, and has exceeded 80 percent for each of the last three surveys.

There are no systematic continuing sources of information about measurement error in REGS. 

As noted in Chapter 5, information about response variance, interviewer variance, and bias has 

come largely from occasional studies, in some instances restricted to a small set of sample 

households and often providing indications, rather than direct measures, of these components of 

total survey error. Such studies were more frequent in the earlier survey years. Two particularly 

useful sources of information have been longitudinal comparisons for households that were in the 

longitudinal component of the sample (see Tables 5.3 to 5.5) and reinterviews of a few 

households that had unusually large differences between consumption reported by suppliers and 

model-based estimates of consumption (see Figure 5,1).

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Figure 9.3 Sources of Data for Estimates of Total Fuel Consumption, by Fuel: 1993

Kerosene

Fuel Oil

0% 20% 40% 60% 80% 100%

D Billing Records Supplier/Household Estimate CI Regression Estimate |

Source: Energy Information Administration, Residential Energy Consumption, 1993.

Figure 9.4 Proportion of Total Energy Consumption, by Fuel: 1993

LPG ^H 3.8

Kerosene L.5

Fuel Oil 10.2

Natural Gas

Electricity

52.7

Source: Energy Information Administration, Residential Energy Consumption Survey, 1993.

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A review of the available evidence indicates that certain items are especially difficult for some 

respondents to answer and are therefore subject to relatively high levels of response variability 

and possibly bias. Such items include the year of construction of the housing unit (see Tables 

5.3 and 5.6) and the number of windows in the unit (Table 5.3). Interviewers have had some 

difficulty distinguishing single-family attached housing units from single-family units and from 

units in apartment buildings with two to four units (Table 5.5). An examination of changes based 

on the Rental Agent Surveys (Table 5.7) suggests that households eligible for that survey often 

gave incorrect information about the type of main heating equipment and the fuels used for space 

and water-heating and for air-conditioning.

Comparisons of data on family income from RECS and the Census Bureau's Current Population 

Survey suggest that RECS, on the average, is understating income, even after the possible effects 

of conceptual differences are considered (see Table 8.6). The income questions used in the CPS 

distinguish more different sources of income than those used in RECS and ask for separate 

information for each member of the household. CPS data, in turn, appear to understate income 

when compared with data from the Census Bureau's Survey of Income and Program Participation 

(Jabine 1990, Table 10.1), which uses a still more extensive set of income questions.

There have been no formal evaluation studies of the quality of billing data provided by the energy 

suppliers for the RECS sample households. One cannot assume that billing records are entirely 

error-free. For electricity and natural gas, errors may occur when meter readings are estimated 

or read by the consumer. Even when the meter is read by a company employee, the meter may 

not be entirely accurate or a value may be incorrectly recorded. Nevertheless, it is unlikely that 

errors in the billing records have a significant effect on overall quality. As discussed in the next 

subsection, a more important determinant of the quality of consumption and expenditure data for 

households with usable supplier data is the set of procedures used to annualize the billing data 

and to adjust the values for households that use some energy for nonresident]al purposes.

Data Processing and Imputation

A complex set of manual and automated processing procedures is used to convert the completed 

questionnaires from the three component surveys of RECS plus the weather data from NOAA 

into a set of usable data files. Both manual reviews and computer-assisted edits play a major role 

in attempts to locate and eliminate errors in the data. Following the initial computer edits, hard 

copy questionnaires are often consulted when inadmissable or inconsistent values are identified. 

In a small proportion of cases, respondents are contacted by telephone. These procedures are 

described in detail in the first section of Chapter 6, "Data Processing Other Than Imputation," 

and some aspects are summarized in outline form in Figures 6.1 through 6.5.

During the computerized data processing operations, an archival file is created containing records 

of changes made to individual records at each stage. An evaluation study based on the archival 

file for the 1984 RECS showed that many changes were needed to correct errors that occurred 

during data entry (Table 6.1). Based on this finding, sample verification of data entry, which had 

been adopted as a cost-saving measure for survey years 1981 through 1984, was replaced by 100 

percent verification in all subsequent survey years,

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In one phase of data processing, weather information obtained from NOAA is used to estimate 

heating and cooling degree-days for different locations and these estimates are linked to 

individual sample housing units associated with those locations. Initially, temperature data for 

each of 345 (as of 1987) NOAA divisions were used for this purpose, but studies (see Chapter 

5, section on "Comparisons of Individual Household Data from Alternate Sources" and Table 5,8; 

indicated that it would be more accurate to use, for each sample housing unit, estimates o ' 

degree-days based on the nearest of NOAA's more than 4,000 individual weather stations. Thi;;. change was made in the 1987 RECS.

Item nonresponse in the Household Survey is low for most items, a major exception being 

income (see Table 4.4). Missing responses are imputed for most variables and the hot-deck 

method of imputation is the one most frequently used. A substantial amount of imputation is 

required for the small proportion of households for which the Household Survey information b 

collected by mail or telephone, because the content of the mail and telephone questionnaires i:- 

deliberately limited to a few key items. The imputation procedure for these questionnaires, which 

is outlined in Figure 6.6, links them (the "donees") to personal interview questionnaires ("donors' 1 ') 

that match on a set of variables that are available on both sets.

The development of consumption and expenditures data requires several types of imputation, all i 

of which are subject to some degree of error:

 For households with usable Supplier Survey data, an elaborate "annualization" 

procedure is used to convert information for the supplier's billing periods or delivery 

dates to estimates for a consumption year that contains exactly 365 days and is as 

close as possible to the RECS reference period for consumption for example, calendar 

year 1993 for the 1993 RECS. The annualization procedure is described in Chapter 

6, in the section on "Imputation," and the procedure for electricity and natural gas is 

shown schematically in Figure 6.7.

 For kerosene, usable Supplier Survey data are obtained for fewer than one-third of lie 

sample households. For most households that obtain kerosene on a cash-and-carry 

basis, estimates of consumption are based on Household Survey questions abou.i; 

number and usual size of purchases during the reference year.

 When neither of these sources of consumption data is available, consumption of the 

fuel for the household is estimated by a nonlinear regression model. This model. 

which is also used to allocate total consumption to end uses, is discussed further in Ihe 

next subsection.

 If a household reports that part of its consumption of a particular fuel was for 

nonresidential purposes, such as farming or a home business, the reported or imputed 

consumption is adjusted downward. Adjustments are based on Household Survey 

questions which ask respondents to choose one of five class intervals containing the 

estimated proportion of the fuel used for such purposes.

