IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0301549.html
   My bibliography  Save this article

Identifying multilevel predictors of behavioral outcomes like park use: A comparison of conditional and marginal modeling approaches

Author

Listed:
  • Marilyn E Wende
  • S Morgan Hughey
  • Alexander C McLain
  • Shirelle Hallum
  • J Aaron Hipp
  • Jasper Schipperijn
  • Ellen W Stowe
  • Andrew T Kaczynski

Abstract

This study compared marginal and conditional modeling approaches for identifying individual, park and neighborhood park use predictors. Data were derived from the ParkIndex study, which occurred in 128 block groups in Brooklyn (New York), Seattle (Washington), Raleigh (North Carolina), and Greenville (South Carolina). Survey respondents (n = 320) indicated parks within one half-mile of their block group used within the past month. Parks (n = 263) were audited using the Community Park Audit Tool. Measures were collected at the individual (park visitation, physical activity, sociodemographic characteristics), park (distance, quality, size), and block group (park count, population density, age structure, racial composition, walkability) levels. Generalized linear mixed models and generalized estimating equations were used. Ten-fold cross validation compared predictive performance of models. Conditional and marginal models identified common park use predictors: participant race, participant education, distance to parks, park quality, and population >65yrs. Additionally, the conditional mode identified park size as a park use predictor. The conditional model exhibited superior predictive value compared to the marginal model, and they exhibited similar generalizability. Future research should consider conditional and marginal approaches for analyzing health behavior data and employ cross-validation techniques to identify instances where marginal models display superior or comparable performance.

Suggested Citation

  • Marilyn E Wende & S Morgan Hughey & Alexander C McLain & Shirelle Hallum & J Aaron Hipp & Jasper Schipperijn & Ellen W Stowe & Andrew T Kaczynski, 2024. "Identifying multilevel predictors of behavioral outcomes like park use: A comparison of conditional and marginal modeling approaches," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-18, April.
    • Handle: RePEc:plo:pone00:0301549
    DOI: 10.1371/journal.pone.0301549
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0301549
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0301549&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0301549?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Klaus Larsen & Jørgen Holm Petersen & Esben Budtz-Jørgensen & Lars Endahl, 2000. "Interpreting Parameters in the Logistic Regression Model with Random Effects," Biometrics, The International Biometric Society, vol. 56(3), pages 909-914, September.
    2. Zoe M Volenec & Joel O Abraham & Alexander D Becker & Andy P Dobson, 2021. "Public parks and the pandemic: How park usage has been affected by COVID-19 policies," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-18, May.
    3. Robert James Schneider, 2013. "Measuring transportation at a human scale: An intercept survey approach to capture pedestrian activity," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 6(3), pages 43-59.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alejandra Tapia & Victor Leiva & Maria del Pilar Diaz & Viviana Giampaoli, 2019. "Influence diagnostics in mixed effects logistic regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 920-942, September.
    2. Carolina Mayen Huerta & Ariane Utomo, 2022. "Barriers Affecting Women’s Access to Urban Green Spaces during the COVID-19 Pandemic," Land, MDPI, vol. 11(4), pages 1-18, April.
    3. Alexandra Jane Crossley & Alessio Russo, 2022. "Has the Pandemic Altered Public Perception of How Local Green Spaces Affect Quality of Life in the United Kingdom?," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    4. Henry R. Scharf & Xinyi Lu & Perry J. Williams & Mevin B. Hooten, 2022. "Constructing Flexible, Identifiable and Interpretable Statistical Models for Binary Data," International Statistical Review, International Statistical Institute, vol. 90(2), pages 328-345, August.
    5. Yifeng Liu & Yuan Lai, 2024. "Analyzing jogging activity patterns and adaptation to public health regulation," Environment and Planning B, , vol. 51(3), pages 670-688, March.
    6. Sulaimon T Adedokun & Victor T Adekanmbi & Olalekan A Uthman & Richard J Lilford, 2017. "Contextual factors associated with health care service utilization for children with acute childhood illnesses in Nigeria," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
    7. Amy E. Wagler, 2014. "Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models," Journal of Educational and Behavioral Statistics, , vol. 39(3), pages 167-179, June.
    8. Diaz, Rafael E., 2007. "Comparison of PQL and Laplace 6 estimates of hierarchical linear models when comparing groups of small incident rates in cluster randomised trials," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2871-2888, March.
    9. Xiangyou Shen & Megan MacDonald & Samuel W. Logan & Colby Parkinson & Lydia Gorrell & Bridget E. Hatfield, 2022. "Leisure Engagement during COVID-19 and Its Association with Mental Health and Wellbeing in U.S. Adults," IJERPH, MDPI, vol. 19(3), pages 1-22, January.
    10. Mariangela Sciandra & Vito Muggeo & Gianfranco Lovison, 2008. "Subject-specific odds ratios in binomial GLMMs with continuous response," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(3), pages 309-320, July.
    11. Elizabeth Ayers & Sophia Rabe-Hesketh & Rebecca Nugent, 2013. "Incorporating Student Covariates in Cognitive Diagnosis Models," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 195-224, July.
    12. Bruce J. Swihart & Brian S. Caffo & Ciprian M. Crainiceanu, 2014. "A Unifying Framework for Marginalised Random-Intercept Models of Correlated Binary Outcomes," International Statistical Review, International Statistical Institute, vol. 82(2), pages 275-295, August.
    13. Sundquist, Jan & Johansson, Sven-Erik & Yang, Min & Sundquist, Kristina, 2006. "Low linking social capital as a predictor of coronary heart disease in Sweden: A cohort study of 2.8 million people," Social Science & Medicine, Elsevier, vol. 62(4), pages 954-963, February.
    14. Cristiano C. Santos & Rosangela H. Loschi, 2017. "Maximum likelihood estimation and parameter interpretation in elliptical mixed logistic regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 209-230, March.
    15. Merlo, Juan & Viciana-Fernández, Francisco J. & Ramiro-Fariñas, Diego, 2012. "Bringing the individual back to small-area variation studies: A multilevel analysis of all-cause mortality in Andalusia, Spain," Social Science & Medicine, Elsevier, vol. 75(8), pages 1477-1487.
    16. Sarina Dass & Daniel T. O’Brien & Alina Ristea, 2023. "Strategies and inequities in balancing recreation and COVID exposure when visiting green spaces," Environment and Planning B, , vol. 50(5), pages 1161-1177, June.
    17. John A. Kupfer & Zhenlong Li & Huan Ning & Xiao Huang, 2021. "Using Mobile Device Data to Track the Effects of the COVID-19 Pandemic on Spatiotemporal Patterns of National Park Visitation," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    18. Glen McGee & Jonathan Schildcrout & Sharon‐Lise Normand & Sebastien Haneuse, 2020. "Outcome‐dependent sampling in cluster‐correlated data settings with application to hospital profiling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 379-402, January.
    19. Elizabeth Tighe & David Livert & Melissa Barnett & Leonard Saxe, 2010. "Cross-Survey Analysis to Estimate Low-Incidence Religious Groups," Sociological Methods & Research, , vol. 39(1), pages 56-82, August.
    20. John‐Patrick Paraskevas & Stephanie Eckerd & Curtis M. Grimm, 2022. "Driving cooperative actions: A multimethod study of the temporal duration of unilateral commitments," Journal of Supply Chain Management, Institute for Supply Management, vol. 58(3), pages 3-22, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0301549. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.