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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Nov 16, 2018
Open Peer Review Period: Nov 22, 2018 - Jan 17, 2019
Date Accepted: Jun 10, 2019
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Identifying Attrition Phases in Survey Data: Applicability and Assessment Study

Hochheimer CJ, Sabo RT, Perer RA, Mukhopadhyay N, Krist AH

Identifying Attrition Phases in Survey Data: Applicability and Assessment Study

J Med Internet Res 2019;21(8):e12811

DOI: 10.2196/12811

PMID: 31444875

PMCID: 6729115

Using practical thresholds and existing statistical methods to identify attrition phases

  • Camille J Hochheimer; 
  • Roy T Sabo; 
  • Robert A Perer; 
  • Nitai Mukhopadhyay; 
  • Alex H Krist

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 dropout by analyzing dropout patterns.

Objective:

To propose and investigate user-specified and existing hypothesis testing methods applied in a novel setting, survey dropout data, in order to identify attrition phases.

Methods:

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

Results:

All three proposed methods were too sensitive but a low user-specified threshold performed best at accurately detecting phases of high attrition in both the simulation study and test case application.

Conclusions:

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


 Citation

Please cite as:

Hochheimer CJ, Sabo RT, Perer RA, Mukhopadhyay N, Krist AH

Identifying Attrition Phases in Survey Data: Applicability and Assessment Study

J Med Internet Res 2019;21(8):e12811

DOI: 10.2196/12811

PMID: 31444875

PMCID: 6729115

Per the author's request the PDF is not available.

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