Factor Structure and Gender Invariance of the Negative Outcome Expectations Scale Among Latiné Engineering College Students
Bibliographic Data
| ID | 22420454 |
|---|---|
| Authors | Han Na Suh (0000-0003-2646-616X, University of Hawaii at Hilo, corresponding author), Lisa Y Flores (0000-0002-4585-0952, University of Missouri), Rachel L Navarro (0000-0002-8400-5428, University of North Dakota) |
| Year | 2026 |
| Publication date | 2026-05-25 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Career Assessment (JOURNAL) |
| Journal identifiers | ISSN: 1069-0727 • E-ISSN: 1552-4590 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/10690727261454662 |
| OpenAlex | W7162337111 |
| Language | EN |
| References cited | 44 |
Using a sample of 883 Latiné engineering students, the present study explored the measurement characteristics of the Negative Outcome Expectations Scale for Engineers (NOES-E), applying advanced psychometric techniques, including bifactor and the exploratory structural equation modeling (ESEM) framework, to ascertain the scale’s most suitable factor structure. Additionally, measurement invariance across Latino and Latina participants was tested to investigate if the scale operates equally across gender groups and whether the latent mean scores are invariant. The results suggested that the bi-factor ESEM provided the best model fit when contrasted with other possible factor structures. The findings generally highlighted the general factor as reflecting the negative outcome expectations, suggesting that the use of total score is advised over the use of individual subscale scores. Findings from the measurement invariance testing across men and women indicated the NOES-E score can be compared between both groups.
Confirmatory factor analysis · Exploratory factor analysis · Measurement invariance · Structural equation modeling · Career Development and Diversity · Education, Achievement, and Giftedness · Psychometric Methodologies and Testing
Scoring and Modeling Psychological Measures in the Presence of Multidimensionality
Sources of self-efficacy and outcome expectations for career exploration and decision-making
Predictors of science, technology, engineering, and mathematics choice options
Best practices for missing data management in counseling psychology.
Model selection and psychological theory
Exploratory Structural Equation Modeling
Evaluating bifactor models
A new look at the big five factor structure through exploratory structural equation modeling.
Contextual supports and barriers to career choice
Linking intersectional invisibility and hypervisibility to experiences of microaggressions among graduate women of color in STEM
Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance
Cutoff criteria for fit indexes in covariance structure analysis
Toward a Unifying Social Cognitive Theory of Career and Academic Interest, Choice, and Performance
Exploratory Structural Equation Modeling
A comparison of factor structure models for the Mental Health Continuum-Short Form (MHC-SF) among preschool teachers in South Korea
Critical contributions to scholarship on women and work
Social cognitive predictors of Latinx and White engineering students' academic satisfaction and persistence intentions
Mean and Covariance Structures (Macs) Analyses of Cross-Cultural Data
The Rediscovery of Bifactor Measurement Models
Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (Esem), and Set-Esem
Measurement invariance conventions and reporting
Measurement invariance of big-five factors over the life span
Microaggressions and Ethnic Identity in the Career Development of Latina College Students
Vocational Psychology
A power primer
| Citation velocity | historical |
|---|---|
| Highly cited | No |