Cover Image

Not for Sale



View/Hide Left Panel
Click for next page ( 69


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 68
68 Guidebook for Conducting Airport User Surveys 4.10 Data Entry and Quality Control 4.10.1 Data Coding, Data Entry, Verification, and Editing Data coding refers to the assignment of numeric codes to the various response options. Typically, this is done at the time the questionnaire is developed and the codes are shown on the printed survey form to assist in data entry or embedded in the EDCD program. In some cases, responses in free-text fields are assigned numeric codes in a separate field, particularly where the same response recurs frequently in the data, such as hotel names. Although response codes can be omitted from printed survey forms and provided to the data entry staff as a separate docu- ment, this procedure may require them to refer to two different documents during data entry (depending on the data entry software) and may slow down the data entry and/or introduce data entry errors. It will of course be necessary to define any response codes for commonly occurring free-text responses after the survey has been performed. Data entry involves transferring the survey response data from the survey forms to a computer file using the numeric codes shown on the survey form or developed later and defined in a sur- vey codebook. Two methods may be considered: Manual data entry, using survey staff (or others) retained after the survey data collection for this purpose. This method is labor intensive and subject to data entry errors. Mass-scanning techniques, using high-speed scanning hardware and software that is capable of automatically coding responses into a database. Further information on this method, including pros and cons, is provided in Appendix E. There are two aspects to data verification: Checking that the data entry was done correctly. Ideally this involves repeating the data entry task with different staff and comparing the two files. Data entry software typically provides a verification function that compares the second data entry to the original file and flags any dif- ferences. Any discrepancies are then resolved with reference to the survey forms. However, this technique doubles the data entry cost. A less expensive but less reliable approach involves verifying a random sample of survey forms. This technique will establish whether the required accuracy for data entry has been achieved, but of course cannot identify and correct any errors on the survey forms that are not included in the verification. Analyzing the data to identify any obvious errors, inconsistencies, or apparently illogical responses. This can address such issues as whether a trip origin zip (postal) code is in the reported city, and whether the street name of a reported address exists in the city indicated. A common problem is misspelling of free-text data, such as city or street names, or switching digits in zip and postal codes. Checks can be run to make sure that respondents reporting the use of ground transportation services reported trip origins in locations where use of the ser- vice would be plausible. Numerical responses can be checked to ensure that they are within a reasonable range. Free-text responses in the "Other" category of categorical questions should be reviewed to determine whether the response should have been given as one of the defined categories. In some cases, an error will be fairly clear, such as misspelled names or transposed digits in a zip or postal code. In other cases, it may be less obvious what the correct answer should have been, or even whether there is an error at all. Data verification can be very time-consuming if done thoroughly, but the overall quality of the survey data is greatly improved by devoting adequate resources to this task. The majority of the required effort lies not so much in identifying apparent errors in the data, which is fairly straightforward, but in the research necessary to determine what the correct response should have been. For example, it may be fairly easy to determine that a zip code is not in the reported city, but figuring out what the error is in the zip code, or even whether the zip code is correct but