A Unified Validation Framework for Assessing Green Skills Implementation from Basic to Vocational Education: A Measurement Invariance Approach

Keywords Green Skills, Sustainability Education, Measurement Invariance, Vocational Education, Psychometric Validation, Structural Equation Modelling, Curriculum Alignment ALL Metrics - Views Downloads How to cite this article Susanto A, Fithriyah NN, Pujiastuti AU et al. A Unified Validation Framework for Assessing Green Skills Implementation from Basic to Vocational Education: A Measurement Invariance Approach [version 1; peer review: 1 approved with reservations]. F1000Research 2026, 15:203 (https://doi.org/10.12688/f1000research.175658.1) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente Select a format first ▬ ✚ Research Article [version 1; peer review: 1 approved with reservations] Aris Susanto1,2, Nur Nafisatul Fithriyah3,4, Arik Umi Pujiastuti3,5, [...] Zuli Nuraeni1,6, Witri Ramadhani https://orcid.org/0009-0002-1616-7465 7,8, Fitri Alfarisa1,9, Dewi Puji Rahayu3,10, Anisah Anisah1,11, Andry Ananda Putra Tanggu Mara https://orcid.org/0009-0007-5981-6847 7,12Aris Susanto1,2, Nur Nafisatul Fithriyah3,4, [...] Arik Umi Pujiastuti3,5, Zuli Nuraeni1,6, Witri Ramadhani https://orcid.org/0009-0002-1616-7465 7,8, Fitri Alfarisa1,9, Dewi Puji Rahayu3,10, Anisah Anisah1,11, Andry Ananda Putra Tanggu Mara https://orcid.org/0009-0007-5981-6847 7,12 PUBLISHED 06 Feb 2026 Author details Author details 1 Educational Research and Evaluation, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 2 Primary School Teacher Education, STKIP Pelita Nusantara Buton, Buton, Bau Bau, Indonesia 3 Department of Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 4 Primary Madrasah Teacher Education, Universitas Nahdlatul Ulama Sidoarjo, Sidoarjo, Jawa Timur, Indonesia 5 Primary School Teacher Education, Universitas PGRI Ronggolawe Tuban, Tuban, East Java, Indonesia 6 Mathematics Education, Universitas Sriwijaya Fakultas Matematika dan Ilmu Pengetahuan Alam, Palembang, South Sumatra, Indonesia 7 Department of Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 8 Department of Vocational Education in Electronics Engineering, Universitas Muhammadiyah Riau, Pekanbaru, Riau, Indonesia 9 Primary School Teacher Education, Universitas Pendidikan Indonesia Fakultas Ilmu Pendidikan, Bandung, West Java, Indonesia 10 Teacher Professional Education, Universitas Musamus Fakultas Keguruan dan Ilmu Pendidikan, Merauke, Papua, Indonesia 11 Primary School Teacher Education, STKIP Taman Siswa Bima, Bima, West Nusa Tenggara, Indonesia 12 Information Technology Education, Universitas Stella Maris Sumba, Tambolaka, Indonesia 2 Primary School Teacher Education, STKIP Pelita Nusantara Buton, Buton, Bau Bau, Indonesia 3 Department of Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 4 Primary Madrasah Teacher Education, Universitas Nahdlatul Ulama Sidoarjo, Sidoarjo, Jawa Timur, Indonesia 5 Primary School Teacher Education, Universitas PGRI Ronggolawe Tuban, Tuban, East Java, Indonesia 6 Mathematics Education, Universitas Sriwijaya Fakultas Matematika dan Ilmu Pengetahuan Alam, Palembang, South Sumatra, Indonesia 7 Department of Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 8 Department of Vocational Education in Electronics Engineering, Universitas Muhammadiyah Riau, Pekanbaru, Riau, Indonesia 9 Primary School Teacher Education, Universitas Pendidikan Indonesia Fakultas Ilmu Pendidikan, Bandung, West Java, Indonesia 10 Teacher Professional Education, Universitas Musamus Fakultas Keguruan dan Ilmu Pendidikan, Merauke, Papua, Indonesia 11 Primary School Teacher Education, STKIP Taman Siswa Bima, Bima, West Nusa Tenggara, Indonesia 12 Information Technology Education, Universitas Stella Maris Sumba, Tambolaka, Indonesia Aris Susanto Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Project Administration, Writing – Review & Editing Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Project Administration, Writing – Review & Editing Nur Nafisatul Fithriyah Roles: Data Curation, Investigation, Resources, Validation Roles: Data Curation, Investigation, Resources, Validation Arik Umi Pujiastuti Roles: Formal Analysis, Investigation, Methodology, Validation, Writing – Review & Editing Roles: Formal Analysis, Investigation, Methodology, Validation, Writing – Review & Editing Zuli Nuraeni Roles: Data Curation, Project Administration, Resources, Software, Supervision Roles: Data Curation, Project Administration, Resources, Software, Supervision Witri Ramadhani Roles: Data Curation, Formal Analysis, Investigation, Resources, Validation, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Investigation, Resources, Validation, Writing – Review & Editing Fitri Alfarisa Roles: Conceptualization, Investigation, Resources, Validation, Writing – Review & Editing Roles: Conceptualization, Investigation, Resources, Validation, Writing – Review & Editing Dewi Puji Rahayu Roles: Formal Analysis, Investigation, Methodology, Project Administration, Resources, Writing – Review & Editing Roles: Formal Analysis, Investigation, Methodology, Project