APPENDIX C: QUALITY OF THE DATA
APPENDIX C 

APPENDIX C 

QUALITY OF THE DATA

INTRODUCTION 

This section discusses several issues relating to the quality of the National Household Travel 

Survey (NHTS) data and to the interpretation of conclusions based on these data. In particular, 

the focus of our discussion is on the quality of specific data items, such as the fuel economy and 

fuel type, that were imputed to the NHTS via a cold-decking imputation procedure. This

imputation procedure used vehicle-level information from the NHTSA Corporate Average Fuel

Economy files for model year’s 1978 through 2001. It is nearly impossible to quantify directly

the quality of this imputation procedure because NHTS does not collect the necessary fuel 

economy information for comparison. At best, we have indirect evidence on the quality of our

imputations, which is addressed in the following sections. Indeed, such an imputation procedure 

could be vastly improved with the collection of Vehicle Identification Number (VIN), fuel type 

and retail fuel price for each sample vehicle. However, those collections may represent an 

unreasonable burden on NHTS respondents. 

The quality of the data collection and the processing of the data affect the accuracy of 

estimates based on survey data. All the statistics published in this appendix, such as total vehiclemiles traveled (VMT), are estimates of population values. These estimates are based on 

observations from a randomly chosen subset of the entire population of occupied housing units. 

Consequently, the estimates always differ from the true population values. Because the NHTS is a 

sample survey, data from the survey are subject to various sources of nonsampling and sampling

error. 

Nonsampling error is a measure of variability due to the execution and processing of the 

survey. These errors can include: population undercoverage during sampling; questionnaire 

wording and format; response bias and variance; interviewer error; coding and/or keypunching 

error; and nonresponse bias. Nonsampling errors are treated in several sections of this appendix. 

The main section pertains to the imputation procedures used for “missing” fuel economy, fuel

type, and fuel economy adjustments. In the previous sections, fuel economy adjustments were 

addressed. This section deals mainly with imputing fuel economy or MPGi(EPA 55/45) to each

appropriate sample vehicle. 

NONSAMPLING ERROR 

Nonsampling errors are due to the conduct of the survey, and include both random errors and

systematic errors or biases. The magnitudes of nonsampling biases cannot be estimated from the 

sample data. Thus, avoidance of systematic biases is a primary objective of all stages of survey 

design. Subsequent to conducting a survey, problems of unit nonresponse and item nonresponse 

need to be addressed. 

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In surveys with complex questionnaires and procedures, such as the NHTS, the final dataset

reflects fundamental approaches taken in the data collection and editing processes. For the 2001 

NHTS, two approaches may have had considerable impact on the resulting data. 

The first is the reluctance to impute data. If the respondent did not answer an item, its value 

was generally not imputed, (i.e., determine what the logical response would be given the response 

to other items). Carefully performed imputation has its place in many statistical surveys, however

Westat and U.S. DOT determined that imputation would be limited in the NHTS data. If data 

were imputed, an imputation/edit flag was set for the variable to indicate the values that were

imputed. The treatment in the NHTS of these types of errors is discussed in 3-D.3. APPROACH 

TO POST INTERVIEW EDITING of the NHTS User’s Guide. 

Supplemental data, by definition, are 100 percent imputed. Thus, it is important that EIA 

thoroughly present the approach used to impute energy-related supplemental NHTS data (see 

Appendix B).

UNIT NONRESPONSE 

Unit nonresponse is the type of nonresponse that occurs when no data are available for an 

entire sampled household. The respondent being unavailable or the respondent’s refusal to 

cooperate causes most unit nonresponse cases. See the NHTS User’s Guide, CHAPTER 4.

SURVEY RESPONSE RATES, for further details on unit nonresponse. 

IMPUTATION PROCEDURES FOR SUPPLEMENTAL DATA

Imputation procedures fill in the gaps of “missing” data. Item nonresponse occurs when the 

respondents do not know the answer or refuse to answer a question, or when an interviewer does 

not ask a question or does not record an answer. Or, as in the case of this appendix, item

nonresponse occurs when a question was not asked, such that imputation procedures are required 

to address the need to append supplemental data to a pre-existing file from other external, but 

related, files. As already mentioned, NHTS took a conservative approach to item nonresponse. 

For supplemental data, in an effort to facilitate "full-sample" data analyses, imputations were 

made to provide the most probable responses when responses were “missing.” For linking

supplemental data, a pseudo cold-decking imputation was employed. Figure C1 depicts the colddeck approach, using NHTS make, model, model year, and vehicle type information to “match” 

with eligible donors from the NHTSA CAFE files. 

