Measuring the Benefits of Intercity Passenger Rail: A Study

sampling technique. A more efficient approach for the intercity rail passenger on-board survey
is to use a stratified random sample or a cluster sample approach. Both approaches divide the
population into smaller groups for the selection of sampling units. In the intercity passenger rail
application, the smaller groups might be individual train runs during the day (for example, #821
and #822 for the Heartland Flyer might be considered separate groups). Under stratified random
sampling, individual strata (train runs) would be selected for study and a simple random sample
of units from within each strata is considered. For cluster sampling, a simple random sample of
the smaller units (train runs) is made and all sampling units within a cluster are observed. A
robust sampling strategy for an on-board survey of intercity rail passengers would likely
resemble a multi-stage cluster sample, where a formal process would be employed to randomly
select individual train runs for study and the survey distributed to all eligible passengers on those
trains. While this approach would not require a complete sampling frame, a multi-stage cluster
sample would still be problematic as the resource efficiencies realized without a formal plan
(most notably, conducting on-board surveys on consecutive days) would likely be lost.
The tradeoffs between these efficiencies and the precision that is gained with a formal
sampling plan do not appear to have been an issue for past on-board passenger surveys, as none
of the surveys reviewed in this study reported using a formal sampling plan. In this study,
researchers employed no simple random sampling procedures to select either the train runs to
survey (cluster sample) or passengers to sample from an individual train run (stratified random
sampling). Rather, researchers selected train runs in this study from within a particular month
(April for average season, July for peak season) based on other factors such as the presence of
holiday weekends (and the researchers’ availability). Within each train run, all passengers aged
18 and over that were not part of a group were asked to participate in the research study. While
it is likely that the analysis methods for stratified random sampling or cluster sampling could
have been used to approximate the variance of any estimators developed from the Heartland
Flyer on-board passenger survey data set, the absence of the random sampling component of
each of these sampling strategies (an underlying assumption) would result in erroneous variance
estimates being reported. Consequently, the analysis reports no variances for the estimators so as
to avoid giving the perception that a certain level of precision exists in the estimates.
All of the analysis findings summarized in this chapter are reported as a percentage of the
total sample, computed by dividing the total number of samples in a particular category of a
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