Administration, Resources, Writing – Review & Editing Anisah Anisah Roles: Funding Acquisition, Methodology, Supervision, Validation, Visualization, Writing – Review & Editing Roles: Funding Acquisition, Methodology, Supervision, Validation, Visualization, Writing – Review & Editing Andry Ananda Putra Tanggu Mara Roles: Data Curation, Software, Supervision, Validation, Writing – Review & Editing Roles: Data Curation, Software, Supervision, Validation, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS The transition toward greener economies has heightened the need for education systems to cultivate green skills from early schooling through vocational pathways. Despite growing interest, existing assessments remain fragmented and lack validation across educational levels. This study develops and validates a unified measurement framework for assessing green skills implementation across basic, lower-secondary, and vocational education. A multi-phase quantitative design was employed, including item development, expert review, exploratory factor analysis, confirmatory factor analysis, and multi-group measurement invariance testing. The final instrument comprised 36 items across six constructs: environmental awareness, sustainable behaviour, critical sustainability thinking, sustainability participation, resource management skills, and technical green practices. Using data from 215 students, the six-factor CFA model demonstrated excellent fit (CFI = .953; RMSEA = .052) and strong reliability and convergent validity across all constructs. Multi-group CFA supported configural and metric invariance, while partial scalar invariance was achieved after freeing four intercepts, enabling valid latent mean comparisons across educational levels. Results indicated a clear developmental progression of green skills, with foundational competencies stronger in primary education and technical competencies highest in vocational students. These findings confirm that green skills evolve along a continuum and can be measured consistently across schooling stages. The validated framework provides a robust tool for curriculum alignment, professional development, and monitoring sustainability competencies within national education systems. This study advances psychometric approaches in sustainability education and offers empirical guidance for policymakers seeking coherent strategies to support future green workforces. Green Skills, Sustainability Education, Measurement Invariance, Vocational Education, Psychometric Validation, Structural Equation Modelling, Curriculum Alignment Corresponding Author(s) Andry Ananda Putra Tanggu Mara ([email protected]) Grant information: The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Doctoral Scholarship Program for Indonesian Lecturers (PDDI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of the Republic of Indonesia, and the Indonesian Endowment Fund for Education (LPDP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2026 Susanto A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. How to cite: Susanto A, Fithriyah NN, Pujiastuti AU et al. A Unified Validation Framework for Assessing Green Skills Implementation from Basic to Vocational Education: A Measurement Invariance Approach [version 1; peer review: 1 approved with reservations]. F1000Research 2026, 15:203 (https://doi.org/10.12688/f1000research.175658.1) First published: 06 Feb 2026, 15:203 (https://doi.org/10.12688/f1000research.175658.1) Latest published: 06 Feb 2026, 15:203 (https://doi.org/10.12688/f1000research.175658.1) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The global shift toward greener economies has intensified the demand for competencies that enable individuals to contribute to environmentally sustainable practices. These competencies, broadly referred to as green skills, encompass knowledge, values, behaviours, and technical abilities necessary to support sustainable development across multiple sectors (Vona et al., 2015; Auktor, 2020). As societies transition toward low-carbon and circular economic systems, education systems are increasingly expected to embed green skills into formal curricula to prepare future generations for evolving ecological and labour market challenges (Albertz & Pilz, 2025). This need applies not only to vocational education—where green skills have traditionally been emphasized—but also to basic and lower-secondary education, where foundational competencies and environmental attitudes begin to form. A growing body of research underscores the strategic importance of integrating green skills into vocational education and training (VET), particularly given the sector’s proximity to industry and technical fields (Chen, 2025; Mutohhari et al., 2025). Studies in Indonesia and other developing contexts show that both teachers and students demonstrate varied levels of maturity in generic green skills, indicating uneven readiness within the VET ecosystem (Mutohhari et al., 2025). Similar findings appear in logistics and agricultural VET programs, where green skills integration