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Figure C1. Schematic for Linking or Matching a NHTS Sample Vehicle to Eligible EPA/NHTSA 

Vehicles 

Matching: 1 to Many

Volkswagen, Jetta, 2001, Auto, ??.?

Volkswagen, Jetta, 2001, Auto, 44.8

Volkswagen, Jetta, 2001, Auto, 26.3

Volkswagen, Jetta, 2001, Auto, 52.6

Volkswagen, Jetta, 2001, Auto, 29.4

Volkswagen, Jetta, 2001, Auto, 29.2

Volkswagen, Jetta, 2001, Auto, 25.1

Volkswagen, Jetta, 2001, Auto, 31.7

Volkswagen, Jetta, 2001, Auto, 31.4

Volkswagen, Jetta, 2001, Auto, 31.3

Volkswagen, Jetta, 2001, Auto, 29.5

Source: EPA/NHTSA.

1

Many

MPG 55/45

Select One

Note: EPA – Environmental Protection Agency, NHTSA – National Highway Transportation Safety Administration.

COLD-DECK PROCEDURE

Because the fuel economy for a sampled vehicle could not be unequivocally determined by 

its NHTS-collected descriptors, a cold-deck imputation procedure was employed to “match” a 

NHTSA file record to a sample vehicle. A matching record was chosen from among the several 

applicable ones, with probability proportional to sales, using the sales figures on the NHTSA 

files. Once chosen, a record provided (1) EPA Composite MPG, (2) fuel metering, and (3) engine 

type. Although more attributes were available for selection, EIA limited its “donated” vehicle 

attributes to those required to assign an appropriate fuel price to a sample vehicle. This matching 

routine commonly resulted in a 1-to-many record linkage (see Figure C1 for an example). 

Cold-deck procedures make use of a fixed set of values, which covers all of the perspective

data items. These values can be constructed with the use of historical data, subject-matter 

expertise, or a combination of both. Such a procedure is an attempt to create a “perfect'”

questionnaire in order to fill in the missing data gaps or, in this case, append supplemental data. 

If these procedures are completed properly and with limited bias, imputation has the ability to 

derive a complete and accurate record that (1) contains an audit trail for evaluation purposes; and 

(2) ensures that the imputed records are internally consistent. 

Multiple paths were used to “match” recipient NHTS sample vehicles to eligible donor 

NHTSA file record vehicles. Because matching used a combination of four common linking 

variables – vehicle manufacturer, vehicle model, vehicle model year, and vehicle type – several

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“matching” paths were followed. These paths are denoted (i.e., internally audited) with

imputation flags, which are defined for each vehicle as follows: 

• 10# denotes a NHTS sample vehicle that had a single model name “matching” to

eligible NHTSA file records using four linking variables: vehicle manufacturer, 

vehicle model, vehicle model year, and vehicle type. 

• 20# denotes a NHTS sample vehicle that had multiple model names “matching” to 

eligible NHTSA file records using four linking variables: vehicle manufacturer, 

vehicle model, vehicle model year, and vehicle type. 

• 30# denotes a NHTS sample vehicle that had a single model name “matching” to

eligible NHTSA file records using three linking variables: vehicle manufacturer, 

vehicle model, and vehicle model year. 

• 40# denotes a NHTS sample vehicle that had multiple model names “matching” to 

eligible NHTSA file records using three linking variables: vehicle manufacturer, 

vehicle model, and vehicle model year. 

• 50# denotes a NHTS sample vehicle that had a single model name “matching” to

eligible NHTSA file records using three linking variables: vehicle manufacturer, 

vehicle type, and vehicle model year. 

• 60# denotes a “match” based on EIA expert analysis using subject matter experience, 

in conjunction with past RTECS. Additionally, this imputation flag value represents 

recreational vehicles (VEHTYPE = “06”), where MPG(EPA 55/45) has been fixed at a 

yearly estimate based on the U.S. Department of Transportation, National

Transportation Statistics 2000.

76

• 001 denotes a NHTS sample vehicle that was internally hot-decked to match with its 

median Composite fuel economy value as defined by one or more vehicle 

characteristics, such as make, model, model year, and vehicle type. These flagged 

values become more meaningful with pre-1978 model year vehicles since NHTSA’s 

CAFE exclude pre-1978 model years. EIA, therefore, recommends that users take 

extreme caution when making inferences concerning pre-1978 model year vehicles 

from this report. 

• 999 denotes an imputation flag where no eligible NHTSA file records were found to 

“match” a NHTS sample vehicle. 