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160 Residential Energy Consumption Survey Quality Profile
Estimation

The weighting procedures used to produce RECS sample estimates are similar to those used in 

several other U.S. national household surveys. The final estimation weight for each household 

(see Chapter 7, section on "Sample Weighting Procedures," and Figure 7.1) is the product of 

three components: a weight based on the household's overall probability of selection; an 

adjustment for unit nonresponse; and a ratio adjustment. The ratio-adjustment component serves 

two purposes: to reduce the effect on the sampling error of the variation between primary 

sampling units and to reduce the mean square error of estimates by benchmarking them to 

household counts based on the Census Bureau's Current Population Survey (CPS). Initially, the 

second part of the ratio-adjustment component benchmarked the sample estimates to 12 control 

totals consisting of CPS estimates for the four Census regions and three location categories within 

each region. Subsequently, the adjustments have been refined to provide separate control totals 

for one-person households, four large States, and the nine Census divisions.

Some special estimation procedures were developed for use in analyses of 1990 RECS data on 

energy consumption in recently built housing units. These estimation procedures were not applied 

to all data for the 1990 RECS, but they illustrate the potential for improving the precision of 

estimates used in specific kinds of analyses (see Chapter 7, subsection on "Special Estimation 

Procedures for New Homes").

There is no feasible direct method, in a national sample survey, of measuring the allocation of 

individual households' consumption of each fuel to different end uses like space heating and 

cooling, water heating, and various appliances. Nevertheless, it is important, for energy policy 

analysis and other purposes, to have estimates of consumption by major end-use category. 

Consequently, an indirect, model-based nonlinear regression method of end-use estimation has 

been developed for RECS (see Chapter 7, section on "End-use Estimation," for a detailed 

description). The independent variables for the model include many of the housing unit and 

household variables for which data are collected in the Household Survey, as well as heating and 

cooling degree-day estimates based on the temperature data obtained from NOAA. The end-use 

allocation model is also used to estimate total consumption of fuels for which no usable data are 

available from the Supplier Survey or other sources.

There have been many changes in the details of the end-use estimation methodology since it was 

first developed. In 1984 the original linear model was replaced by a nonlinear model, with the 

logarithm of the difference between actual and estimated consumption serving as the error term. 

For the 1990 RECS, the logarithmic error term was replaced by the difference between the fourth 

roots of estimated and actual consumption. In addition, there have been many changes in the 

content of the Household Survey questionnaires and hence in the data items available for use as 

independent variables in the model. Some new items have been added to the questionnaires 

primarily in hopes of reducing the mean squared error of the model estimates.

An early evaluation of the model was undertaken for housing units in apartment buildings as part 

of the 1981 RECS (Chapter 6, Section 6.2, "Evaluation of imputation procedures"). For 

apartment buildings with one or more RECS sample households, average measured consumption 

per housing unit was compared with values imputed for the sample households by using the

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Residential Energy Consumption Survey Quality Profile 161
model. This study indicated that the model-based estimates were low by about 50 percent for 

electricity in households with air- conditioning and for natural gas in households that used it only 

for purposes other than space heating. Based on these findings, the end-use model for the 1984 

RECS was modified to reflect differences between apartments and other units more explicitly.

Some more recent evaluations of the model have made use of special studies in which utility 

companies have used recently developed "submetering" procedures to measure the consumption 

of electricity for different purposes within a household (see Chapter 7, subsection on "Evaluation 

of End Use Estimation Procedures," and Table 7.2). Because of the limited scope of these 

studies, they do not permit definitive conclusions, but the results suggest that the model may have 

been overestimating the consumption of electricity for central arid room air-conditioning and 

space heating, and underestimating its consumption for water heating.

Sampling Error

Sampling errors are estimated by using a balanced half-sample replication method for all 

published RECS estimates (see Chapter 7, section on "Sampling Errors"). Through the 1982 

RECS, 32 half-sample replicates were used; subsequently, the number was increased, and 96 

replicates were used for the 1993 RECS estimates of sampling error. The estimates of sampling 

error for individual items are used as inputs to a generalized variance model which estimates "row 

and column factors" that are included in the publications.

As shown in Figure 7.3, the row and column factors allow users, with a few exceptions, -;o 

determine an approximate value of the sampling error for any cell in a table. Instructions in the 

introduction and appendices to the published reports explain the use of the row and column 

factors, including their use to derive estimates of standard errors for ratios and differences of 

individual table cells (see, for example, EIA 1995a, pp. 18-20 and Appendix B).

The individual records in the RECS public-use microdata files and diskettes do not include the 

information, such as primary sampling unit identifiers or replication weights, that would be 

needed to permit users to estimate sampling errors for the variables included in their analyses, 

Inclusion of such information would lead to an unacceptable risk that the identities of some 

sample households or housing units could be determined.

Estimates of sampling error have some limitations. They are themselves subject to sampling 

error. The use in RECS of a sample design which selects a single primary sampling unit from 

each stratum precludes the possibility of obtaining strictly unbiased estimates of sampling errors, 

The sampling error determined for a particular estimate by use of the published row and column 

factors is an approximation to the value that was calculated for that estimate. (Table 7.4 shows 

some comparisons of direct estimates and approximate values.) The sampling errors for estimates 

of end-use consumption do not reflect the error of estimation of the model parameters and are 

therefore underestimates (to a lesser extent, this is also true for total consumption and 

expenditures).

Energy Information Administration / Energy Consumption Series 

162 Residential Energy Consumption Survey Quality Profile
Targets for the standard errors of estimates of average energy consumption per household in the 

1993 RECS were set at 1.25 percent for the U.S. total, 2.75 percent for Census region totals, and 

4.50 percent for Census division totals. As shown in Table 7.4, the estimated sampling errors 

were well below these target values in every instance.

Current Research and Potential Design Changes

Planning for the 1996 RECS is proceeding. Consideration is being given to how best to take 

advantage of recent developments in computer-assisted techniques for survey data collection and 

processing, both to reduce costs and to improve quality. Computer-assisted personal interviewing 

(CAPI) will probably be the principal data collection mode for the 1996 RECS. Another change 

being explored is conversion to a modern automated survey-processing system, such as Blaise, 

a system developed by the Netherlands Central Bureau of Statistics. Findings from a recent study 

of edit changes (Martin 1995), based on comparisons of tabulations of edited and unedited data 

for selected items from the 1993 RECS, will be helpful in deciding what kinds of edit checks to 

include in a CAPI version of the questionnaire.

In order to reduce costs and also in anticipation of conducting surveys after 1996 primarily by 

telephone, the number of topics and individual questions in the 1996 RECS will be considerably 

smaller than in 1993. Physical measurement of the area of floor space will no longer be part of 

the survey interview. Other variables, such as number of heated rooms, will take the place of 

floor space in the end-use consumption model.