differs significantly across countries and sectors (Chen, 2025; Hu et al., 2025). Beyond VET, green skills have also been linked to broader socio-economic transformation, such as sustainable tourism development (Ristanović et al., 2025) and innovation within informal economies (Manyati et al., 2024). Green skills are increasingly recognized as spanning three interconnected domains: foundational competencies, transitional or applied competencies, and technical or occupation-specific competencies (Wegenberger & Ponocny, 2025). Foundational competencies—such as environmental awareness, eco-friendly habits, and sustainability-oriented attitudes—are typically formed during basic education. Lower-secondary education fosters intermediate competencies through critical thinking, participation in sustainability projects, and contextual understanding of environmental issues. Vocational education refines these into advanced technical skills relevant to industry practices (Kamis et al., 2018; Haloho et al., 2023). This developmental trajectory highlights the importance of examining green skills not as isolated competencies within VET but as a continuum that begins early in schooling and evolves across educational stages. Despite the conceptual clarity surrounding green skills, significant challenges remain in their measurement and assessment. Existing instruments vary widely in scope, structure, and psychometric robustness. Some studies focus on validating green character or environmental behaviour instruments (Sukri et al., 2022), while others develop assessment tools for specific contexts, such as agricultural professionals (Hu et al., 2025) or sustainability practices in schools (Ismail et al., 2024). The Green Compass tool, for instance, offers self-assessment for institutional integration of green skills but is limited in its ability to compare competencies across learner groups or educational levels (Jovanovski et al., 2024). Within VET, Kamis et al. (2018) developed and validated an instrument for green skills, but the model is not designed for cross-level comparisons involving basic or lower-secondary learners. Methodological literature also emphasizes the importance of using rigorous validation approaches when measuring competencies across diverse populations. Studies employing factor analysis, Rasch modelling, and structural equation modelling (SEM) demonstrate the necessity of ensuring measurement reliability, validity, and structural consistency across groups (Sukri et al., 2022; Unfried et al., 2015; Lombardi et al., 2022). In sustainability-related contexts, measurement invariance testing is increasingly recognized as essential for comparing constructs across different demographic or institutional groups (Sepasgozar, 2023). Without establishing invariance, observed differences in green skill levels may reflect measurement bias rather than true differences in competencies. While efforts have been made to document green skills within VET systems (Chen, 2025; Mutohhari et al., 2025) and to explore curriculum integration in specific sectors (Hu et al., 2025; Chen, 2025), there remains a critical gap in developing a unified validation framework that captures the progression of green skills from basic to vocational education. Existing research tends to focus on either early-stage environmental attitudes or advanced technical competencies, resulting in fragmented understandings of how green skills evolve across educational pathways. Moreover, no available instrument has been systematically validated across primary, lower-secondary, and vocational groups using measurement invariance, a method crucial for establishing structural equivalence and ensuring meaningful cross-level comparisons. This gap is especially significant for countries like Indonesia, where the integration of sustainability practices and indigenous cultural perspectives in education is increasingly emphasized (Tanggu Mara et al., 2025). Understanding how green skills develop across schooling stages can support curriculum alignment, teacher training, and national strategies aimed at empowering future green workforces. Furthermore, international policy frameworks—including the SDG 4.7 agenda and green industrial skills initiatives—require reliable data on learners’ competencies across age groups to inform equitable and coherent sustainability education (Auktor, 2020). Given these challenges, there is a pressing need to construct and validate a unified measurement framework that assesses green skills consistently across educational levels. Such a framework must account for the developmental distinctions between basic, lower-secondary, and vocational learners while ensuring that core constructs remain conceptually and statistically comparable. Establishing measurement invariance across these levels not only enables valid comparisons but also provides empirical evidence for the continuity of green skills development within a national education system. This study aims to develop and validate a unified framework for assessing green skills implementation across