Identifying Attrition Phases in Survey Data: Applicability and Assessment Study

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Abstract

Background

Although Web-based questionnaires are an efficient, increasingly popular mode of data collection, their utility is often challenged by high participant dropout. Researchers can gain insight into potential causes of high participant dropout by analyzing the dropout patterns.

Objective

This study proposed the application of and assessed the use of user-specified and existing hypothesis testing methods in a novel setting—survey dropout data—to identify phases of higher or lower survey dropout.

Methods

First, we proposed the application of user-specified thresholds to identify abrupt differences in the dropout rate. Second, we proposed the application of 2 existing hypothesis testing methods to detect significant differences in participant dropout. We assessed these methods through a simulation study and through application to a case study, featuring a questionnaire addressing decision-making surrounding cancer screening.

Results

The user-specified method set to a low threshold performed best at accurately detecting phases of high attrition in both the simulation study and test case application, although all proposed methods were too sensitive.

Conclusions

The user-specified method set to a low threshold correctly identified the attrition phases. Hypothesis testing methods, although sensitive at times, were unable to accurately identify the attrition phases. These results strengthen the case for further development of and research surrounding the science of attrition.

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Most cited references 14

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Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions

Saskia M Kelders, Robin N Kok, Hans Ossebaard … (2013)

Background Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. Objective This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. Methods We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. Results We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p = .004), setup (p < .001), updates (p < .001), frequency of interaction with a counselor (p < .001), the system (p = .003) and peers (p = .017), duration (F = 6.068, p = .004), adherence (F = 4.833, p = .010) and the number of primary task support elements (F = 5.631, p = .005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Conclusions Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.

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Increasing response rates to postal questionnaires: systematic review.

Phil Edwards, Ian Roberts, Mike Clarke … (2002)

To identify methods to increase response to postal questionnaires. Systematic review of randomised controlled trials of any method to influence response to postal questionnaires. 292 randomised controlled trials including 258 315 participants INTERVENTION REVIEWED: 75 strategies for influencing response to postal questionnaires. The proportion of completed or partially completed questionnaires returned. The odds of response were more than doubled when a monetary incentive was used (odds ratio 2.02; 95% confidence interval 1.79 to 2.27) and almost doubled when incentives were not conditional on response (1.71; 1.29 to 2.26). Response was more likely when short questionnaires were used (1.86; 1.55 to 2.24). Personalised questionnaires and letters increased response (1.16; 1.06 to 1.28), as did the use of coloured ink (1.39; 1.16 to 1.67). The odds of response were more than doubled when the questionnaires were sent by recorded delivery (2.21; 1.51 to 3.25) and increased when stamped return envelopes were used (1.26; 1.13 to 1.41) and questionnaires were sent by first class post (1.12; 1.02 to 1.23). Contacting participants before sending questionnaires increased response (1.54; 1.24 to 1.92), as did follow up contact (1.44; 1.22 to 1.70) and providing non-respondents with a second copy of the questionnaire (1.41; 1.02 to 1.94). Questionnaires designed to be of more interest to participants were more likely to be returned (2.44; 1.99 to 3.01), but questionnaires containing questions of a sensitive nature were less likely to be returned (0.92; 0.87 to 0.98). Questionnaires originating from universities were more likely to be returned than were questionnaires from other sources, such as commercial organisations (1.31; 1.11 to 1.54). Health researchers using postal questionnaires can improve the quality of their research by using the strategies shown to be effective in this systematic review.

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Inference and Missing Data

Donald B Rubin (1976)

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Author and article information

Contributors

Camille J Hochheimer:

Department of BiostatisticsVirginia Commonwealth UniversityPO Box 980032Richmond, VA, 23298-0032United States1 804 828 98241 804 828 8900hochheimercj@vcu.edu

ORCID: http://orcid.org/0000-0002-0984-0909

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J. Med. Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: August 2019

Publication date (Electronic): 23 August 2019

Volume: 21

Issue: 8

Electronic Location Identifier: e12811

Affiliations

[1 ] Department of Biostatistics Virginia Commonwealth University Richmond, VA United States

[2 ] Department of Family Medicine and Population Health Virginia Commonwealth University Richmond, VA United States

Author notes

Corresponding Author: Camille J Hochheimer hochheimercj@ 123456vcu.edu

Author information

Camille J Hochheimer http://orcid.org/0000-0002-0984-0909

Roy T Sabo http://orcid.org/0000-0002-5866-2598

Robert A Perera http://orcid.org/0000-0002-0375-0427

Nitai Mukhopadhyay http://orcid.org/0000-0002-1530-9516

Alex H Krist http://orcid.org/0000-0003-4060-9155

Article

Publisher ID: v21i8e12811

DOI: 10.2196/12811

PMC ID: 6729115

PubMed ID: 31444875

SO-VID: 71e75ce5-e07e-43c1-a9b5-3ed462a9217d

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/.as well as this copyright and license information must be included.

History

Date received : 16 November 2018

Date revision requested : 14 April 2019

Date revision received : 7 June 2019

Date accepted : 10 June 2019

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