Looking beyond 1996, serious consideration is being given to using computer-assisted telephone 

interviewing (CATI) with random-digit dialing (RDD) as the principal mode of data collection. 

Use of this mode would require a careful evaluation of its effects on response rates and other 

aspects of data quality. One question of special significance for RECS is whether it will be 

possible, with some combination of telephone and mail procedures, to obtain authorization to 

contact energy suppliers for a sufficiently high proportion of the sample households. A pilot test 

of the use of CATI/RDD procedures for the collection of RECS data is under way, and 

preliminary results are expected to be available early in 1996.

Some Suggestions for Data Users 

User Options

The primary means of user access to RECS data are through publications and public-use data 

files. A full list of all EIA consumption survey publications and public-use files, along with 

instructions for obtaining them, is provided in Appendix A. The main publications for each 

RECS survey year are the reports on Housing Characteristics and on Household Energy 

Consumption and Expenditures, The latter is published in two volumes, the first containing 

national data and the second containing regional data. There have also been several special 

publications based on RECS, and some summary data from RECS are published annually in 

EIA's Annual Energy Review and in the Census Bureau's Statistical Abstract.

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Residential Energy Consumption Survey Quality Profile 163
Public-use data files for each survey year are available to users who wish to develop their own 

tabulations or do other kinds of statistical analyses. Files for the 1987 RECS and subsequent 

years can be obtained on diskettes or downloaded from the Internet; those for earlier years are 

available on tapes. The public-use files contain data for individual sample households, including 

billing data from the Supplier Survey, with all identifiers removed in order to preserve the 

confidentiality of individual, information. For the same reason, selected billing records with 

unusual values have been deleted, and random errors have been introduced for certain variables, 

such as degree-days and starting and ending dates of billing periods. Additional information. 

about steps taken to preserve confidentiality is provided with the documentation that accompanies, 

each public-use data tape or diskette.

Learning More About RECS

Each of the regular Housing Characteristics and Household Energy Consumption and 

Expenditures reports includes detailed appendices describing how the survey was conducted and 

discussing various aspects of the quality of the survey data. The reports also contain copies of 

the data collection forms used and an extensive glossary defining terms and concepts used in the 

survey. A detailed description of the sample design and selection procedures for all surveys 

through 1993 is contained in a 1994 report, Sample Design for the Residential Energy 

Consumption Survey (El A 1994).

All public-use data files include extensive internal documentation. The User's Guide for the 1993 

public-use files includes: information about the general nature of the survey; technical file 

specifications; variable listings, including information about variables that changed from the prior 

survey; unweighted and weighted frequencies for each variable; an explanation of the codss 

used for imputed variable values; copies of the questionnaires; and a list of "Cautions when 

Using RECS Data." The User's Guide also identifies persons to contact at El A for additional 

information.

The above sources, along with this Quality Profile, should meet the needs of most users, 

However, additional information that may lead to a fuller understanding of some aspects of the 

quality of RECS data is contained in internal operating manuals: for the surveys, including 

interviewer instruction manuals and, starting with the 1984 RECS, separate survey documentation 

reports covering sample design, data collection, and data processing procedures. The list of 

references cited in this report Includes several articles, contractor reports, and internal memoranda 

and reports, some of which may be of interest to a few data users,

Using Cross-sectional Data:: General Considerations

Effective use of data from, any survey requires knowledge of the basic features of the survey 

design and awareness of how sampling and nonsampling errors may affect conclusions drawn 

from the data. The following suggestions apply to all users of RECS data, whether they are 

working with published tabulations or public-use data files:

Energy Information Administration / Energy Consumption Series 

164 Residential Energy Consumption Survey Quality Profile
To obtain a general overview of the RECS objectives, content and design, review 

Chapter 2 of this report. It may also be useful to read the section of Chapter 3 on 

"RECS Target Populations," which provides information about the target populations 

and the reference periods and dates for each survey year.

For data elements of particular interest, review the specific questionnaire items relating 

to these topics and the relevant definitions given in the glossary of each published 

report.

Whenever possible, evaluate the statistical significance of any comparisons based on 

the survey data. For users of published data for 1984 and subsequent survey years, 

this can usually be accomplished by using the "row and column factors" appearing in 

each table according to the instructions provided. However, no row and column 

factors were provided for estimates of end-use consumption in the 1990 Consumption 

and Expenditures report.

Be aware of the possible effects of coverage, nonresponse, and measurement errors on 

the estimates. The most accurate data on consumption and expenditures are for 

electricity and natural gas, because most of the data for these fuels are obtained from 

billing records obtained in the Supplier Surveys. For the same reason, consumption 

data for single-family owner-occupied housing units are likely to be more accurate 

than data for multi-family units and those occupied by renters. Estimated totals for 

newly-constructed housing units those completed during the survey year and the years 

immediately preceding it are likely to be low because of problems entailed in 

incorporating new units into the sampling frame. Additional information about 

nonsampling errors and their effects can be found in Chapters 3 through 8 of this 

report and in appendices to the Housing Characteristics and Consumption and 

Expenditures reports.

Special Considerations for Users of Public-Use Files

Users working with public-use data files should, of course, review the documentation that is 

provided with them. Additional recommendations are to:

 Use weighted data for all tabulations. Several features of the sample design, such as 

oversampling of low-income households and newly-constructed housing units in some 

survey years, require the use of variable weights to produce unbiased estimates. For 

analytical uses of the data, such as multivariate analyses, users may sometimes find 

it more convenient to use unweighted data. However, before deciding to do so, it 

would be advisable to determine the extent of variability of the sample weights for the 

housing units to be included in the analysis.

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Residential Energy Consumption Survey Quality Profile 165
Some users may wish to exclude imputed variable values from their analyses or to 

reimpute the values using a different procedure. The data files contain information 

that allows users to determine which values were imputed. Consult the public-use file 

User Guide for specific information on how to do this.

Be aware of the possible effects of statistical disclosure limitation procedures that have 

been used to prevent data users from determining the identity of individual sample 

households. For survey years 1980, 1981, 1982, 1984, and 1993, records for sample 

households in Alaska and Hawaii were excluded from the public-use files. They were 

included in the public-use files for 1987 and 1990. but a substantial proportion of the 

billing records for households in the two States were excluded. More specific 

information is given in the documentation material that accompanies each file.

For the same reason, that is, to prevent disclosure, it has not been possible for the public-use files 

to include replication weights or other variables that would allow users to develop their own 

estimates of sampling error for items of interest. For most survey years, the most that users of 

these files can do is to obtain a range of possible values for the sampling error of a particular 

estimate by calculating standard errors for similar items appearing in the survey publications for 

the same year. For survey years 1981 and 1982 however, each of the publications includes an 

appendix with a generalized procedure for deriving an approximate sampling error for any item 

of interest.