basic, lower-secondary, and vocational education using a measurement invariance approach. By integrating theoretical insights, empirical validation, and cross-group analyses, the study contributes to advancing green skills assessment methodologies and offers practical implications for curriculum design, competency mapping, and sustainability-oriented educational policies. Green skills have evolved from a narrow focus on environmental knowledge toward a multidimensional construct encompassing cognitive, behavioral, and technical competencies required for sustainable development (Vona et al., 2015; Auktor, 2020). Recent literature conceptualizes green skills along a continuum from foundational environmental awareness to advanced technical competencies embedded in sector-specific practices (Wegenberger & Ponocny, 2025). This progression reflects the increasing complexity of sustainability challenges across economic sectors and the rising expectation that workers possess capabilities to adapt to green technologies, support eco-efficient production processes, and engage in environmentally responsible behaviours (Ristanović et al., 2025; Cao et al., 2025). Albertz and Pilz (2025) further expand this conceptualization through a comprehensive literature review of green vocational education, identifying shared themes across institutional, geographic, and pedagogical contexts. Their analysis shows that green skills development is shaped by diverse actors—including policymakers, schools, industries, and communities—and that curricular alignment plays a vital role in ensuring relevance to labour market demands. These frameworks highlight the importance of embedding green skills across educational systems rather than limiting them to vocational education and training (VET). A growing body of research emphasizes the developmental nature of green skills, where competencies emerge gradually from early schooling and become increasingly specialized in vocational settings. In basic education, students develop fundamental environmental attitudes, ecological awareness, and sustainable habits that form the foundation for more advanced competencies (Ismail et al., 2024; Sukri et al., 2022). These early-stage competencies reflect what Wegenberger and Ponocny (2025) describe as foundational competences, emphasizing environmental literacy and personal responsibility. In lower secondary education, students’ exposure to scientific inquiry, sustainability projects, and critical thinking activities supports the development of transitional competences. These competencies involve analyzing environmental issues, evaluating human–environment interactions, and participating in real-world sustainability initiatives. The shift from awareness to action is crucial in bridging basic education with vocational pathways. Vocational education, by contrast, emphasizes technical and applied green skills needed for emerging green industries. Research demonstrates significant variation in vocational curricula across countries, highlighting differences in how green skills are integrated into logistics, agriculture, manufacturing, and other technical fields (Chen, 2025; Hu et al., 2025). Mutohhari et al. (2025), studying Indonesian vocational schools, found uneven levels of green skills maturity among both teachers and students, underscoring challenges in curriculum implementation and professional development. Additional studies show that students’ and teachers’ behavioural engagement in sustainability practices is influenced by institutional support, availability of learning resources, and workplace expectations (Haloho et al., 2023). Taken together, these findings suggest that green skills constitute a developmental continuum spanning basic, lower secondary, and vocational education. Understanding this continuum requires assessment frameworks capable of capturing differences across educational stages while maintaining conceptual consistency. The assessment of green skills has gained substantial attention as institutions attempt to quantify sustainability competencies for curriculum evaluation and policy planning. Early work in this field focused on environmental behaviour instruments or green character scales, often validated using classical test theory or Rasch modelling (Sukri et al., 2022). These tools emphasized behavioural indicators and attitudinal components, suitable primarily for younger students. More recent research expands assessment models into vocational and professional contexts. Kamis et al. (2018) developed a psychometrically tested instrument for green skills among Malaysian vocational students, demonstrating acceptable reliability and factorial validity. Meanwhile, Jovanovski et al. (2024) introduced the Green Compass tool for institutional self-assessment, which supports schools in identifying strengths and weaknesses in sustainability implementation. Although useful for organizational evaluation, Green Compass does not measure individual learner competencies and is not applicable for cross-level comparisons. Sector-specific instruments continue to