Paradata analyses to inform population-based survey capture of pregnancy outcomes : EN-INDEPTH study
BACKGROUND: Paradata are (timestamped) records tracking the process of (electronic) data collection. We analysed paradata from a large household survey of questions capturing pregnancy outcomes to assess performance (timing and correction processes). We examined how paradata can be used to inform and improve questionnaire design and survey implementation in nationally representative household surveys, the major source for maternal and newborn health data worldwide.
METHODS: The EN-INDEPTH cross-sectional population-based survey of women of reproductive age in five Health and Demographic Surveillance System sites (in Bangladesh, Guinea-Bissau, Ethiopia, Ghana, and Uganda) randomly compared two modules to capture pregnancy outcomes: full pregnancy history (FPH) and the standard DHS-7 full birth history (FBH+). We used paradata related to answers recorded on tablets using the Survey Solutions platform. We evaluated the difference in paradata entries between the two reproductive modules and assessed which question characteristics (type, nature, structure) affect answer correction rates, using regression analyses. We also proposed and tested a new classification of answer correction types.
RESULTS: We analysed 3.6 million timestamped entries from 65,768 interviews. 83.7% of all interviews had at least one corrected answer to a question. Of 3.3 million analysed questions, 7.5% had at least one correction. Among corrected questions, the median number of corrections was one, regardless of question characteristics. We classified answer corrections into eight types (no correction, impulsive, flat (simple), zigzag, flat zigzag, missing after correction, missing after flat (zigzag) correction, missing/incomplete). 84.6% of all corrections were judged not to be problematic with a flat (simple) mistake correction. Question characteristics were important predictors of probability to make answer corrections, even after adjusting for respondent's characteristics and location, with interviewer clustering accounted as a fixed effect. Answer correction patterns and types were similar between FPH and FBH+, as well as the overall response duration. Avoiding corrections has the potential to reduce interview duration and reproductive module completion by 0.4 min.
CONCLUSIONS: The use of questionnaire paradata has the potential to improve measurement and the resultant quality of electronic data. Identifying sections or specific questions with multiple corrections sheds light on typically hidden challenges in the survey's content, process, and administration, allowing for earlier real-time intervention (e.g.,, questionnaire content revision or additional staff training). Given the size and complexity of paradata, additional time, data management, and programming skills are required to realise its potential.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:19 |
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| Enthalten in: |
Population health metrics - 19(2021), Suppl 1 vom: 08. Feb., Seite 10 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gordeev, Vladimir Sergeevich [VerfasserIn] |
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| Links: |
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| Themen: |
Answer correction type |
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| Anmerkungen: |
Date Completed 28.10.2021 Date Revised 30.03.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12963-020-00241-0 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM321183029 |
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| 100 | 1 | |a Gordeev, Vladimir Sergeevich |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Paradata analyses to inform population-based survey capture of pregnancy outcomes |b EN-INDEPTH study |
