Using paradata to assess respondent burden and interviewer effects in household surveys: Evidence from low- and middle-income countries1.
Saved in:
| Title: | Using paradata to assess respondent burden and interviewer effects in household surveys: Evidence from low- and middle-income countries1. |
|---|---|
| Authors: | Hasanbasri, Ardina1 (AUTHOR) ardina.hasanbasri@yale.edu, Kilic, Talip2 (AUTHOR), Koolwal, Gayatri2 (AUTHOR), Moylan, Heather2 (AUTHOR) |
| Source: | Statistical Journal of the IAOS. 2024, Vol. 40 Issue 2, p247-267. 21p. |
| Subjects: | Household surveys, Telephone interviewing, Middle-income countries, Offices, Interviewers, Multilevel models, Epidemiological transition |
| Geographic Terms: | Tanzania, Ethiopia, Cambodia |
| Abstract: | Over the past decade, national statistical offices in low- and middle-income countries have increasingly transitioned to computer-assisted personal interviewing and computer-assisted telephone interviewing for the implementation of household surveys. The byproducts of these types of data collection are survey paradata, which can unlock objective, module- and question-specific, actionable insights on survey respondent burden, survey costs, and interviewer effects – all of which have been understudied in low- and middle-income contexts. This study uses paradata generated by Survey Solutions, a computer-assisted personal interviewing platform used in recent national household surveys implemented by the national statistical offices of Cambodia, Ethiopia, and Tanzania. Across countries, the average household interview, based on a socioeconomic household questionnaire, ranges from 82 to 120 minutes, while the average interview with an adult household member, based on a multi-topic individual questionnaire, takes between 13 to 25 minutes. The paper further provides guidelines on the use of paradata for module-level analysis to aid in operational survey decisions, such as using interview length to estimate unit cost for budgeting purposes as well as understanding interviewer effects using a multilevel model. Our findings, particularly by module, point to where additional interviewer training, fieldwork supervision, and data quality monitoring may be needed in future surveys. [ABSTRACT FROM AUTHOR] |
| Copyright of Statistical Journal of the IAOS is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Over the past decade, national statistical offices in low- and middle-income countries have increasingly transitioned to computer-assisted personal interviewing and computer-assisted telephone interviewing for the implementation of household surveys. The byproducts of these types of data collection are survey paradata, which can unlock objective, module- and question-specific, actionable insights on survey respondent burden, survey costs, and interviewer effects – all of which have been understudied in low- and middle-income contexts. This study uses paradata generated by Survey Solutions, a computer-assisted personal interviewing platform used in recent national household surveys implemented by the national statistical offices of Cambodia, Ethiopia, and Tanzania. Across countries, the average household interview, based on a socioeconomic household questionnaire, ranges from 82 to 120 minutes, while the average interview with an adult household member, based on a multi-topic individual questionnaire, takes between 13 to 25 minutes. The paper further provides guidelines on the use of paradata for module-level analysis to aid in operational survey decisions, such as using interview length to estimate unit cost for budgeting purposes as well as understanding interviewer effects using a multilevel model. Our findings, particularly by module, point to where additional interviewer training, fieldwork supervision, and data quality monitoring may be needed in future surveys. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 18747655 |
| DOI: | 10.3233/SJI-230042 |