emerge. Hu et al. (2025) validated green skills for agricultural professionals in China, using SEM-based approaches to confirm construct validity. Similarly, Ibrahim et al. (2020) explored green knowledge and attitudes from an industrial perspective, highlighting the need for alignment between educational preparation and workplace expectations. Research in other applied fields, including green tourism (Ristanović et al., 2025) and informal economic sectors (Manyati et al., 2024), further underscores the importance of robust green skills assessment. Despite these advances, existing models remain fragmented. Many instruments focus on either early education or vocational contexts, with little attention to cross-level alignment. Furthermore, studies vary widely in their methodological rigor, limiting their comparability. Instrument validation is critical to ensuring that assessment tools accurately measure intended constructs. Techniques such as exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modelling (SEM) have been widely used in educational measurement to assess dimensionality, reliability, and construct validity (Unfried et al., 2015; Lombardi et al., 2022). Within green skills research, these methods have been applied to validate teacher and student competency instruments (Kamis et al., 2018; Mutohhari et al., 2025; Hu et al., 2025). Rasch modelling is also employed to evaluate item difficulty, discrimination, and response patterns (Sukri et al., 2022; Ismail et al., 2024). However, few studies extend validation to cross-group comparisons. Without robust invariance testing, differences in green skill scores may reflect measurement artifacts rather than true variations across educational levels, genders, or institutional contexts. As sustainability transitions require coherent educational strategies, measurement tools must be validated for comparisons across diverse learner populations. Measurement invariance ensures that an instrument measures the same construct consistently across different groups. This is essential when comparing competencies across educational levels such as primary, lower secondary, and vocational students. Invariance testing typically proceeds through hierarchical stages—configural, metric, scalar, and strict invariance—each establishing increasing levels of equivalence (Sepasgozar, 2023). Recent methodological studies highlight the importance of invariance testing in both composite models and latent variable frameworks. For instance, Sepasgozar (2023) applied measurement invariance in composite modelling to examine digital technology assimilation, demonstrating how group-level comparisons depend on establishing structural equivalence. Similarly, Lombardi et al. (2022) emphasize invariance as a prerequisite for meaningful interpretation in educational fidelity assessments, while Unfried et al. (2015) apply invariance approaches to STEM attitude measurement. Although the importance of measurement invariance is recognized in broader educational research, its application within green skills assessment remains limited. No known studies have examined invariance across the full schooling continuum from basic to vocational education. This gap poses challenges for policymakers and researchers seeking to track green skill development over time or compare implementation efforts across school types. Several gaps emerge from the existing literature. First, research on green skills remains concentrated in sector-specific or vocational contexts, with limited attention to early education where foundational skills develop (Sukri et al., 2022; Ismail et al., 2024). Second, most instruments assess isolated aspects of green skills, lacking an integrative developmental perspective that spans multiple educational stages. Third, cross-group validation remains underexplored, as few studies utilize measurement invariance to ensure comparable constructs across educational levels (Sepasgozar, 2023). Fourth, current frameworks do not provide a comprehensive alignment between foundational, transitional, and technical green skills as conceptualized in recent competency models (Wegenberger & Ponocny, 2025). Given these limitations, there is a clear need for a unified validation framework capable of assessing green skills implementation across basic, lower-secondary, and vocational education. Such a framework must be psychometrically robust, developmentally sensitive, and theoretically grounded, enabling researchers and policymakers to examine the continuity of green skill development within national education systems. This study aims to address these gaps by constructing and validating a cross-level instrument using measurement invariance to ensure structural equivalence across educational groups. This study adopted a multi-phase quantitative design to develop and validate a unified instrument for assessing green skills implementation across basic, lower-secondary, and vocational education. The validation process followed contemporary psychometric standards emphasizing internal structure, construct validity, and cross-group