Adult Numeracy Skill Practice by STEM and Non-STEM Workers in the USA: An Exploration of Data using Latent Class Analysis
dc.contributor.author | Yamashita, Takashi | |
dc.contributor.author | Punksungka, Wonmai | |
dc.contributor.author | Narine, Donnette | |
dc.contributor.author | Helsinger, Abigail | |
dc.contributor.author | Kramer, Jenna W. | |
dc.contributor.author | Cummins, Phyllis A. | |
dc.contributor.author | Karam, Rita | |
dc.date.accessioned | 2022-12-14T18:37:36Z | |
dc.date.available | 2022-12-14T18:37:36Z | |
dc.date.issued | 2022-11-14 | |
dc.description.abstract | Adult numeracy is one of the essential skill sets to navigate through numeric information-rich labour markets in general, and STEM industries in particular. Yet, relatively little is known about how numeracy skills are used in different settings in the USA. This study examined numeracy skill use patterns of STEM and non-STEM workers at work and home. Data were obtained from the 2012/2014/2017 Program for International Assessment of Adult Competencies, USA restricted-use file. Adults who were employed and aged between 25 and 65 years old (n = 5,220) were included in this study. Latent class analysis revealed four numeracy skill use patterns: non-users, non-occupational (i.e. at home) simple numeracy users, ubiquitous numeracy users, and occupational numeracy users. Additional multinomial logistic regression analysis showed that the STEM occupation was associated with a greater likelihood of being ubiquitous users than being non-occupational simple users. Results also showed that numeracy proficiency, socioeconomic statuses (i.e. educational attainment and income), as well as demographic characteristics (i.e. gender and race/ethnicity), were predictive of the numeracy skill use patterns in terms of the level of engagement and settings. Findings from this study inform policies and interventions which promote skill engagement and improvement among workers in the USA. | en_US |
dc.description.sponsorship | In this research, Takashi Yamashita, Wonmai Punksungka, Donnette Narine, Abigail Helsinger, Jenna W. Kramer, Phyllis A. Cummins & Rita Karam, were partially supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A200261 to University of Maryland, Baltimore County. The opinions expressed are those of the authors and do not represent the views of the institute or the U.S. Department of Education. | en_US |
dc.description.uri | https://www.tandfonline.com/doi/full/10.1080/02601370.2022.2146772 | en_US |
dc.format.extent | 40 pages | en_US |
dc.genre | journal articles | en_US |
dc.genre | postprints | en_US |
dc.identifier | doi:10.13016/m2kl48-r4ke | |
dc.identifier.citation | Takashi Yamashita, Wonmai Punksungka, Donnette Narine, Abigail Helsinger, Jenna W. Kramer, Phyllis A. Cummins & Rita Karam (2022) Adult Numeracy Skill Practice by STEM and Non-STEM Workers in the USA: An Exploration of Data using Latent Class Analysis, International Journal of Lifelong Education, DOI: 10.1080/02601370.2022.2146772 | en_US |
dc.identifier.uri | https://doi.org/10.1080/02601370.2022.2146772 | |
dc.identifier.uri | http://hdl.handle.net/11603/26461 | |
dc.language.iso | en_US | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Sociology and Anthropology Department Collection | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.relation.ispartof | UMBC Gerontology Program | |
dc.relation.ispartof | UMBC Maryland Institute for Policy Analysis & Research (MIPAR) | |
dc.relation.ispartof | UMBC Student Collection | |
dc.rights | This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Lifelong Education on 14 Nov 2022, available online: http://www.tandfonline.com/10.1080/02601370.2022.2146772. | en_US |
dc.rights | Access to this item will begin on 05/14/2024 | |
dc.title | Adult Numeracy Skill Practice by STEM and Non-STEM Workers in the USA: An Exploration of Data using Latent Class Analysis | en_US |
dc.type | Text | en_US |
dcterms.creator | https://orcid.org/0000-0003-2325-126X | en_US |
dcterms.creator | https://orcid.org/0000-0003-2982-4129 | en_US |
dcterms.creator | https://orcid.org/0000-0003-2748-1812 | en_US |
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