Survey sidekick: Structuring scientifically sound surveys

Ihan Hsiao, Shuguang Han, Manav Malhotra, Hui Soo Chae, Gary Natriello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Online surveys are becoming more popular as a means of information gathering in both academia and industry because of their relatively low cost and delivery. However, there are increasing debates on data quality in online surveys. We present a novel survey prototyping tool that integrates embedded learning resources to facilitate the survey prototyping process and encourage creating scientifically sound surveys. Results from a controlled pilot study confirmed that survey structure follows three guided principles: simple-first, structure-coherent and gradual-difficulty-increase, revealing positive effects on survey structures under learning resources influences.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages516-522
Number of pages7
Volume8474 LNCS
ISBN (Print)9783319072203
DOIs
StatePublished - 2014
Externally publishedYes
Event12th International Conference on Intelligent Tutoring Systems, ITS 2014 - Honolulu, HI, United States
Duration: Jun 5 2014Jun 9 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8474 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other12th International Conference on Intelligent Tutoring Systems, ITS 2014
CountryUnited States
CityHonolulu, HI
Period6/5/146/9/14

Keywords

  • Hidden Markov Model
  • Ill-defined domain
  • Survey Design

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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