comparability (Rosario-Hernández et al., 2021; Shultz et al., 2020). In accordance with recommended practices in scale development, the research included item generation, expert review, pilot testing, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and measurement invariance testing using multiple-group structural equation modelling (Thompson, 2016; de Vries et al., 2025). Figure 1 illustrates the Unified Green Skills Validation Framework developed in this study to assess green skills implementation across basic, lower-secondary, and vocational education. The framework integrates both measurement and structural components to capture the multidimensional and developmental nature of green skills. At the core of the model are six validated green skills constructs: Environmental Awareness (EA), Sustainable Behavior (SB), Critical Sustainability Thinking (CST), Sustainability Participation (SP), Resource Management Skills (RMS), and Technical Green Practices (TGP). These constructs represent a continuum from foundational cognitive and behavioral competencies to applied and technical sustainability skills. The directional flow depicted in the model reflects the developmental progression of green skills, whereby early awareness and behavior (EA, SB) support higher-order reasoning and participation (CST, SP), which in turn facilitate practical resource management and technical green practices (RMS, TGP). The framework explicitly incorporates cross-level equivalence through multi-group measurement invariance testing, including configural, metric, and partial scalar invariance. This ensures that the constructs are interpreted consistently across educational levels, enabling valid comparisons of latent means. The lower section of the figure visualizes the application of the framework across three educational groups—primary, lower-secondary, and vocational—highlighting differences in dominant green skill profiles at each level. While foundational competencies are emphasized in primary education, vocational education demonstrates stronger technical and applied green skills. A total of N = 215 participants were recruited across three educational levels: basic (primary school), lower-secondary (junior high school), and vocational education. Stratified cluster sampling was used to ensure proportional representation across groups despite differing school sizes. Sample size considerations followed recommendations for CFA and invariance testing, where minimum samples above 200 are considered adequate for model stability and parameter accuracy (van Dijk et al., 2022; Martin, 2013; Owusu et al., 2025). Participation was voluntary and informed consent procedures were applied. 3.3.1 Item generation An initial pool of items was generated from literature on green skills, sustainability competencies, vocational education frameworks, and prior validated scales. The goal was to capture the developmental continuum of green skills from foundational to technical competencies. Construction followed accepted principles of clarity, age appropriateness, and construct representation (Shultz et al., 2020; Steffgen et al., 2020). After reviewing theoretical and empirical sources (Kamis et al., 2018; Sukri et al., 2022; Hu et al., 2025), an initial set of 48 items was drafted, distributed across six conceptual constructs: a. Environmental Awareness b. Sustainable Behaviour c. Critical Sustainability Thinking d. Participation in Sustainability Actions e. Resource Management Skills f. Technical and Applied Green Practices 3.3.2 Content validity Content validity was established through a panel of 10 experts in green skills, TVET, environmental education, and psychometric assessment. Experts rated each item for relevance, clarity, and representativeness. Items not meeting recommended criteria for content validity were revised or removed following established practices in similar validation studies (Koob et al., 2025; González-Navarro et al., 2023). After this stage, the final pool consisted of 36 items across 6 constructs. A pilot test was conducted with n = 60 students to evaluate item functioning and preliminary factor structure. EFA using principal axis factoring with oblique rotation was conducted to examine dimensionality, following psychometric recommendations for early-stage scale refinement (Merino-Soto et al., 2022; Avinç & Doğan, 2024). Items with low communalities (<0.40), cross-loadings, or lack of theoretical coherence were discarded. The EFA supported a six-factor solution consistent with the conceptualization of green skills. The full sample (N = 215) was used to perform CFA with robust maximum likelihood (MLR) estimation to validate the internal structure of the instrument. Fit indices (CFI, TLI, RMSEA, SRMR) were interpreted according to widely accepted guidelines in psychological measurement (Rosario-Hernández et al., 2021; de Vries et al., 2025). Model adjustments were carefully considered using modification indices guided by theoretical and conceptual consistency. The final CFA model demonstrated satisfactory fit across all