Measuring geographic mobility: Comparison of estimates from longitudinal and cross-sectional data
Attrition of sample members from a longitudinal survey can undermine the quality of the data and its research potential, especially when the sample members who drop out are different from those who do not. People who move house are more likely to drop out of the survey as they are harder to locate,...
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| Format: | Article |
| Language: | English |
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European Survey Research Association
2020-04-01
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| Series: | Survey Research Methods |
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| Online Access: | https://ojs.ub.uni-konstanz.de/srm/article/view/7422 |
| _version_ | 1811214729003139072 |
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| author | Nicole Watson |
| author_facet | Nicole Watson |
| author_sort | Nicole Watson |
| collection | DOAJ |
| description | Attrition of sample members from a longitudinal survey can undermine the quality of the data and its research potential, especially when the sample members who drop out are different from those who do not. People who move house are more likely to drop out of the survey as they are harder to locate, and once located, may be harder to interview in the remaining fieldwork time available. Moving coincides with many other life events (such as changes in marital status, the birth of a child, buying a home, changes in employment, or retirement) and if movers are not adequately interviewed, this may result in the study under-representing these changes and the events that occur after a move. This paper examines the weighted estimates of the rate of moving by age in a long running household panel study, the Household, Income and Labour Dynamics in Australia (HILDA) Survey, compared to official cross-sectional data sources and probabilistically linked Census data. Geographic mobility is examined over one-, five- and 10-year periods. Some of the differences that occur in the mobility estimates is a result of item non-response or recall error in the cross-sectional sources but little evidence is found of the differential impact of attrition in the HILDA Survey. There is, however, some indication that the longitudinal survey data underrepresents long distance moves. Other differences between the data sources are investigated by fitting logistic regression models of mobility to estimate the effect of housing tenure and education levels over the life course. These models show similar overall trends, but there is some evidence of differential effects for renters with lower education levels which may be due, at least in part, to the differential role recall error plays in these measures. Overall, these findings reassure longitudinal data users of the quality of geographic mobility estimates from the HILDA Survey and encourage similar comparisons to be made for other longitudinal data sources. |
| first_indexed | 2024-04-12T06:09:32Z |
| format | Article |
| id | doaj.art-860d1291b1914eb8becfb75b0329d530 |
| institution | Directory Open Access Journal |
| issn | 1864-3361 |
| language | English |
| last_indexed | 2024-04-12T06:09:32Z |
| publishDate | 2020-04-01 |
| publisher | European Survey Research Association |
| record_format | Article |
| series | Survey Research Methods |
| spelling | doaj.art-860d1291b1914eb8becfb75b0329d5302022-12-22T03:44:44ZengEuropean Survey Research AssociationSurvey Research Methods1864-33612020-04-0114110.18148/srm/2020.v14i1.7422Measuring geographic mobility: Comparison of estimates from longitudinal and cross-sectional dataNicole Watson0University of MelbourneAttrition of sample members from a longitudinal survey can undermine the quality of the data and its research potential, especially when the sample members who drop out are different from those who do not. People who move house are more likely to drop out of the survey as they are harder to locate, and once located, may be harder to interview in the remaining fieldwork time available. Moving coincides with many other life events (such as changes in marital status, the birth of a child, buying a home, changes in employment, or retirement) and if movers are not adequately interviewed, this may result in the study under-representing these changes and the events that occur after a move. This paper examines the weighted estimates of the rate of moving by age in a long running household panel study, the Household, Income and Labour Dynamics in Australia (HILDA) Survey, compared to official cross-sectional data sources and probabilistically linked Census data. Geographic mobility is examined over one-, five- and 10-year periods. Some of the differences that occur in the mobility estimates is a result of item non-response or recall error in the cross-sectional sources but little evidence is found of the differential impact of attrition in the HILDA Survey. There is, however, some indication that the longitudinal survey data underrepresents long distance moves. Other differences between the data sources are investigated by fitting logistic regression models of mobility to estimate the effect of housing tenure and education levels over the life course. These models show similar overall trends, but there is some evidence of differential effects for renters with lower education levels which may be due, at least in part, to the differential role recall error plays in these measures. Overall, these findings reassure longitudinal data users of the quality of geographic mobility estimates from the HILDA Survey and encourage similar comparisons to be made for other longitudinal data sources.https://ojs.ub.uni-konstanz.de/srm/article/view/7422residential mobilityinternal migrationrecall errorattrition |
| spellingShingle | Nicole Watson Measuring geographic mobility: Comparison of estimates from longitudinal and cross-sectional data Survey Research Methods residential mobility internal migration recall error attrition |
| title | Measuring geographic mobility: Comparison of estimates from longitudinal and cross-sectional data |
| title_full | Measuring geographic mobility: Comparison of estimates from longitudinal and cross-sectional data |
| title_fullStr | Measuring geographic mobility: Comparison of estimates from longitudinal and cross-sectional data |
| title_full_unstemmed | Measuring geographic mobility: Comparison of estimates from longitudinal and cross-sectional data |
| title_short | Measuring geographic mobility: Comparison of estimates from longitudinal and cross-sectional data |
| title_sort | measuring geographic mobility comparison of estimates from longitudinal and cross sectional data |
| topic | residential mobility internal migration recall error attrition |
| url | https://ojs.ub.uni-konstanz.de/srm/article/view/7422 |
| work_keys_str_mv | AT nicolewatson measuringgeographicmobilitycomparisonofestimatesfromlongitudinalandcrosssectionaldata |