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How school climate relates to chronic absence: A multi–level latent profile analysis
Abstract
Chronic absence is a significant problem in schools. School climate may play an important role in influencing chronic absence rates among schools, yet little research has evaluated how school climate constructs relate to chronic absence. Using multilevel latent profile analysis, we evaluated how profiles of student perceptions of school climate at both the student and school level differentiated school-level rates of chronic absence. Participants included 25,776 middle and high school students from 106 schools who completed a district administered school climate survey. Students attended schools in a large urban school district where 89% of 6th through 12th grade students were African-American and 61% were eligible for the federally subsidized school meals program. Three student-level profiles of perceptions of school climate emerged that corresponded to "positive," "moderate," and "negative" climate. Two predominant patterns regarding the distribution of these profiles within schools emerged that corresponded to the two school-level profiles of "marginal climate" and "climate challenged" schools. Students reporting "moderate" and "negative" climate in their schools were more likely to attend schools with higher chronic absence rates than students reporting that their school had "positive" climate. Likewise, "climate challenged" schools had significantly higher chronic absence rates than "marginal climate" schools. These results suggest that school climate shares an important relation with chronic absence among adolescent students attending urban schools. Implications for prevention and intervention programs are discussed.
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Categories of risk and contextual factors for SA/A are sometimes studied singularly, though many recent approaches have utilized more sophisticated multilevel modeling and related statistical procedures to examine these categories collectively.
“Categories of risk and contextual factors for SA/A are sometimes studied singularly (e.g., Hendron and Kearney, 2016), though many recent approaches have utilized more sophisticated multilevel modeling and related statistical procedures to examine these categories collectively (Dembo et al., 2016; Van Eck et al., 2017; Ramberg et al., 2019).”
From Reconciling Contemporary Approaches to School Attendance and School Absenteeism: Toward Promotion and Nimble Response, Global Policy Review and Implementation, and Future Adaptability (Part 1) (2019) · cites this paper as Van Eck et al., 2017
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References 72 · Cited by 159
Research works printed but not linked: 2
- Every Student Succeeds Act, Pub. L. No. 114-95, § 1005, 129 Stat. 1802, 1848 (2015) (codified at 20 U.S.C. § 6311(h)(1)(C)(viii)(I)).
- Neild, 2007, An early warning system, Educational Leadership, 65, 28
Metadata
- Authors
- Van Eck, K., Johnson, S. R., Bettencourt, A., & Johnson, S. L.
- Affiliations
- Johns Hopkins University (US)
- Venue
- Journal of School Psychology
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 61
- Pages
- 89-102
- ISSN
- 0022-4405 (ISSN-L)
- OpenAlex
- W2550355017
- Cited by
- 159
- References
- 72
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 21 · 2023: 13 · 2022: 18 · 2021: 16 · 2020: 19 · 2019: 15 · 2018: 26 · 2017: 3
- FWCI
- 65.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of School Psychology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Energy
- Centers for Disease Control and Prevention
- Topics
- Social Sciences › Education › Youth Substance Use and School Attendance
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › School Health and Nursing Education
- Social Sciences › Education › Educational Environments and Student Outcomes
- Keywords
- school climate, chronic absenteeism, urban schools, adolescent students, African American students, prevention and intervention programs
- MeSH terms
- Absenteeism, Adolescent, Adolescent Behavior, Child, Child Behavior, Female, Humans, Male, Schools, Students, Models, Statistical
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