A welcome and much-needed addition to the literature on survey data quality in social research, McNabb's book examines the most common sources of nonsampling error: frame error; measurement error; response error, nonresponse error, and interviewer error. Offering the only comprehensive and non-technical treatment available, the book's focus on controlling error shows readers how to eliminate the opportunity for error to occur, and features revealing examples of past and current efforts to control the incidence and effects of nonsampling error. Most importantly, it gives readers the tools they need to understand, identify, address, and prevent the most prevalent and difficult-to-control types of survey errors.
In a broad sense, the error brought about by the influence of a sampling frame to a sampling survey is the systematic representation error or systematic error. It is caused by lack of representativeness … due to non-random factors. And its manifestation is that the value of the sample statistics is systemically higher or lower. In our application in practice, it is often regarded as the deviation and neglected. In fact, we should treat sampling frame errors as non-sampling errors.
The sample frame is the source or list of sample units from which a sample is drawn. The frame can be of any subject group selected from an environment of interest. Examples include a list of manufacturing firms, patients with ...