Analyzing Changes Over Time

There have been many changes, since the initial NIECS effort in 1978, to the RECS survey 

design, content, and procedures. These changes, which are described in Chapter 2, in the section. 

on "Evolution of the RECS Design: 1978-1993," have been motivated by efforts to respond, to 

new data needs, improve the quality of the data, and take advantage of new technologies for 

survey data collection and processing. RECS data users who are interested in analyzing trends 

in housing unit characteristics and energy consumption need to be aware of these changes and 

their possible effects on comparisons of data for different survey years. The most important 

features to keep in mind are:

 An upper bound to the sampling error of the difference between estimates of the same 

item for different survey years can be obtained by assuming that the two estimates are 

independent. With this assumption, the appropriate formula is:

where X, and X2 are the estimates for times one and two. Because the estimates for 

different survey years are positively correlated in most instances, the value derived 

from this formula will be an overstatement. For information about the extent of the 

overstatement, see Chapter 7, section on "Sampling Errors," and Table 7.3.

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166 Residential Energy Consumption Survey Quality Profile
For the first two survey years, 1978 and 1979. Alaska and Hawaii were not included 

in the target population for the survey. They have been included in all subsequent 

survey years, but, as noted above, all or some of the individual records for sample 

housing units in these two States have been excluded from public-use data files.

The reference month for household counts was November for all survey years through 

1990; in the 1993 RECS, it was changed to July. Through the 1984'RECS, the 12- 

month reference period for consumption and expenditures ran from April of the survey 

year through March of the following year. From the 1987 survey year on, the 

reference period has been the calendar year corresponding to the survey year (see 

Figure 2.2).

Consumption data for fuel oil and kerosene were combined through the 1982 survey 

year; since 1984, data for the two fuels have been collected and presented separately.

In the 1987 RECS, a significant change was made in the method of associating 

weather data (heating and cooling degree-days) with sample housing units. 

Consequently, weather data from 1987 to date are not comparable with data for earlier 

survey years (see Chapter 5, section on "Comparisons of Individual Household Data 

from Alternate Sources," and Table 5.8).

Since its initial development, there have been frequent changes in the end-use 

consumption model which is used to allocate total consumption to specific end-uses 

and to impute total consumption when billing or delivery data are unavailable. There 

have been several significant structural changes in the model and. in virtually every 

survey year, changes in the data items used as independent variables. It is believed 

that most of these changes have significantly improved the reliability of cross-sectional 

estimates of end-use consumption, but, at the same time, they constitute an additional 

source of error in estimates of change between survey years.

Estimates of end-use consumption are available for all survey years except 1979 in the 

following categories:

Category Fuels

Space heating All fuels

Water heating All fuels

Appliances All fuels

Air-conditioning Electricity and natural gas

For electricity, in 1990 the appliance category was subdivided into refrigerators, 

freezers, and all other appliances. In 1993, the all other category was further 

subdivided to provide separate estimates for lighting, cooking, dishwashers, and clothes 

dryers.

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Residential Energy Consumption Survey Quaiity Profile 167
Users who are interested in analyzing long-term trends in residential energy 

consumption should keep in mind the likely effects of short-term fluctuations in 

average temperatures during the heating and cooling seasons, whether at the national. 

regional, or divisional level. In the same vein, analyses of variation across regions or 

divisions may be influenced by departures from long-term averages that differ in 

direction from one area to another. Trends in expenditures can be affected by 

fluctuations in both average temperature and energy prices. The 1993 Consumption 

and Expenditures report presented, for the first time, trend data for consumption and 

expenditures adjusted to control for the effects of price changes and variations in 

weather (EIA 1995d, pp. 3-7).

A final suggestion for analysis of changes over time is to review the specific questions 

used in each survey year for the items included in the analysis in order to determine 

whether there have been any changes in wording, format, or placement that may have 

affected comparability between survey years. As noted above, the User's Guide for 

the 1993 public-use files includes complete information on variables that have changed 

from the previous survey.

User Feedback

EIA and the staff responsible for RECS are anxious to hear from data users. Let us know about 

your experiences in using the data, any problems you may have encountered, and you;: 

suggestions for improving the quality and utility of RECS data. Please contact Robert Latta b;y 

telephone (202/586-1385) or E-Mail (rlatta@eia.doe.gov).

Energy Information Administration / Energy Consumption Series 

168 Residential Energy Consumption Survey Quality Profile
Appendix A

References

Allied Technology Group, Inc. (1995). Revised Analysis Report: Comparison of Data from Energy 

Consumption and Supply Surveys, Task Assignment No. 94-004. Rockville, MD, March 21.

Battles, Stephanie J. (1990). Comparison Between Residential End-Use Submetering and Conditional 

Demand Estimates for a National Survey, Presented at the ACEEE 1990 Summer Study on Energy 

Efficiency in Buildings.

Battles, Stephanie J. (1991 a). Comparison of Short Run Changes in Residential Energy Consumption 

and Expenditures in Black andNonblack Households, Presented at the Socioeconomic Energy Research 

and Analysis Conference, June 28.

Battles, Stephanie J. (1991b). Technical Note No. 32. Effects of the Adjustment of 1990 Census Data 

on the 1990 RECS Control Totals Obtained from the Current Population Survey, December 30.

Battles, Stephanie J. and Harrison, Ivy M. (1992). Setback and Discrete Temperature Behavior in the 

United States as Related to Housing Unit and Household Characteristics, Presented at the International 

Association of Energy Economics Conference, France.

Battles, Stephanie J. and Thompson, Wendel (1991). Technical Note No. 31. Observation of a 1990 

RECS Interviewer and the Implications for the 1993 RECS, December 30.

Biemer, Paul P. (1992). Brief evaluation of an early draft of the questionnaire for the 1993 RECS. 

Letter to Lynda Carlson, October 9.

Blumstein, Carl J., Kemp, William, McGuire, C. Bart, Valentine, Susan D., and York, Carl M. (1982). 

Supplemental Studies of the National Interim Energy Consumption Survey (NIECS), ERG-82-4, Energy 

Resources Group. University of California, Berkeley, July.

Blumstein, Carl J., York, Carl M., and Kemp, William (1981). An Assessment of the National Interim 

Energy Consumption Survey, ERG-81-1, Energy and Resources Group. University of California, 

Berkeley, July.

Branch, E. Raphael (1994). "The Consumer Expenditure Survey: A Comparative Analysis." Monthly 

Labor Review, U.S. Department of Labor, December.

