The various disciplines that embrace the survey method, including statistics, psychology, sociology, and economics, share a common concern with the weakness of the measurement process, the degree to which survey results deviate from “those that are the true reflections of the population” (Groves, 1989). The disciplines vary in the terminology used to describe error as well as their emphasis on understanding the impact of measurement error on analyses or the reduction of the various sources of error. The existence of these terminological differences and our desire to limit the focus of this research to measurement error suggests that a brief commentary on the various conceptual frameworks may aid in defining our interests unambiguously.
One common conceptual framework is that of mean squared error, the sum of the variance and the square of the bias. Variance is the measure of the variable error associated with a particular implementation of a survey; inherent in the notion of variable error is the fundamental requirement of replication, whether over units of observation (sample units), questions, or interviewers. Bias, as used here, is defined as the type of error that affects all implementations of a survey design, a constant error, within a defined set of essential survey conditions (Hansen et al., 1961). For example, the use of a single question to obtain total family income in the Current Population Survey (CPS) has been shown to underestimate annual income by approximately 20 percent (U.S. Bureau of the Census, 1979); this consistent underestimate would be considered the extent of the bias related to a particular question for a given survey design.
Another conceptual framework focuses on errors of observation as compared to errors of nonobservation (Kish, 1965). Errors of observation refer to the degree to which individual responses deviate from the true value for the measure of interest; as defined, they are the errors of interest for this research, to be referred to as measurement errors. Observational errors can arise from any of the elements directly engaged in the measurement process, including the questionnaire, the respondent, and the interviewer, as well as the characteristics that define the measurement process (e.g., the mode and method of data collection). Errors of nonobservation refer to errors related to the lack of measurement for some portion of the sample and can be classified as arising from three sources, coverage: nonresponse (both unit and item nonresponse), and sampling. Errors of nonobservation are the focus of other papers presented in this volume (see, for example, Groves and Couper, this volume).
Ideally a question will convey to the respondent the meaning of interest to the researcher. However, several linguistic, structural, and environmental factors affect the interpretation of the question by the respondent. These factors include