In the above listing, # is a number between 0 and 5. This number, #, represents a year 

increment. Due to the errors in respondents reporting accurate model year or, to a lesser extent, 

due to deficiencies in the NHTSA files, it was necessary to incrementally increase or decrease

(not simultaneously increase and decrease) the model year for “matching” to successively larger 

range of years. If, for example, an eligible match was not found for a NHTS sample vehicle

 76 Table 4-11. Passenger Car and Motorcycle Fuel Consumption and Travel, National Transportation Statistics 2000

Bureau of Transportation Statistics, U.S. Department of Transportation. (Washington, DC).

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having the following attributes: Volkswagen, Scirocco, 1990, Automobile. Toggling of model 

years, by a single year increase followed by a single year decrease of the reported model year,

resulted in a match with a Volkswagen, Scirocco, 1988, Automobile. In this example, the 

Volkswagen, Scirocco, 1990, Automobile, while seemingly a respondent reporting error, would 

receive an imputation flag of “102” due to the “match” with the NHTSA file record 

corresponding to a Volkswagen, Scirocco, 1988, Automobile. 

Table C1. Distribution of NHTS Sample Vehicles by Fuel Economy Imputation Flag, 2001 

Imputation Flag for MPG(EPA 55/45) Number of Vehicles in NHTS Sample

001 2,347

100 3,122

101 103

102 39

103 28

104 23

105 44

200 31,407

201 1,428

202 397

203 272

204 172

205 63

300 33

301 4

302 1

303 4

400 582

401 35

402 54

403 28

404 10

405 5

500 1,907

501 38

502 33

503 20

504 8

505 19

600 510

Total 42,736

Source: U.S. Department of Transportation, Federal Highway Administration, National Household Travel Survey 2001, 

augmented release by the Energy Information Administration, (Washington, DC). 

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While the distribution of imputation flags is helpful, further evidence is needed to quantify 

the quality of this procedure. Figure C2 expands our coverage to include all national sample 

vehicles (of which “100-percent-reporting household” is just a subset) and charts the 1-to-many

“matching” relationship for the 47,669 “matched” sample vehicles, or 53,278 less 3,696 (for 999 

flag) less 1,856 (for 600 flag).77

Figure C2. Distribution of NHTS Sample Vehicles “Matched” with Vehicles "Donated" by NHTSA 

File Records 

0

1000

2000

3000

4000

5000

6000

7000

8000

0 50 100 150 200

Number of Eligible Donor Vehicles from

NHTSA/EPA

Number of NHTS Sample Vehicles, 

N=47,699

Source: U.S. Department of Transportation, Federal Highway Administration, National Household Travel Survey 2001, 

preliminary National and Add-on release, January 2004. (Washington, DC).

To make the “match” distribution display more revealing, values from the above figure are 

tabulated to present range categories of donor vehicles in Table C2.

Table C2. Distribution of All NHTS Sample Vehicles “Matched” by Range of Donor Vehicles 

Range of Eligible Donor Vehicles Number of Vehicles in NHTS Sample 

1 5,238

2 to 5 19,617

6 to 10 11,225

11 to 20 5,399

21 to 30 1,273

 31 to 40 

1,148

77 Estimates were drawn from the public-use file released by FHWA in January 2003. In the January 2004 public-use 

file, three vehicles were deleted, yielding 53,275 vehicles in the complete national vehicle sample, including 100-

percent and 50-percent reporting households, as defined by NHTS.

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Table C2. Distribution of All NHTS Sample Vehicles “Matched” by Range of Donor Vehicles 

Range of Eligible Donor Vehicles Number of Vehicles in NHTS Sample 

41 to 50 1,672

51 or more 2,127

Total 47,699

Source: U.S. Department of Transportation, National Household Travel Survey 2001, preliminary National and Addon release, Federal Highway Administration, January 2004. (Washington, DC). 

QUALITY OF SPECIFIC SUPPLEMENTAL DATA ITEMS 

COLD-DECK PROCEDURE: SENSITIVITY ANALYSIS

Although the accuracy and robustness of the cold-deck procedure and subsequent fuel

economy adjustments are not quantifiable because we lack both fuel purchase and mileage diaries 

for calculating a vehicle’s actual on-road, in-use fuel economy, we can assess the sensitivity of

the cold-deck procedure in an effort to measure its robustness. 

Because we use a single value imputation approach, multiple imputations is one approach

available for investigating the uncertainty of our imputed values. Indeed, imputing a single value 

may result in estimating measures of precision (e.g., standard errors) that are too small because a

single value ignores the uncertainty found in selecting from a listing of donated values. Rather 

than perform a series of multiple imputations, we have assumed that each sample vehicle’s list of 

eligible donors represents a complete set of values for its “missing” fuel economy variable. 