Carlson, Lynda T. (1985). Letter to Arthur Young, Chief, Housing Division. Bureau of the Census, 

July 18.

Carroll, David (1987). "RECS Tabulations Report." Memorandum to Martha Johnson, March 5.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 169
Cox, Brenda G. (1995). Transcript of remarks at meeting of the American Statistical Associate*^ 

Committee on Energy Statistics. Washington, DC, April 21.

Daniels, Sally (1992). "Draft of Report of Lighting Questions Pretest." Memorandum to Wended 

Thompson, November 16.

Day, Dawn (1982). "Discrepancies Between Day and Night Temperatures in RECS-2." Memor.and.Mtri 

to Wendel Thompson, November 3.

Energy Information Administration (1980). Residential Energy Consumption Survey: Consumption ana 

Expenditures, April 1978 Through March 1979, Part 1: National Data. DOE/EIA-0207/5. Washington 

DC, July 1980.

Energy Information Administration (1981). Residential Energy Consumption Survey: 1979-19^0 

Consumption and Expenditures, Part 1: National Data. DOE/EI A-0262./1. Washington, DC, April 198 !

Energy Information Administration (1982a). Residential Energy Consumption Survey: Homir^ 

Characteristics, 1980. DOE/EIA-0314(80). Washington, DC, June 1982.

Energy Information Administration (1982b). Residential Energy: Consumption Survey: Housing 

Characteristics, 1980-1981. DOE/EIA-0321(80). Washington, DC, September 1982.

Energy Information Administration (1983a). Residential Energy Consumption Survey: Ho-usw% 

Characteristics, 1981. DOE/EIA-0314(81). Washington, DC, August 1983.

Energy Information Administration (1983b). Residential Energy Consumption Survey: Housing 

Characteristics, 1981-1982. DOE/EIA-0321(81). Washington, DC,'September 1983.

Energy Information Administration (1983c). Residential Energy Consumption Survey: Regression 

Analysis of Energy Consumption by End Use, DOE/EIA-0431. Washington, DC, October 1983.

Energy Information Administration (1984a). Residential Energy Consumption Survey: Housing 

Characteristics, 1992. DOE/EIA-0314(82). Washington, DC, August 1984.

Energy Information Administration (1984b). Residential Energy Consumption Survey: Housing 

Characteristics, 1982-1983. DOE/EIA-0321(82). Washington, DC, November 1984.

Energy Information Administration (1984c). Residential Energy Consumption and Expenditures by End 

Use for 1978, 1980, and 198L DOE/EIA-0458. Washington, DC, December 1984.

Energy Information Administration (1985). Technical Documentation, Public Use Tape, 198't 

Residential Energy Consumption Survey and 1983 Residential Transportation Energy Consumption 

Survey, Washington, DC.

Energy Information Administration (1986a). An Assessment of the Quality of Selected EIA Data Sen**:: 

Energy Consumption Data, DOE/EIA-0292(85). Washington, DC." April 2.

Energy Information Administration / Energy Consumption Series 

170 Residential Energy Consumption Survey Qualify Profile
Energy Information Administration (1986b). Nonresidential Buildings Energy Consumption Survey: 

Consumption and Expenditures 1985, DOE/EIA-0318(83), Washington, DC, September 1986.

Energy Information Administration (1986c). Residential Energy Consumption Survey: Housing 

Characteristics 1984, DOE/EIA-0314(84). Washington, DC, October 1986.

Energy Information Administration (1987a). Residential Energy Consumption Survey: Consumption and 

Expenditures April 1984 Through March 1985, Part 1: National Data, DOE/EIA-0321(84). 

Washington, DC, March 1987.

Energy Information Administration (1987b). Residential Energy Consumption Survey: Consumption and 

Expenditures April 1984 Through March 1985, Part 2: Regional Data, DOE/EIA-0321(84). 

Washington, DC, May 1987.

Energy Information Administration (1987c). Residential Energy Consumption Survey: Trends in 

Consumption and Expenditures 1978-1984, DOE/EIA-0482. Washington, DC, June 1987.

Energy Information Administration (1987d), Technical Documentation, Public Use Tape: 1984 

Residential Energy Consumption Survey and 1985 Residential Transportation Energy Consumption 

Survey. Washington, DC.

Energy Information Administration (1989a) Housing Characteristics 1987, DOE/EIA-0314(87). 

Washington, DC, May 1989.

Energy Information Administration (1989b). Household Energy Consumption and Expenditures 1987, 

National Data, DOE/EIA-0321(87). Washington, DC, October 1989.

Energy Information Administration (1989c). An Assessment of the Quality of Selected EIA Data Series: 

Electric Power Data, DOE/EIA-0292(87). Washington, DC.

Energy Information Administration (1990). Energy Consumption by End-Use Sector, A Comparison of 

Measures by Consumption and Supply Surveys, DOE/EIA-0533. Washington, DC, April 6, 1990.

Energy Information Administration (1992). Housing Characteristics 1990, DOE/EIA-0314(90). 

Washington, DC, May 1992.

Energy Information Administration (1993a). Household Energy Consumption and Expenditures 1990, 

DOE/EIA-0321(90). Washington, DC, February 1993.

Energy Information Administration (1993b). Supporting Statement for Forms E1A-457 A-H: Residential 

Energy Consumption Survey, Submitted to the Office of Management and Budget for clearance of the 

1993 RECS, April 2, 1993.

Energy Information Administration (1993c). User-Needs Study for the 1993 Residential Energy 

Consumption Survey, DOE/EIA-0555(93)/2, Washington, DC, September 1993.

Energy Information Administration / Energy Consumption Series

Residentlat Energy Consumption Survey Quality Profile 171
Energy Information Administration (1994). Sample Design for the Residential Energy Consumption 

Survey, DOE/EIA-0555(94)/1, Washington, DC, August 1994.

Energy Information Administration (1995a). Housing Characteristics 1993, DOE/EIA-03I4(9T 

Washington, DC, June 1995.

Energy Information Administration (1995b). Buildings and Energy in the 1980's, Energy Consump1:i;:r 

Series, DOE/EIA-0555(95)/1. Washington, DC, June 1995.

Energy Information Administration (1995c). 1993 Residential Energy Consumption Survey: Public l.'.w 

Diskettes, User's Guide, Washington, DC, July 1995.

Energy Information Administration (1995d). Household Energy Consumption and Expenditures 1993 

DOE/EIA-0321(93). Washington, DC, October 1995.

