Literature DB >> 22640750

Mode of data elicitation, acquisition and response to surveys: a systematic review.

K Hood1, M Robling, D Ingledew, D Gillespie, G Greene, R Ivins, I Russell, A Sayers, C Shaw, J Williams.   

Abstract

BACKGROUND: Many studies in health sciences research rely on collecting participant-reported outcomes and attention is increasingly being paid to the mode of data collection. Consideration needs to be given to the validity of response via different modes and the impact that choice of mode might have on study conclusions.
OBJECTIVES: (1) To provide an overview of the theoretical models of survey response and how they relate to health research; (2) to review all studies comparing two modes of administration for subjective outcomes and assess the impact of mode of administration on response quality; (3) to explore the impact of findings for key identified health-related measures; and (4) to inform the analysis of multimode studies. DATA SOURCES: A broad range of databases (for example EMBASE, PsychINFO, MEDLINE, EconLit, SPORTDiscus, etc.) were chosen to allow as comprehensive a selection as possible, and they were searched up until the end of 2004. REVIEW
METHODS: The abstracts were reviewed against inclusion/exclusion criteria. Full papers were retrieved for all selected abstracts and then screened again using more detailed inclusion criteria related to the measures used. Papers that were still included were reviewed in full and detailed data extracted. At each stage, abstracts or papers were reviewed by a single reviewer.
RESULTS: The search strategy identified 39,253 unique references, of which 2156 were considered as full papers, with 381 finally included in the review. Two features of mode were clearly associated with bias in response; however, none of the features of mode was associated with changes in precision. How the measure was administered, by an interviewer or by the person themselves, was highly significantly associated with bias (p < 0.001). A difference in sensory stimuli was also significant (p = 0.03). When both of these were present the average overall bias was < 1 point on a percentage scale. In terms of mediating factors, there was some suggestion that there was an interaction between both telephone and computer for data collection and date of publication, supporting the theory that differences disappear as new technologies become commonplace. Single-item measures were also related to greater degrees of bias than multi-item scales (p = 0.01). Individual analysis of the Short Form questionnaire-36 items and Minnesota Multiphasic Personality Inventory (MMPI) showed a varied pattern across the different subscales, with conflicting results between the two types of study. None of the MMPI measures used to detect deviant responding showed a relationship with the mode features tested. The limits of agreement analysis showed how variable measures were between modes at an individual rather than a group mean level. LIMITATIONS: The search strategy covered the period up to 2004, so any new and emerging technologies were not included. Not all potential mode features were tested and there was limited information on potential mediating factors.
CONCLUSIONS: Researchers need to be aware of the different mode features that could have an impact on their results when selecting a mode of data collection for subjective outcomes. Further mode comparison studies, which manipulate mode features and directly assess impact over time, would be beneficial.

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Year:  2012        PMID: 22640750     DOI: 10.3310/hta16270

Source DB:  PubMed          Journal:  Health Technol Assess        ISSN: 1366-5278            Impact factor:   4.014


  16 in total

Review 1.  Mode of administration does not cause bias in patient-reported outcome results: a meta-analysis.

Authors:  Claudia Rutherford; Daniel Costa; Rebecca Mercieca-Bebber; Holly Rice; Liam Gabb; Madeleine King
Journal:  Qual Life Res       Date:  2015-09-03       Impact factor: 4.147

2.  Does the COPD assessment test (CAT(TM)) questionnaire produce similar results when self- or interviewer administered?

Authors:  A Agusti; J J Soler-Cataluña; J Molina; E Morejon; M Garcia-Losa; M Roset; X Badia
Journal:  Qual Life Res       Date:  2015-04-07       Impact factor: 4.147

3.  Validation of the Care-Related Quality of Life Instrument in different study settings: findings from The Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS).

Authors:  J E Lutomski; N J A van Exel; G I J M Kempen; E P Moll van Charante; W P J den Elzen; A P D Jansen; P F M Krabbe; B Steunenberg; E W Steyerberg; M G M Olde Rikkert; R J F Melis
Journal:  Qual Life Res       Date:  2014-11-08       Impact factor: 4.147

4.  How reliable is internet-based self-reported identity, socio-demographic and obesity measures in European adults?

Authors:  Carlos Celis-Morales; Katherine M Livingstone; Clara Woolhead; Hannah Forster; Clare B O'Donovan; Anna L Macready; Rosalind Fallaize; Cyril F M Marsaux; Lydia Tsirigoti; Eirini Efstathopoulou; George Moschonis; Santiago Navas-Carretero; Rodrigo San-Cristobal; Silvia Kolossa; Ulla L Klein; Jacqueline Hallmann; Magdalena Godlewska; Agnieszka Surwiłło; Christian A Drevon; Jildau Bouwman; Keith Grimaldi; Laurence D Parnell; Yannis Manios; Iwona Traczyk; Eileen R Gibney; Lorraine Brennan; Marianne C Walsh; Julie A Lovegrove; J Alfredo Martinez; Hannelore Daniel; Wim H M Saris; Mike Gibney; John C Mathers
Journal:  Genes Nutr       Date:  2015-07-05       Impact factor: 5.523

5.  Psychometric evaluation of self-report outcome measures for prosthetic applications.

Authors:  Brian J Hafner; Sara J Morgan; Robert L Askew; Rana Salem
Journal:  J Rehabil Res Dev       Date:  2016

6.  Danish population-based reference data for the EORTC QLQ-C30: associations with gender, age and morbidity.

Authors:  Therese Juul; Morten Aagaard Petersen; Bernhard Holzner; Søren Laurberg; Peter Christensen; Mogens Grønvold
Journal:  Qual Life Res       Date:  2014-03-28       Impact factor: 4.147

7.  Patient-reported measurement of time to diagnosis in cancer: development of the Cancer Symptom Interval Measure (C-SIM) and randomised controlled trial of method of delivery.

Authors:  Richard D Neal; Sadia Nafees; Diana Pasterfield; Kerenza Hood; Maggie Hendry; Simon Gollins; Matthew Makin; Nick Stuart; Jim Turner; Ben Carter; Clare Wilkinson; Nefyn Williams; Mike Robling
Journal:  BMC Health Serv Res       Date:  2014-01-03       Impact factor: 2.655

8.  Parent Programs for Reducing Adolescent's Antisocial Behavior and Substance Use: A Randomized Controlled Trial.

Authors:  Camilla Jalling; Maria Bodin; Anders Romelsjö; Håkan Källmén; Natalie Durbeej; Anders Tengström
Journal:  J Child Fam Stud       Date:  2015-08-09

9.  Evaluating CollaboRATE in a clinical setting: analysis of mode effects on scores, response rates and costs of data collection.

Authors:  Paul J Barr; Rachel C Forcino; Rachel Thompson; Elissa M Ozanne; Roger Arend; Molly Ganger Castaldo; A James O'Malley; Glyn Elwyn
Journal:  BMJ Open       Date:  2017-03-24       Impact factor: 2.692

10.  Validity of web-based self-reported weight and height: results of the Nutrinet-Santé study.

Authors:  Camille Lassale; Sandrine Péneau; Mathilde Touvier; Chantal Julia; Pilar Galan; Serge Hercberg; Emmanuelle Kesse-Guyot
Journal:  J Med Internet Res       Date:  2013-08-08       Impact factor: 5.428

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