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Cited by 344

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Longitudinal Analysis

·2021·Big Data Mining and Complexity
DOI 10.4135/9781529682755.n10
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Abstract

This paper identifies factors which differentiate elderly residential movers and non- movers. Longitudinal data were used in the analysis. Logistic regression results showed that length of residency, home ownership, use of community support services, and number of adult children each had an effect on relocation. Respondents who reported longer lengths of residency and those who owned a home were less likely to relocate. Those elders who utilized more community support services were also less likely to move. Conversely, elders with greater numbers of adult children were more likely to relocate. There was not a significant effect of health status on relocation.

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Metadata

Venue
Big Data Mining and Complexity
Publisher
SAGE Publications Ltd
Published
2021
Pages
115-130
DOI
10.4135/9781529682755.n10
OpenAlex
W2510113425
Cited by
344
References
14
FWCI
5.41
Type
book chapter
Language
en
Open access
closed

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CoChat. (2026). Citation check: Longitudinal Analysis. https://app.cochat.ai/papers/10.4135/9781529682755.n10
The paper
Longitudinal Analysis. (2021). Big Data Mining and Complexity. https://doi.org/10.4135/9781529682755.n10

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.