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.

Tools for Identifying Nonsampling Error in Survey Data

In many applications data can be interpreted as indirect observations of a latent distribution. A typical example is the phenomenon known as digit preference, i.e. the tendency to round outcomes to pleasing digits. … When people read an analog scale or report numeric results, a commonly found effect is that certain preferred [digits] are reported substantially more often thanthe general pattern of the distribution suggests. … This type of mi sreporting leads to unusual heapings at the preferreddigits.

Carlo G. Camarda, Paul H. C. Eilers, and Jutta, Gampe, 2008, p. 385

Survey researchers have employed a number of different data distribution tests to identify, estimate, and manage nonsampling error. This chapter is organized in two sections; the first describes ...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles