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A latent class analysis of self‐identified reasons for experiencing homelessness: Opportunities for prevention
Author(s) -
Barile John P.,
Pruitt Anna Smith,
Parker Josie L.
Publication year - 2018
Publication title -
journal of community and applied social psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.042
H-Index - 63
eISSN - 1099-1298
pISSN - 1052-9284
DOI - 10.1002/casp.2343
Subject(s) - latent class model , mental health , psychology , depression (economics) , quality of life (healthcare) , clinical psychology , gerontology , psychiatry , medicine , statistics , mathematics , psychotherapist , economics , macroeconomics
Individuals experience homelessness due to a vast number of factors. Therefore, the methods used to prevent individuals from experiencing homelessness should match their diverse needs. This study utilized survey data obtained from 577 adults experiencing homelessness to identify self‐reported causes of homelessness. A latent class analysis was conducted in order to identify classes or subgroups of respondents with distinct patterns of reported causes of homelessness. A latent class analysis is a person‐centred statistical approach that is used to determine groups of individuals who share similar characteristics. Findings from this analysis identified 5 distinct classes based on individuals' responses to 19 potential vulnerabilities or events that contributed to experiencing homelessness. Individuals tended to cluster around issues associated with (a) disability or physical health issues (4%), (b) substance abuse or mental health issues (30%), (c) report major life changes (3%), (d) financial crises (7%), or (e) employment difficulties (55%). Significant group differences occurred across military veteran status, history of homelessness, depression, and health‐related quality of life. Results for these analyses suggest that individuals report notable differences in their reasons for becoming homeless and therefore require unique preventative solutions.