The effects of monetary and non-monetary incentives on respondent attrition in longitudinal survey
| dc.contributor.advisor | De Renzi, Brian | |
| dc.contributor.advisor | Rosenstock, Todd | |
| dc.contributor.author | Choga, Ngonidzashe Nicholas | |
| dc.date.accessioned | 2020-02-28T12:15:18Z | |
| dc.date.available | 2020-02-28T12:15:18Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-02-28T08:45:14Z | |
| dc.description.abstract | Longitudinal studies are essential for governments and organizations as they help in making decisions that are based on factual data. Longitudinal studies collect data repeatedly from a set of participants over a period of time, enabling the tracking and studying of entity behaviour at individual, organizational, and national levels. One major challenge facing longitudinal data collection is the attrition of subjects during the course of the study, which is the continuous loss of participants during a longitudinal survey due to verbal drop-outs and non-response. Attrition can render datasets useless due to incomplete entries, making it one of the most significant weaknesses of longitudinal surveys. In order to explore the effects of incentives on attrition, this research project studies the effects of monetary and non-monetary incentives on explicit (subject says s/he does not want to be part of the study) and implicit (non-response) attrition. In particular, this study uses telephonically delivered feedback, derived from participant responses, as non-monetary incentives. To measure the effects of incentives on attrition, the study gave four treatments groups —50 participants each—mobile credit, verbally delivered feedback, machine delivered feedback and no incentive. After monitoring their attrition, over a 12-week period that involved bi-weekly surveys, a generalised linear model and Cochran’s q-test were used to find that monetary incentives remain the strongest in under-served community settings. It was not only found that monetary incentive treatments completed the most surveys most weeks, but also had the least explicit attrition. Surprisingly it was also found that machine delivered feedback performed similarly to mobile credit when the cost, social impact and participant behaviour in terms of their survey completion and attrition is assessed. | |
| dc.identifier.apacitation | Choga, N. N. (2019). <i>The effects of monetary and non-monetary incentives on respondent attrition in longitudinal survey</i>. (). ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/31393 | en_ZA |
| dc.identifier.chicagocitation | Choga, Ngonidzashe Nicholas. <i>"The effects of monetary and non-monetary incentives on respondent attrition in longitudinal survey."</i> ., ,Faculty of Science ,Department of Computer Science, 2019. http://hdl.handle.net/11427/31393 | en_ZA |
| dc.identifier.citation | Choga, N.N. 2019. The effects of monetary and non-monetary incentives on respondent attrition in longitudinal survey. . ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/31393 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Choga, Ngonidzashe Nicholas AB - Longitudinal studies are essential for governments and organizations as they help in making decisions that are based on factual data. Longitudinal studies collect data repeatedly from a set of participants over a period of time, enabling the tracking and studying of entity behaviour at individual, organizational, and national levels. One major challenge facing longitudinal data collection is the attrition of subjects during the course of the study, which is the continuous loss of participants during a longitudinal survey due to verbal drop-outs and non-response. Attrition can render datasets useless due to incomplete entries, making it one of the most significant weaknesses of longitudinal surveys. In order to explore the effects of incentives on attrition, this research project studies the effects of monetary and non-monetary incentives on explicit (subject says s/he does not want to be part of the study) and implicit (non-response) attrition. In particular, this study uses telephonically delivered feedback, derived from participant responses, as non-monetary incentives. To measure the effects of incentives on attrition, the study gave four treatments groups —50 participants each—mobile credit, verbally delivered feedback, machine delivered feedback and no incentive. After monitoring their attrition, over a 12-week period that involved bi-weekly surveys, a generalised linear model and Cochran’s q-test were used to find that monetary incentives remain the strongest in under-served community settings. It was not only found that monetary incentive treatments completed the most surveys most weeks, but also had the least explicit attrition. Surprisingly it was also found that machine delivered feedback performed similarly to mobile credit when the cost, social impact and participant behaviour in terms of their survey completion and attrition is assessed. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Computer Science LK - https://open.uct.ac.za PY - 2019 T1 - The effects of monetary and non-monetary incentives on respondent attrition in longitudinal survey TI - The effects of monetary and non-monetary incentives on respondent attrition in longitudinal survey UR - http://hdl.handle.net/11427/31393 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/31393 | |
| dc.identifier.vancouvercitation | Choga NN. The effects of monetary and non-monetary incentives on respondent attrition in longitudinal survey. []. ,Faculty of Science ,Department of Computer Science, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31393 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Computer Science | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Computer Science | |
| dc.title | The effects of monetary and non-monetary incentives on respondent attrition in longitudinal survey | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc |