IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i5p2338-d507346.html
   My bibliography  Save this article

Economic Stressors, COVID-19 Attitudes, Worry, and Behaviors among U.S. Working Adults: A Mixture Analysis

Author

Listed:
  • Andrea Bazzoli

    (Department of Psychology, Washington State University Vancouver, Vancouver, WA 98686, USA)

  • Tahira M. Probst

    (Department of Psychology, Washington State University Vancouver, Vancouver, WA 98686, USA)

  • Hyun Jung Lee

    (Department of Psychology, Washington State University Vancouver, Vancouver, WA 98686, USA)

Abstract

Since the unfolding of the novel coronavirus global pandemic, public health research has increasingly suggested that certain groups of individuals may be more exposed to the virus. The aim of this contribution was to investigate whether workers grouped into several latent classes, based on two perceived economic stressors, would report different levels of enactment of the Centers for Disease Control (CDC) recommended behaviors to prevent the spread of such virus. We also tested propositions regarding the potential differential predictors of compliance behavior, differentiating between cognitive (i.e., attitudes toward the CDC guidelines) and affective (i.e., COVID-specific worry) predictors. Using a longitudinal dataset of 419 U.S. workers, we did not find significant differences among the levels of CDC guidelines enactment across three latent classes, representing a range of economic vulnerability. We found that cognitive attitudes were a significantly stronger predictor of compliance with CDC guidelines for workers in the most economically secure class, whereas worry was a significantly stronger predictor of compliance for the most vulnerable counterpart. We discuss these findings in light of the Conservation of Resources theory and other health behavior theories, being mindful of the need to further understand the differential impact of this health and economic crisis on employees facing economic stressors.

Suggested Citation

  • Andrea Bazzoli & Tahira M. Probst & Hyun Jung Lee, 2021. "Economic Stressors, COVID-19 Attitudes, Worry, and Behaviors among U.S. Working Adults: A Mixture Analysis," IJERPH, MDPI, vol. 18(5), pages 1-14, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2338-:d:507346
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/5/2338/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/5/2338/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Hsinkuang Chi & Thinh-Van Vu & Tan Vo-Thanh & Nguyen Phong Nguyen & Duy van Nguyen, 2020. "Workplace health and safety training, employees’ risk perceptions, behavioral safety compliance, and perceived job insecurity during COVID-19: Data of Vietnam," Post-Print hal-03403852, HAL.
    3. Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrea Bazzoli & Tahira M. Probst & Jasmina Tomas, 2022. "A Latent Profile Analysis of Precarity and Its Associated Outcomes: The Haves and the Have-Nots," IJERPH, MDPI, vol. 19(13), pages 1-13, June.
    2. Xinyue Wen & Ismaël Rafaï & Sébastien Duchêne & Marc Willinger, 2022. "Did Mindful People Do Better during the COVID-19 Pandemic? Mindfulness Is Associated with Well-Being and Compliance with Prophylactic Measures," IJERPH, MDPI, vol. 19(9), pages 1-25, April.
    3. Lixin Jiang & Erica L. Bettac & Hyun Jung Lee & Tahira M. Probst, 2022. "In Whom Do We Trust? A Multifoci Person-Centered Perspective on Institutional Trust during COVID-19," IJERPH, MDPI, vol. 19(3), pages 1-20, February.

    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. Jennifer Oser & Marc Hooghe & Zsuzsa Bakk & Roberto Mari, 2023. "Changing citizenship norms among adolescents, 1999-2009-2016: A two-step latent class approach with measurement equivalence testing," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4915-4933, October.
    2. Lecegui, Antonio & Olaizola, Ana María & López-i-Gelats, Feliu & Varela, Elsa, 2022. "Implementing the livelihood resilience framework: An indicator-based model for assessing mountain pastoral farming systems," Agricultural Systems, Elsevier, vol. 199(C).
    3. Aely Park & Youngmi Kim & Jennifer Murphy, 2023. "Adverse Childhood Experiences and Substance Use Among Korean College Students: Different by Gender?," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 16(4), pages 1811-1825, August.
    4. Zsuzsa Bakk & Jouni Kuha, 2018. "Two-Step Estimation of Models Between Latent Classes and External Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 871-892, December.
    5. Bakk, Zsuzsa & Kuha, Jouni, 2020. "Relating latent class membership to external variables: an overview," LSE Research Online Documents on Economics 107564, London School of Economics and Political Science, LSE Library.
    6. Gugerty, Mary Kay & Mitchell, George E. & Santamarina, Francisco J., 2021. "Discourses of evaluation: Institutional logics and organizational practices among international development agencies," World Development, Elsevier, vol. 146(C).
    7. Paweł A. Atroszko & Bartosz Atroszko & Edyta Charzyńska, 2021. "Subpopulations of Addictive Behaviors in Different Sample Types and Their Relationships with Gender, Personality, and Well-Being: Latent Profile vs. Latent Class Analysis," IJERPH, MDPI, vol. 18(16), pages 1-29, August.
    8. Konte M., 2014. "Do remittances not promote growth? : a bias-adjusted three-step mixture-of-regressions," MERIT Working Papers 2014-075, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    9. F. J. Clouth & S. Pauws & F. Mols & J. K. Vermunt, 2022. "A new three-step method for using inverse propensity weighting with latent class analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 351-371, June.
    10. Lucy Prior, 2021. "Allostatic Load and Exposure Histories of Disadvantage," IJERPH, MDPI, vol. 18(14), pages 1-17, July.
    11. Bakk, Zsuzsa & Kuha, Jouni, 2018. "Two-step estimation of models between latent classes and external variables," LSE Research Online Documents on Economics 85161, London School of Economics and Political Science, LSE Library.
    12. Yajing Zhu & Fiona Steele & Irini Moustaki, 2020. "A multilevel structural equation model for the interrelationships between multiple latent dimensions of childhood socio‐economic circumstances, partnership transitions and mid‐life health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1029-1050, June.
    13. Martin Lukac & Nadja Doerflinger & Valeria Pulignano, 2019. "Developing a Cross-National Comparative Framework for Studying Labour Market Segmentation: Measurement Equivalence with Latent Class Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(1), pages 233-255, August.
    14. Jason V. Chen & Kurt H. Gee & Jed J. Neilson, 2021. "Disclosure Prominence and the Quality of Non‐GAAP Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 59(1), pages 163-213, March.
    15. Lekkas, Peter & Howard, Natasha J & Stankov, Ivana & daniel, mark & Paquet, Catherine, 2019. "A Longitudinal Typology of Neighbourhood-level Social Fragmentation: A Finite Mixture Model Approach," SocArXiv 56x9c, Center for Open Science.
    16. Zhu, Yajing & Steele, Fiona & Moustaki, Irini, 2017. "A general 3-step maximum likelihood approach to estimate the effects of multiple latent categorical variables on a distal outcome," LSE Research Online Documents on Economics 81850, London School of Economics and Political Science, LSE Library.
    17. Seohee Park & Seongeun Kim & Ji Hoon Ryoo, 2020. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    18. Brewsaugh, Katrina & Masyn, Katherine E. & Salloum, Alison, 2018. "Child welfare workers' sexism and beliefs about father involvement," Children and Youth Services Review, Elsevier, vol. 89(C), pages 132-144.
    19. Kim, Hyun Ju & Chung, Jae Young, 2020. "Factors affecting youth citizenship in accordance with socioeconomic background," Children and Youth Services Review, Elsevier, vol. 111(C).
    20. Qianru Liang & Jimmy de la Torre & Nancy Law, 2023. "Latent Transition Cognitive Diagnosis Model With Covariates: A Three-Step Approach," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 690-718, December.

    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:gam:jijerp:v:18:y:2021:i:5:p:2338-:d:507346. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.