Therefore, the uncertainty associated with the imputation procedure may be assessed by imputing 

a pre-determined subset of values; that is, ones that represent the extremes and average of eligible

donors. P5 and P95 – the 5th and 95th percentiles of sales-weighted fuel economy, respectively – 

represent our extreme distribution values, while the average value corresponded to the salesweighted average of the eligible donor vehicles. Using Figure 2 as an example, we calculate: P5 

= 25.1, P95 = 44.8 and a sales-weighted average of 30.8 miles per gallon. 

By separately totaling the consumption of transportation fuel for each of these 3 outcomes

and, then, comparing them to our single-value total, it is not surprising that we find that 

• applying sales-weighted fuel economy values yields a energy consumption total 2 percent 

less than the single-value total; 

• applying 5th percentile values yields an energy consumption total 7 percent more than the 

single-value total; and, 

• applying 95th percentile values yields an energy consumption total 9 percent less than the

single-value total. 

Clearly, applying extreme distribution values – P5 and P95 – to each and every eligible 

sample vehicle results in biased energy-related estimates. While these extreme values are not 

acceptable to a researcher, such biased estimates illustrate the upper and lower uncertainty bounds 

associated with cold-deck estimates. Given these bounds, along with survey sampling and nonsampling errors, the use and usefulness of an enhanced 2001 National Household Travel Survey

should be evaluated against a researcher’s project requirements. 

158 ENERGY INFORMATION ADMINISTRATION/HOUSEHOLD VEHICLES ENERGY USE: LATEST DATA & TRENDS
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VEHICLE FUEL PRICE AND EXPENDITURES 

In the 2001 NHTS, fuel price data were not collected via fuel purchase diaries, compared to 

previous EIA studies (e.g., RTECS). Instead, fuel prices were determined from EIA price series. 

Unfortunately, there is no way to validate the price methodology used to assign a monthly price 

paid for transportation fuel because EIA lacks the necessary fuel purchase diaries from NHTS 

repondents. 

The Bureau of Labor Statistics (BLS) Retail Pump Average Gasoline Prices and the 

Lundberg Survey, Inc. offer alternate price series. However, there was a general consistency with

using a price series from one statistical agency. 

GASOLINE EQUIVALENT GALLON 

The following table provides the gasoline equivalent gallon conversion used in this appendix.

All conversion values, to the extent possible, have been made to mirror the conversion values 

used in deriving equivalent-gallon fuel economy estimates found in the NHTSA CAFE files. 

Table C3. Gasoline Equivalent Gallon Conversion Values 

Transportation Fuel Gasoline Equivalent Gallon 

Diesel 1 diesel gallon = 1 gasoline equivalent gallon 

Electricity 33,705 Watt-hours = 1 gasoline equivalent gallon 

Compressed Natural Gas 121.5 cubic feet = 1 gasoline equivalent gallon 

Sources: 40 CFR Parts 80, 85, 86, 88, and 600 and 10 CFR Part 474. 

GREET MODEL 

Of course, there are other conversion factors available, depending on the various fuel-specific 

factors. For the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation

(GREET) model, the U.S. Department of Energy, Argonne National Laboratory uses the 

following: 

Table C4. Lower and Higher Heating Values for Select Transportation Fuels Based on the GREET 

Model

Transportation 

Fuel 

LHV (net) 

Btu per 

gallon 

HHV (gross)

Btu per 

gallon 

Density 

Grams per 

gallon 

Carbon 

Content 

(% by wt) 

Sulfur 

Content 

(ppm by 

wt) 

Conv.Gasoline 115,500 125,000 2,791 85.5% 200

Ref. Gasoline 112,265 121,456 2,795 82.9% 30

Diesel 128,500 138,700 3,240 87.0% 250

Methanol 57,000 65,000 2,996 37.5% 0

Ethanol 76,000 84,500 2,996 52.2% 0

LPG 84,000 91,300 2,000 82.0% 0

Natural gas 

928 1,031 21 74.0% 7

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Table C4. Lower and Higher Heating Values for Select Transportation Fuels Based on the GREET 

Model

Transportation 

Fuel 

LHV (net) 

Btu per 

gallon 

HHV (gross)

Btu per 

gallon 

Density 

Grams per 

gallon 

Carbon 

Content 

(% by wt) 

Sulfur 

Content 

(ppm by 

wt) 

Electricity 3,412 Btu/kWh 

Source: M. Wang, GREET 1.5 -- Transportation Fuel-Cycle Model, Volume 1: Methodologies, Development, Use, and 

Results, Center for Transportation Research, Argonne National Laboratory, ANL/ESD-39, Vol.1, Aug. 1999. M. 