Energy Information Administration (1995e). Special tabulations showing energy consumption by fud 

building type and basis for annual estimate, 1990 and 1993 Residential Energy Consumption Surve;;/s

Energy Information Administration (1995f). Special tabulation showing the numbers of sample houshi x 

units added by the half-open interval for the 1984, 1987 and 1990 Residential Energy Consumption 

Surveys.

Energy Information Administration (1995g). State Energy Data Report, 1993, DOE/EIA-0214(93;. 

Washington, DC, July 1995.

Erickson, Rita J. (1984). Letter to Dr. Dawn Day, Response Analysis Corporation, March 6.

Gonzalez, Maria E., Kasprzyk, Daniel, and Scheuren, Fritz (1994). "Nonresponse in Federal Surveys: 

An Exploratory Study," Amstat News, No. 208, April.

Groves, Robert M. (1989). Survey Errors and Survey Costs, New York: John Wiley & Sons, Inc.

Hall, Christy (1992). Technical Note No. 33, "Nameplate Data Collection in the 1990 Residential 

Energy Consumption Survey." March 18.

Harrison, Ivy (1993). "Nonlinear Regression Analysis 1990 RECS Energy Consumption Data," draft. 

1993.

Hersey, James C. and McCarthy, John (1986). Analysis of Response Rates in Residential Energy 

Consumption Surveys, Final Report, SRA Technologies, Inc., June 1986.

Jabine, Thomas B. (1987). Review of Computer Edit and Update Performance Statistics far the 

Residential Energy Consumption Survey, Final Report, Washington, DC, December 1987.

Jabine, Thomas B. (1993). "The EIA RECS Household Energy Consumption Data for 1990," 

Independent Review submitted to the Energy Information Administration, September 30, 1993.

Energy Information Administration / Energy Consumption Series 

172 Residential Energy Consumption Survey Quality Profile
Jabine, Thomas B. (1994). Quality Profile for SASS, NCES 94-340, National Center for Education 

Statistics, U.S. Department of Education, 1994.

Jabine, Thomas B. (assisted by King, Karen E. and Petroni, Rita J.) (1990). SIPP Quality Profile, 

Bureau of the Census, U.S. Department of Commerce, 1990.

Latta, Robert B. (1988). Technical Note No. 7, Square Footage Imputations for RECS, July 13, 1988. 

Latta, Robert B. (1993). Draft Technical Note, Poststratification Estimation, May 7, 1993.

Latta, Robert B. (1994). Analysis of Listing Undercount and Other Under count Factors for RECS, 

Technical Note, October 28, 1994.

Leach, Nancy L. (1991). "An Assessment of the Use of Videotape Interviewer Training for the 

Residential Energy Consumption Survey (RECS): The First Step." Presented at meeting of the 

American Statistical Association Committee on Energy Statistics, Washington, DC, October 24-25, 

1991.

Luyben, Paul D. (1982). "Prompting Thermostat Setting Behavior," Environment and Behavior, Vol. 

14, No. 1:113-128, January 1982,

Martin, Antoinette W. (1995). "An Examination of the Effects of the Automated Edits on Housing 

Characteristics for 1993," unpublished report, Energy Information Administration, November.

Miller, Renee (1995). "Why do Consumption Estimates Differ?", presented at the meeting of the 

American Statistical Association Committee on Energy Statistics Committee, Washington, DC, June 

23, 1995.

Miksovic, Ann (1989). "Results of the Field Pretest of the RECS-90 Questionnaire." Memorandum to 

Robert Latta, June 20, 1989.

Mooney, Geraldine M. and Can-oil, David (1986). "The 1986 Weather Methodology Evaluation," 

Princeton, NJ: Response Analysis Corporation, December 1986.

Response Analysis Corporation (1980). "Residential Energy Consumption Survey: Final Report on 

Study Design and National Interim Energy Consumption Survey," April 30, 1980.

Response Analysis Corporation (1983). "RECS/CPS Differences in Estimated Number of One person 

Households, Notes for Discussion," Memorandum, October 26, 1983.

Response Analysis Corporation (1987). "RECS/RTECS Methodological Study." Draft report, Princeton, 

NJ, November 24, 1987.

Response Analysis Corporation (1988). "Comparison of RECS4 End Use Estimates Using Individual 

Weather Station Data and Division Average Weather Station Data." Princeton, NJ, August 23, 1988.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 173
Response Analysis Corporation (1991). "Quality Assessment of Videotape Training: Conclusions ard 

Recommendations," September 30. 1991.

Response Analysis Corporation (1992a). "1990 Residential Energy Consumption Sample Desig::: 

Procedures Manual," Princeton, NJ, March 1992.

Response Analysis Corporation (1992b). "1990 Residential Energy Consumption Survey Data Edit THI 

and Manipulation Procedures Manual," Princeton, NJ, September 1992.

Response Analysis Corporation (1992c). "End-Use Validation Study: Draft Report on Modified 

Models," Princeton, NJ, August 1992.

Response Analysis Corporation (1992d). "End-Use Validation Study: Final Report on Findings." 

Princeton, NJ, December 1992.

Response Analysis Corporation (1994). Characterizing the Impact of Energy Expenditures on Low 

Income Households: An Analysis of Alternative Energy Burden Statistics: Technical Report, Submitted 

to Energy Information Administration, Second draft, May 1994.

Response Analysis Corporation (1995a). 1993 Residential Energy Consumption Sample Desist; 

Procedures Manual, Princeton, NJ, 1995.

Response Analysis Corporation (1995b). 1993 Residential Energy Consumption Survey Data Collection 

Procedures Manual, Princeton, NJ, 1995.

Response Analysis Corporation (1995c). 1993 Residential Energy Consumption Survey Data Editing 

and Manipulation Procedures Manual, Princeton, NJ, 1995.

Slider, Chuckle (1995). Memorandum to Wendel Thompson, Energy Information Administration,, 

"RECS Quality Profile," Response Analysis Corporation, November 20.

Thompson, Wendel (1982). Letter to Reuben Cohen, Response Analysis Corporation, regarding 

apparently inconsistent reporting of indoor temperatures, September 15, 1982.

Thompson, Wendel (1985a). "Residential Energy Consumption Survey: From a 2-Year to a 3-Ye;;ir 

Cycle." Presented at meeting of the American Statistical Association Committee on Energy Statistic-:, 

Washington, DC, October 24-25, 1985,

Thompson, Wendel (1985b). "Utility of Paying Respondents: Evidence from the Residential Energy 

Consumption Surveys." Presented at American Association for Public Opinion Research Arum;u 

Conference, May 17, 1985.

Thompson, Wendel (1987). "End-Use Consumption of Residential Energy," Monthly Energy Review1 ', 

pp. vii-xiv, July 1987.