| 264 | 1 | |c 2021 | |
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| 500 | |a Date Completed 28.10.2021 | ||
| 500 | |a Date Revised 30.03.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND: Paradata are (timestamped) records tracking the process of (electronic) data collection. We analysed paradata from a large household survey of questions capturing pregnancy outcomes to assess performance (timing and correction processes). We examined how paradata can be used to inform and improve questionnaire design and survey implementation in nationally representative household surveys, the major source for maternal and newborn health data worldwide | ||
| 520 | |a METHODS: The EN-INDEPTH cross-sectional population-based survey of women of reproductive age in five Health and Demographic Surveillance System sites (in Bangladesh, Guinea-Bissau, Ethiopia, Ghana, and Uganda) randomly compared two modules to capture pregnancy outcomes: full pregnancy history (FPH) and the standard DHS-7 full birth history (FBH+). We used paradata related to answers recorded on tablets using the Survey Solutions platform. We evaluated the difference in paradata entries between the two reproductive modules and assessed which question characteristics (type, nature, structure) affect answer correction rates, using regression analyses. We also proposed and tested a new classification of answer correction types | ||
| 520 | |a RESULTS: We analysed 3.6 million timestamped entries from 65,768 interviews. 83.7% of all interviews had at least one corrected answer to a question. Of 3.3 million analysed questions, 7.5% had at least one correction. Among corrected questions, the median number of corrections was one, regardless of question characteristics. We classified answer corrections into eight types (no correction, impulsive, flat (simple), zigzag, flat zigzag, missing after correction, missing after flat (zigzag) correction, missing/incomplete). 84.6% of all corrections were judged not to be problematic with a flat (simple) mistake correction. Question characteristics were important predictors of probability to make answer corrections, even after adjusting for respondent's characteristics and location, with interviewer clustering accounted as a fixed effect. Answer correction patterns and types were similar between FPH and FBH+, as well as the overall response duration. Avoiding corrections has the potential to reduce interview duration and reproductive module completion by 0.4 min | ||
| 520 | |a CONCLUSIONS: The use of questionnaire paradata has the potential to improve measurement and the resultant quality of electronic data. Identifying sections or specific questions with multiple corrections sheds light on typically hidden challenges in the survey's content, process, and administration, allowing for earlier real-time intervention (e.g.,, questionnaire content revision or additional staff training). Given the size and complexity of paradata, additional time, data management, and programming skills are required to realise its potential | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Answer correction type | |
| 650 | 4 | |a Neonatal | |
| 650 | 4 | |a Newborn | |
| 650 | 4 | |a Paradata | |
| 650 | 4 | |a Survey | |
| 650 | 4 | |a Survey design | |
| 700 | 1 | |a Akuze, Joseph |e verfasserin |4 aut | |
| 700 | 1 | |a Baschieri, Angela |e verfasserin |4 aut | |
| 700 | 1 | |a Thysen, Sanne M |e verfasserin |4 aut | |
| 700 | 1 | |a Dzabeng, Francis |e verfasserin |4 aut | |
| 700 | 1 | |a Haider, M Moinuddin |e verfasserin |4 aut | |
| 700 | 1 | |a Smuk, Melanie |e verfasserin |4 aut | |
| 700 | 1 | |a Wild, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Lokshin, Michael M |e verfasserin |4 aut | |