Wang, GREET 1.5 -- Transportation Fuel-Cycle Model, Volume 2: Appendices of Data and Results, Center for 

Transportation Research, Argonne National Laboratory, ANL/ESD-39, Vol.2, Aug. 1999. Notes: 1) Gasoline results 

are for the mix of 70% conventional gasoline and 30% reformulated gasoline. 2) LPG results are for the mix of 40% 

LPG produced from crude and 60% from natural gas. 3) Electricity results are for the current U.S. average electricity

generation mix.

TRANSPORTATION ENERGY DATA BOOK: EDITION 22 — 2002 

Likewise, the Energy Information Administration, U.S. Department of Energy (according to 

the latest Transportation Energy Data Book) applies the following approximate heat content for

various fuels: 

Table C5. Lower and Higher Heating Values for Various Transportation Fuels 

Transportation Fuel HHV (gross) equivalent to LHV (net)

Automotive gasoline 125,000 Btu/gal (gross) = 115,400 Btu/ gal(net)

Diesel motor fuel 138,700 Btu/gal (gross) = 128,700 Btu/gal (net)

Biodiesel 126,206 Btu/gal (gross) = 117,093 Btu/gal (net)

Methanol 64,600 Btu/gal (gross) = 56,560 Btu/gal (net) 

Ethanol 84,600 Btu/gal (gross) = 75,670 Btu/gal (net) 

Gasohol 120,900 Btu/gal (gross) = 112,417 Btu/gal (net)

Aviation gasoline 120,200 Btu/gal (gross) = 112,000 Btu/gal (net)

Propane 91,300 Btu/gal (gross) = 83,500 Btu/gal (net) 

Butane 103,000 Btu/gal (gross) = 93,000 Btu/gal (net) 

Jet fuel (naphtha) 127,500 Btu/gal (gross) = 118,700 Btu/gal (net)

Jet fuel (kerosene) 135,000 Btu/gal (gross) = 128,100 Btu/gal (net)

Lubricants 144,400 Btu/gal (gross) = 130,900 Btu/gal (net)

Waxes 131,800 Btu/gal (gross) = 120,200 Btu/gal (net)

Natural Gas 

 Wet 1,109 Btu/ft3

 Dry 1,027 Btu/ft3

 Compressed 20,551 Btu/pound

960 Btu/ft3

Liquid 90,800 Btu/gal (gross) = 87,600 Btu/gal (net) 

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Table C5. Lower and Higher Heating Values for Various Transportation Fuels 

Transportation Fuel HHV (gross) equivalent to LHV (net)

Fuel Oils 

Residual 149,700 Btu/gal (gross) = 138,400 Btu/gal (net)

Distillate 138,700 Btu/gal (gross) = 131,800 Btu/gal (net)

Source: U.S. Department of Energy, Oakridge National Laboratory, Center for Transportation Analysis, Transportation

Energy Data Book Edition 22, Washington, DC, 2002, Table B.1, ORNL-6967. 

According to ORNL’s latest Data Book, 

The heat content of a fuel is the quantity of energy released by burning a unit amount of

that fuel. However, this value is not absolute and can vary according to several factors.

For example, empirical formulae for determining the heating value of liquid fuels depend 

on the fuels' American Petroleum Institute (API) gravity. The API gravity varies 

depending on the percent by weight of the chemical constituents and impurities in the fuel, 

both of which are affected by the combination of raw materials used to produce the fuel 

and by the type of manufacturing process. Temperature and climatic conditions are also 

factors. 

Because of these variations, the heating values in above table may differ from values in

other publications. The figures in the Edition 22 report are representative or average 

values, not absolute ones. The gross heating values used here agree with those used by the 

Energy Information Administration. 

Heating values fall into two categories, gross and net. If the products of fuel combustion

are cooled back to the initial fuel-air or fuel-oxidizer mixture temperature and the water 

formed during combustion is condensed, the energy released by the process is the higher 

(gross) heating value. If the products of combustion are cooled to the initial fuel-air 

temperature, but the water is considered to remain as a vapor, the energy released by the 

process is lower (net) heating value. Usually the difference between the gross and net 

heating values for fuels used in transportation is around 5 to 8 percent; however, it is 

important to be consistent in their use. 

EIA strongly encourages a consistent use of heating values. 

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