Energy Information Administration / Energy Consumption Series 

174 Residential Energy Consumption Survey Quality Profile
Thompson, Wendel (1994a). "Results of Trip to Los Angeles." Internal memorandum to Nancy Leach, 

February 25, 1994.

Thompson, Wendel (1994b). "Notes for 1993 RECS Reports." Selected pages from a memorandum to 

the files, August 2, 1994.

Windell, Phillip A. (1986). "The ELCAP Residential Base Study Sample: A Basic Characterization," 

Proceedings from the ACEEE 1986 Summer Study on Energy Efficiency in Buildings: Program 

Evaluation, American Council for an Energy-Efficient Economy, August 1986.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 175
Appendix B

EIA on Energy 

Consumption

For information about how to obtain these 

publications, see the inside cover of this 

report. Please note that the prices quoted 

here are subject to change.

In addition to the reports listed below, 

public-use data tapes and data diskettes for 

the residential, residential transportation, and 

commercial sectors are available from the 

National Technical Information Service 

(NTIS). To obtain information on how to 

order the tapes/diskettes, you may call NTIS 

at 703-487-4807, FAX number 703-321- 

8547. Data diskettes can also be obtained 

from the Office of Scientific and Technical 

Information (OSTI). For OSTI ordering 

information, call 615-576-8401.

Residential Sector

Housing Characteristics

Note: The survey name was dropped from 

the beginning of the report title starting with 

the 1987 data reports.

Housing Characteristics, 1993; June 1995, 

DOE/EIA-0314(93), GPO Stock No. 061- 

003-00912-3, $23.00.

Housing Characteristics 1990; May 1992, 

DOE/EIA-0314(90), GPO Stock No. 

061-003-00754-6, $23.00.

Housing Characteristics 1987; May 1989, 

DOE/EIA-0314(87), GPO Stock No. 

061-003-00619-1, $13.00.

Residential Energy Consumption Survey: 

Housing Characteristics 1984; October 1986, 

DOE/EIA-0314(84), GPO Stock No. 

061-003-00499-7, $12.00.

Residential Energy Consumption Survey: 

Housing Characteristics, 1982; August 1984, 

DOE/EIA-0314(82), GPO Stock No. 

061-003-00393-1, $7.00.

Residential Energy Consumption Survey 

Housing Characteristics, 1981; August 1983, 

DOE/EIA-0314(81), GPO Stock No. 

061-003-00330-3, $6.50.

Residential Energy Consumption Survey: 

Housing Characteristics, 1980; June 1982, 

DOE/EIA-0314, GPO Stock No. 

061-003-00256-1, $11.00.

Residential Energy Consumption Survey: 

Characteristics of the Housing Stock and 

Households, 1978; February 1980, 

DOE/EIA-0207/2, GPO Stock No. 

061-003-00093-2, $4.25.

Residential Energy Consumption Survey: 

Conservation; February 1980, 

DOE/EIA-0207/3, GPO Stock No. 061- 

003-00087-8, $6.00.

Preliminary Conservation Tables from the 

National Interim Energy Consumption 

Survey; August 1979, DOE/EIA-0193/P (no 

GPO Stock No.).

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 177
Characteristics of the Housing Stock and 

Households: Preliminary Findings from the 

National Interim Energy Consumption 

Survey; October 1979, DOE/EIA-0199/P 

(no GPO Stock No. available).

Consumption and Expenditures

Note: The survey name was dropped from 

the beginning of the report title starting with 

the 1987 data reports. The titles were 

changed to Household Energy Consumption 

and Expenditures 1987, Part 1: National and 

Part 2: Regional.

Household Energy Consumption and 

Expenditures 1993; October 1995, 

DOE/EIA-0321(93), GPO Stock No. 061- 

005-00932-8, $21.00.

"Household Energy Consumption and 

Expenditures 1990," Monthly Energy Review, 

August 1993, DOE/EIA-0035(93/08).

Household Energy Consumption and 

Expenditures 1990; February 1993, 

DOE/EIA-0321/1(90), GPO Stock No. 061- 

003-00795-3, $22.00.

Household Energy Consumption and 

Expenditures 1990\S; DOE/EIA-0321/2(90), 

GPO Stock No. 061-003-00796-1, $21.00.

Household Energy Consumption and 

Expenditures 1987, Part 1: National Data; 

October 1989, DOE/EIA-0321/1(87), GPO 

Stock No. 061-003-00635-3, $15.00. Note: 

Energy end-use data are included in this 

report.

Household Energy Consumption and 

Expenditures 1987, Part 2: Regional Data; 

DOE/EIA-0321/2(87) (no GPO Stock No. 

available), $16.00.

Residential Energy Consumption Survey 

Consumption and Expenditures, April 198-' 

Through Match 1985, Part 1: National 

Data; March 1987, DOE/EIA-0321/l(84i. 

GPO Stock No. 061-003-00519-5, $9.50.

Residential Energy Consumption Survey 

Consumption and Expenditures, April 198--< 

Through March 1985, Part 2: Regionc! 

Data; May 1987, DOE/EIA-032 1/2 (841 

GPO Stock. No. 061-003-00528-4, $17.00. 

Note: Energy end-use data are included ii: 

this report.

Residential Energy Consumption Survey 

Consumption and Expenditures, April 198': 

Through March 1983, Part I: Naiionci 

Data; November 1984, DOE/EIA-C321/1(82), GPO StockNo. 061-003-0041 1-3., 

$7.00.

Residential Energy Consumption Survey. 

Consumption and Expenditures, April j9S'- 

Through March 1983, Part 2: Regions! 

Data; December 1984, DOE/EIA-0- 

321/2(82), GPO Stock No. 061-003-004 14-!!!, 

$9.50.

Residential Energy Consumption Svrvey. 

Consumption and Expenditures, April 1981. 

Through March 1982, Part 1: Nafionti! 

Data; September 1983, DOE/EIA-0-- 

321/1 (81), GPO StockNo. 061-003-00340-.. . $6.00.

Residential Energy Consumption Survey: 

Consumption and Expenditures, April J98i 

Through March 1982, Part 2: Regional 

Data; October 1983, DOE/EIA-032 1 /2(8 1; , 

GPO Stock No. 061-003-00357-5, $8.

Residential Energy Consumption Surve^ 

Consumption and Expenditures, April 1 98'.* 

Through March 1981, Part 1: National 

Data; September 1982, DOE/EIA-032]/ 

1(80), GPO Stock No. 061-003-00278-'., 

$7.50.

178

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Residential Energy Consumption Survey: 

Consumption and Expenditures, April 1980 

Through March 1981, Part 2: 

Regional Data; June 1983, DOE/EIA0321/2(80), GPO Stock No. 061-003- 

00319-2, $7.00.

