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Sampling and Construction of Variables

  • Jean-Michel Josselin
  • Benoît Le Maux
Chapter

Abstract

The construction of a reliable and relevant database is a key aspect of any statistical study. Not only will misleading information create bias and mistakes (sampling, coverage or measurement errors, etc.), but it may also seriously affect public decisions if the study is used for guiding policy-makers. The sample should be sufficiently representative of the population of interest (Sect. 2.1). The time needed to collect and process the data is an important issue in this respect (Sect. 2.2). As the purpose of a survey is to obtain sincere responses from the respondents, the design of the questionnaire also has its importance; in particular the sequencing, phrasing and format of questions (Sect. 2.3). The process of data collection should not be neglected either. The analyst must decide which sampling method is more relevant (e.g., non-probability vs. probability sampling) and assess the efficacy of data collection during the survey process (Sect. 2.4). Last, the coding of variables and the way measurement and nonresponse errors are investigated are an essential step of data construction (Sect. 2.5).

Keywords

Census Survey Questionnaire Sampling Coding Nonresponse errors Outliers Raking 

Bibliography

  1. Eurostat. (2004). Handbook of recommended practices for questionnaire development and testing in the European Statistical System.Google Scholar
  2. OECD. (1998). Public opinion surveys as input to administrative reform (SIGMA Papers, No. 25). OECD Publishing.Google Scholar
  3. Statistics Canada. (2010). Survey methods and practices.Google Scholar
  4. US Census Bureau. (2007). Guidelines for designing questionnaires for administration in different modes.Google Scholar
  5. United Nations. (2005). Designing household survey samples: Practical guidelines.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jean-Michel Josselin
    • 1
  • Benoît Le Maux
    • 1
  1. 1.Faculty of EconomicsUniversity of Rennes 1RennesFrance

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