| 700 | 1 | |a Yitayew, Temesgen Azemeraw |e verfasserin |4 aut | |
| 700 | 1 | |a Abebe, Solomon Mokonnen |e verfasserin |4 aut | |
| 700 | 1 | |a Natukwatsa, Davis |e verfasserin |4 aut | |
| 700 | 1 | |a Gyezaho, Collins |e verfasserin |4 aut | |
| 700 | 1 | |a Amenga-Etego, Seeba |e verfasserin |4 aut | |
| 700 | 1 | |a Lawn, Joy E |e verfasserin |4 aut | |
| 700 | 1 | |a Blencowe, Hannah |e verfasserin |4 aut | |
| 700 | 0 | |a Every Newborn-INDEPTH Study Collaborative Group |e verfasserin |4 aut | |
| 700 | 1 | |a Byass, Peter |e investigator |4 oth | |
| 700 | 1 | |a Tollman, Stephen M |e investigator |4 oth | |
| 700 | 1 | |a Godefay, Hagos |e investigator |4 oth | |
| 700 | 1 | |a Lawn, Joy E |e investigator |4 oth | |
| 700 | 1 | |a Waiswa, Peter |e investigator |4 oth | |
| 700 | 1 | |a Blencowe, Hannah |e investigator |4 oth | |
| 700 | 1 | |a Yargawa, Judith |e investigator |4 oth | |
| 700 | 1 | |a Akuze, Joseph |e investigator |4 oth | |
| 700 | 1 | |a Fisker, Ane B |e investigator |4 oth | |
| 700 | 1 | |a Martins, Justiniano S D |e investigator |4 oth | |
| 700 | 1 | |a Rodrigues, Amabelia |e investigator |4 oth | |
| 700 | 1 | |a Thysen, Sanne M |e investigator |4 oth | |
| 700 | 1 | |a Biks, Gashaw Andargie |e investigator |4 oth | |
| 700 | 1 | |a Abebe, Solomon Mokonnen |e investigator |4 oth | |
| 700 | 1 | |a Ayele, Tadesse Awoke |e investigator |4 oth | |
| 700 | 1 | |a Bisetegn, Telake Azale |e investigator |4 oth | |
| 700 | 1 | |a Delele, Tadess Guadu |e investigator |4 oth | |
| 700 | 1 | |a Gelaye, Kassahun Alemu |e investigator |4 oth | |
| 700 | 1 | |a Geremew, Bisrat Misganaw |e investigator |4 oth | |
| 700 | 1 | |a Gezie, Lemma Derseh |e investigator |4 oth | |
| 700 | 1 | |a Melese, Tesfahun |e investigator |4 oth | |
| 700 | 1 | |a Mengistu, Mezgebu Yitayal |e investigator |4 oth | |
| 700 | 1 | |a Tesega, Adane Kebede |e investigator |4 oth | |
| 700 | 1 | |a Yitayew, Temesgen Azemeraw |e investigator |4 oth | |
| 700 | 1 | |a Kasasa, Simon |e investigator |4 oth | |
| 700 | 1 | |a Galiwango, Edward |e investigator |4 oth | |
| 700 | 1 | |a Gyezaho, Collins |e investigator |4 oth | |
| 700 | 1 | |a Kaija, Judith |e investigator |4 oth | |
| 700 | 1 | |a Kajungu, Dan |e investigator |4 oth | |
| 700 | 1 | |a Nareeba, Tryphena |e investigator |4 oth | |
| 700 | 1 | |a Natukwatsa, Davis |e investigator |4 oth | |
| 700 | 1 | |a Tusubira, Valerie |e investigator |4 oth | |
| 700 | 1 | |a Enuameh, Yeetey A K |e investigator |4 oth | |
| 700 | 1 | |a Asante, Kwaku P |e investigator |4 oth | |
| 700 | 1 | |a Dzabeng, Francis |e investigator |4 oth | |
| 700 | 1 | |a Etego, Seeba Amenga |e investigator |4 oth | |
| 700 | 1 | |a Manu, Alexander A |e investigator |4 oth | |
| 700 | 1 | |a Manu, Grace |e investigator |4 oth | |
| 700 | 1 | |a Nettey, Obed Ernest |e investigator |4 oth | |
| 700 | 1 | |a Newton, Sam K |e investigator |4 oth | |
| 700 | 1 | |a Owusu-Agyei, Seth |e investigator |4 oth | |
| 700 | 1 | |a Tawiah, Charlotte |e investigator |4 oth | |
| 700 | 1 | |a Zandoh, Charles |e investigator |4 oth | |
| 700 | 1 | |a Alam, Nurul |e investigator |4 oth | |
| 700 | 1 | |a Delwar, Nafisa |e investigator |4 oth | |
| 700 | 1 | |a Haider, M Moinuddin |e investigator |4 oth | |
| 700 | 1 | |a Imam, Md Ali |e investigator |4 oth | |
| 700 | 1 | |a Mahmud, Kaiser |e investigator |4 oth | |
| 700 | 1 | |a Baschieri, Angela |e investigator |4 oth | |
| 700 | 1 | |a Cousens, Simon |e investigator |4 oth | |
| 700 | 1 | |a Gordeev, Vladimir S |e investigator |4 oth | |
| 700 | 1 | |a Hardy, Victoria Ponce |e investigator |4 oth | |
| 700 | 1 | |a Kwesiga, Doris |e investigator |4 oth | |
| 700 | 1 | |a Machiyama, Kazuyo |e investigator |4 oth | |
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