Residential Energy Consumption Survey: 

1979-1980 Consumption and Expenditures, 

Part 1: National Data (Including 

Conservation); April 1981, DOE/EIA0262/1, GPO Stock No. 061-003-00191-2, 

$6.50.

Residential Energy Consumption Survey: 

1979-1980 Consumption and Expenditures, 

Part II: Regional Data; May 1981, 

DOE/EIA-0262/2, GPO Stock No. 

061-003-00189-1, $8.50.

Residential Energy Consumption Survey: 

Consumption and Expenditures, April 1978 

Through March 1979; July 1980, 

DOE/EIA-0207/5, GPO Stock No. 

061-003-00131-9, $7.50.

Single-Family Households: Fuel Oil 

Inventories and Expenditures: National 

Interim Energy Consumption Survey; 

December 1979, DOE/EIA-0207/1, GPO 

Stock No. 061-003-00075-4, $3.50.

Other Publications on the Residential 

Sector

Energy Consumption Series Sample Design 

for the Residential Energy Consumption 

Survey, August 1994, DOE/EIA-0555(94)/1, 

GPO Stock No. 061-003-00865-8, $6.50.

Energy Consumption Series User-Needs 

Study of the 1993 Residential Energy 

Consumption Survey, September 1993, 

DOE/EIA-0555(93)/2, GPO Stock No. 061- 

003-00819-4, $13.00.

"End-Use Consumption of Residential 

Energy" Monthly Energy Review (Article), 

pp. vii-xiv, July 1987, DOE/EIA-0035 

(87/07).

Residential Energy Consumption Survey: 

Trends in Consumption and Expenditures 

1978-1984 June 1987, DOE/EIA-0482, GPO 

Stock No. 061-003-00535-7, $12.00.

Residential Conservation Measures; July 

1986, SR/EEUD/86/01 (no GPO Stock No.).

An Economic Evaluation of Energy 

Conservation and Renewable Energy Tax 

Credits; October 1985, Service Report (no 

GPO Stock No.).

Residential Energy Consumption and 

Expenditures by End Use for 1978, 1980, 

and 1981; December 1984, DOE/EIA-0458, 

GPO Stock No. 061-003-00415-6, $4.50.

Weatherization Program Evaluation, 

SR-EEUD- 84-1; August 1984 (available 

from the Office of the Assistant Secretary 

for Conservation and Renewable Energy, 

Department of Energy).

Residential Energy Consumption Survey: 

Regression Analysis of Energy Consumption 

by End Use; October 1983, DOE/EIA-0431, 

GPO Stock No. 061-00300-347-8, $5.00.

National Interim Energy Consumption 

Survey: Ex- ploring the Variability In 

Energy Consumption; 

DOE/EIA-0272, GPO 

061-003-00205-6, $5.00.

July 1981, 

Stock No.

National Interim Energy Consumption 

Survey: Ex-ploring the Variability in Energy 

Consumption—A Supplement; October 1981, 

DOE/EIA-0272/S, GPO Stock No. 

061-003-00217-0, $4.50.

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile 179
Energy Use by U.S. Households; November 

1980, DOE/EIA-0248 (brochure, no GPO 

Stock No.).

Cross-Sector

Energy Consumption Series-Measuring 

Energy Efficiency in the United States 

Economy: A Beginning, October 1995, 

DOE/EIA-0555(95)/2, GPO Stock No. 061- 

003-00935-2, $6.50.

Energy Consumption Series-Buildings and 

Energy in the 1980's, June 1995, DOE/EIA0555(95)71, GPO Stock No. 061-003-00914- 

0, $6.00.

Energy Consumption by End-Use Sector: A 

Comparison of Measures by Consumption 

and Supply Surveys; April 6, 1990, 

DOE/EIA-0533 (no GPO Stock No. 

available), $2.50.

Natural Gas: Use and Expenditures; April 

1983, DOE/EIA-0382, GPO Stock No. 

061-003-00307-9, $5.50.

Public-Use Tapes

Note: All tapes are available through the 

NTIS.

Residential and Residential 

Transportation Sectors

Residential Energy Consumption Survey: 

1987 and Residential Transportation Energy 

Consumption Survey, 1988, Order No. PB90- 

501461, $220.

Residential Energy Consumption Survey: 

1984 and Residential Transportation Energy 

Consumption Survey, 1985; Order No. 

PB87-186540, $220.

Residential Energy Consumption Survey: 

1982 and Residential Transportation Energy 

Consumption Survey, 1983; Order N::, 

PB85-221760, $220.

Residential Energy Consumption Survey: 

Consumption and Expenditures, 1980-198.\: 

Monthly Billing Data; Order N>:,

PB84-I66230, $220.

Residential Energy Consumption Survey: 

Housing Characteristics, 1981: 

Consumption and Expenditures, 1981-198 2: 

Monthly Billing Data; Order No. PB84-I20476, $220.

Residential Energy Consumption Survey • 

Housing Characteristics, Annualized 

Consumption and Expenditures, 1980-198 • ; 

Order No. PB83-199554, 

$220.

Residential Energy Consumption Survey 

Household Transportation Panel Monthly 

Gas Purchases and Vehicle and Household 

Characteristics, 6/79-9/81; Order No.

PB84-162452, $220.

Residential Energy Consumption Survey: 

Household Screener Survey, 1979-'980; 

Order No. PB82-114877, $220.

Residential Energy Consumption Survey: 

Household Monthly Energy Consumption ana 

Expenditures, 1978-1979; Order N:>, 

PB82-114901, $220.

National Interim Energy Consumption Surv \-.y 

(Residential), 1978; Order No. PB81-108711 

$220.

180

Energy Information Administration / Energy Consumption Series 

Residential Energy Consumption Survey Quality Profile
Public-Use Diskettes

Note: Diskettes are available through the Residential Energy Consumption Survey 

Office of Scientific and Technical 1987 Data, OSTI - ASCII or dBase format, 

Information (OSTI) and NTIS. order by title, $10 per diskette, $40 set of

four. NTIS - ASCII format: Order No. PBResidential Energy Consumption Survey 91-505115, $130, and dBase format: Order 

1990 Data, OSTI-ASCII (3 diskettes) or No. PB-91-505107, $130. 

dBase (2 diskettes) format, order by title, 

$10.00 per diskette, NTIS-ASCII format, 

Order No. PB93-506103 or dBase format, 

Order No. PB93-506095.

Energy Information Administration / Energy Consumption Series

Residential Energy Consumption Survey Quality Profile 181
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