Visualization of latent components assessed in O*Net occupations (VOLCANO): A robust method for standardized conversion of occupational labels to ratio scale format

Yu, Ju-Chi; Sokolowski, H. Moriah; Rao, Kirthana S; Moraglia, Luke E.; Khoubrouy, Soudeh A.; Abdi, Hervé; Levine, Brian

doi:10.3758/s13428-022-02044-7

Cited by 2 publications

(5 citation statements)

References 39 publications

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“…298 occupations were randomly selected to appear in the rating sets for all raters, allowing us to assess inter-rater reliability using Krippendorff's ( 2004) alpha which had an acceptable value of α = .717. For this study, we analyzed the first two dimensions derived from the O*NET database restricted to Job Zones 4-5 (i.e., requiring post-secondary education) across the entire universe of occupations on the O*NET database (Yu et al, 2023). Dimension 1 characterized the degree to which the occupation requires STEM versus humanities or social sciences abilities (accounting for 27% of the variance).…”

Section: Methodsmentioning

confidence: 99%

“…Course codes from the four years of transcripts were categorized as falling within one of eight O*NET clusters from the Job Zone 4-5 PCA using the VOLCANO app (Yu et al, 2023): Business Government, Engineering, Environment, Medical Science-General, Medical Science-Specific, Science and Math/Computational Science, and Social Science. There were no courses that matched the O*NET clusters Alternative Therapy and Sales and Logistics.…”

Section: Undergraduate Samplementioning

confidence: 99%

“…To evaluate this hypothesis, we explored how individual differences in spatial and object imagery relate to occupation in participants who completed the Object-Spatial Imagery Questionnaire (OSIQ) and whose occupation was coded according to the US Dept of Labor's Occupational Classification Network (O*NET) database (US Department of Labor, 2019), which provides ratings for 966 occupations across 120 occupation skills or traits. O*NET-coded occupations were converted into continuous scores using the Visualization of Latent Components Assessed in O*Net Occupations (VOLCANO) method (Yu et al, 2023), which quantifies occupations within a multivariate space according to the skills (including cognitive traits) required for each occupation. We focused on two orthogonal dimensions derived from the universe of occupational characteristics coded in O*NET, one reflecting STEM characteristics in general (as contrasted to humanities) and one reflecting a distinction between computational versus health sciences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Visual imagery and STEM occupational attainment: Gender matters

Sokolowski,

Fan,

et al. 2024

Preprint

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Science, technology, engineering and mathematical (STEM) occupations are crucial for economic growth and individual financial stability, yet there is a STEM labour shortage, particularly among women. We examined how individual differences in visual imagery relate to characteristics of STEM occupations — specifically those requiring computational abilities. In a discovery cohort of 2357 online participants, we found that — consistent with prior research — spatial thinking was positively associated with STEM occupations for both males and females. Object imagery (mnemonic vividness), however, was negatively associated with STEM occupations that require computational thinking, possibly because efficient analytical reasoning abilities associate with low object imagery. This negative association was present for males, but not for females. We extended these findings to a sample of 1891 individuals with aphantasia (congenitally low imagery) and a sample of 186 university undergraduates. These results suggest that the well-known influence of spatial imagery is evident across genders, whereas an independent influence of non-spatial and non-visual abstract analytic abilities on computational STEM professions is confined to males. These findings have implications for policy in fostering careers in STEM, particularly for females.

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Section: Methodsmentioning

confidence: 99%

Section: Undergraduate Samplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Visual imagery and STEM occupational attainment: Gender matters

Sokolowski,

Fan,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The Occupational Classification Network (O*NET) data, which indexes detailed occupational attributes for US occupations, has been widely used in grouping occupations and examining the job characteristics' relatedness [33][34][35][36][37][38][39][40][41][42][43]. Hundreds of attributes, termed as descriptors, are divided into different dimensions, such as knowledge, skills, abilities, education, experience, training, interests, work values, work styles, tasks, technology skills & tools, work activities, and work contents.…”

Section: Plos Onementioning

confidence: 99%

“…Burrus et al [ 39 ] used “knowledge,” “skill,” “ability,” and “work style.” Manzella et al [ 40 ] used structured and unstructured factor analysis to group occupations based on “task” descriptors. Yu et al [ 41 ] used PCA with a hierarchical cluster analysis to group occupations at two levels. Their analysis was based on descriptors of “knowledge,” “skills,” “abilities,” and “training,” but the higher-level clusters considered only job training requirements.…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of technological change on similar occupations: Implications for employment alternatives

Torosyan,

Wang,

Mack

et al. 2023

PLoS ONE

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Background The fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations. Method We first demonstrated a new method that clusters 756 occupation titles based on knowledge, skills, abilities, education, experience, training, activities, values, and interests. We used the Principal Component Analysis to categorize occupations in the Standard Occupational Classification, which is grouped into a four-level hierarchy. Then, we paired the occupation clusters with the occupational employment projections provided by the U.S. Bureau of Labor Statistics. We analyzed how employment would change and what factors affect the employment changes within occupation groups. Particularly, we specified factors related to technological changes. Results The results reveal that technological change accounts for significant job losses in some clusters. This poses occupational mobility challenges for workers in these jobs at present. Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Meanwhile, many mid-skilled and highly skilled jobs are projected to grow in the next ten years. Conclusion Our results demonstrate the utility of our occupational classification scheme. Furthermore, it suggests a critical need for skills upgrading and workforce development for workers in declining jobs. Special attention should be paid to vulnerable workers, such as older individuals and minorities.

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Visualization of latent components assessed in O*Net occupations (VOLCANO): A robust method for standardized conversion of occupational labels to ratio scale format

Cited by 2 publications

References 39 publications

Visual imagery and STEM occupational attainment: Gender matters

Visual imagery and STEM occupational attainment: Gender matters

Assessing the impact of technological change on similar occupations: Implications for employment alternatives

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