A Review on Official Survey Item Classification for Mixed-Mode Effects Adjustment

Afshin Ashofteh, Pedro Campos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The COVID-19 pandemic has had a direct impact on the development, production, and dissemination of official statistics. This situation led National Statistics Institutes (NSIs) to make methodological and practical choices for survey collection without the need for the direct contact of interviewing staff (i.e. remote survey data collection). Mixing telephone interviews (CATI) and computer-assisted web interviewing (CAWI) with direct contact of interviewing constitute a new way for data collection at the time COVID-19 crisis. This paper presents a literature review to summarize the role of statistical classification and design weights to control cover-age errors and non-response bias in mixed-mode questionnaire design. We identified 289 research articles with a computerized search over two databases, Scopus and Web of Science. It was found that, although employing mixed-mode surveys could be considered as a substitution of traditional face-to-face interviews (CAPI), proper statistical classification of survey items and responders is important to control the nonresponse rates and coverage error risk.
Original languageEnglish
Title of host publicationClassification and Data Science in the Digital Age
EditorsPaula Brito, José G. Dias, Berthold Lausen, Angela Montanari, Rebecca Nugent
Place of PublicationGewerbestrasse, Switzerland
PublisherSpringer, Cham
Number of pages9
ISBN (Electronic)978-3-031-09034-9
ISBN (Print)978-3-031-09033-2
Publication statusPublished - 8 Dec 2023
Event17th Conference of the International Federation of Classification Societies: Classification and Data Science in the Digital Age - Faculdade de Economia da Universidade do Porto, Porto, Portugal
Duration: 19 Jul 202223 Jul 2022
Conference number: 17

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
PublisherSpringer Cham
ISSN (Print)1431-8814
ISSN (Electronic)2198-3321


Conference17th Conference of the International Federation of Classification Societies
Abbreviated titleIFCS 2022
Internet address


  • Mixed Mode
  • Classification
  • Clustering
  • Measurement Error
  • mixed-mode official surveys
  • item classification
  • weighting methods


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