Assessment of the Computer-Assisted Instrument


Caspar, R., & Penne, M. (2001). Assessment of the Computer-Assisted Instrument. In J. Gfroerer, J. Eyerman, & J. R. Chromy (Eds.), Redesigning an Ongoing National Household Survey: Methodological Issues. DHHS Publication No. SMA 03-3768. (pp. 53-84). (SMA 03-3768). Rockville, MD: Substance Abuse and Mental Health Services Administration.


The conversion of the National Household Survey on Drug Abuse (NHSDA) to computer-assisted interviewing (CAI) offered an opportunity to improve the quality of the data collected in the NHSDA in a number of ways. Some of these improvements were implemented easily and manifested themselves in more complete data—for example, the ability to eliminate situations where questions were inadvertently left blank by the respondent. However, other improvements could only be realized through careful development and implementation of new procedures. Thorough testing was needed to determine whether these new procedures did, in fact, result in higher quality data. In this chapter, two significant revisions to how key NHSDA data items are collected and the effect of these revisions on the quality of the data obtained in the 1999 NHSDA are described. The first of these revisions was the addition of a methodology for resolving inconsistent or unusual answers provided by the respondent. This methodology was incorporated into the collection of a large number of the data items that are considered critical to the reporting needs of the NHSDA. The second revision dealt specifically with the way data on frequency of substance use over the past 12-month period was reported. This chapter also provides a review of several basic measures of data quality including rates of Don!t Know and Refused responses, breakoff interviews, and the observational data provided by the interviewers at the conclusion of each interview. Where possible, these measures are compared between the CAI and paper-and-pencil interview (PAPI) NHSDA instruments as a means of assessing the effect of the move to CAI on data quality