indices, confirming the structure of six interrelated green skills constructs. To determine whether the green skills constructs were comparable across educational levels (primary, lower-secondary, vocational), multiple-group CFA was conducted. Following Thompson (2016), invariance was tested hierarchically: (a) Configural Invariance: Assessed whether the baseline six-factor structure was consistent across groups. (b) Metric Invariance: Factor loadings were constrained equal across groups to evaluate equivalence in item–construct relationships (van Dijk et al., 2022). (c) Scalar Invariance: Item intercepts were constrained equal to test whether differences in observed scores reflected true latent differences (Martin, 2013; Rosario-Hernández et al., 2021). Model comparisons used ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 as criteria for acceptable invariance, consistent with recommendations for multi-group assessment in education research (van Dijk et al., 2022). Partial invariance was pursued in cases where full invariance could not be achieved. Reliability was assessed using Cronbach’s alpha and composite reliability (CR), both widely used in recent validation studies (Koob et al., 2025; González-Navarro et al., 2023). Convergent validity was examined through average variance extracted (AVE ≥ 0.50), while discriminant validity was assessed using Fornell–Larcker and HTMT criteria (Shultz et al., 2020). All six constructs demonstrated adequate reliability and validity indicators. Rasch-based item diagnostics were performed to identify potential issues in response patterns and item functioning, following procedures applied in digital literacy and self-regulation assessments (Avinç & Doğan, 2024; Merino-Soto et al., 2022). Exploratory demographic comparisons were also conducted, including gender-based differences, consistent with contemporary research on sustainability communication and competency variation (Ali et al., 2025). All procedures involving human participants were conducted in accordance with established ethical standards. Ethical approval was secured from the relevant institutional review boards, and research authorization was formally granted by Universitas Negeri Yogyakarta under Letter No. B/3088/UN34.13/TU.12/2025. Prior to data collection, informed consent for participation in the research was obtained from all participants. As the study involved students across primary, lower-secondary, and vocational education levels, the majority of participants were minors. For these participants, written informed consent was obtained from parents or legal guardians, and assent was obtained from the students themselves after a clear explanation of the study objectives, procedures, voluntary nature of participation, and confidentiality assurances. Participants were informed that their participation was entirely voluntary, that they could withdraw at any time without any consequences, and that all data would be anonymized and used solely for research purposes. All consent and assent procedures were administered in written form to ensure ethical compliance, transparency, and proper documentation (andryananda et al., 2025; Fan et al., 2025). Prior to analysis, the dataset (N = 215) was inspected for missing values, outliers, and normality assumptions. Missing data (<2%) were imputed using the expectation–maximization method. Skewness and kurtosis values fell within acceptable limits, supporting the use of robust maximum likelihood estimation in subsequent CFA, consistent with methodological recommendations in psychometric research (Baştuğ, 2025; Nurumov et al., 2022). Table 1 presents the descriptive statistics and correlations among the six green skills constructs. As shown in Table 1, correlations ranged from .28 to .58, indicating moderate conceptual relatedness without multicollinearity, which supports discriminant validity and prepares the model for CFA. Moderate correlations indicate each construct is empirically distinguishable while remaining theoretically connected—an expected pattern in multidimensional competency frameworks (Maertens et al., 2024). Exploratory factor analysis (EFA) on pilot data (n = 60) revealed a stable six-factor solution. As shown in Table 2, all factors showed strong loadings (>.55) and collectively explained 72.4% of variance. These results align with prior studies validating competency-based instruments using multidimensional structures (Galiana et al., 2020). Factor loadings, eigenvalues, and percentage of variance explained for each green skills dimension during preliminary exploration. The stable factor structure supports the theoretical assumption that green skills develop across foundational, behavioural, and technical dimensions (Maulana et al., 2023; Hjort et al., 2021). Confirmatory factor analysis on the full dataset validated the six-factor model. As shown in Table 3, all fit indices exceed recommended benchmarks, indicating excellent model fit. | Fit index | Acceptable threshold | Obtained value | Interpretation | |---|---|---|---| | CFI | ≥.90 | .953 | Excellent | | TLI | ≥.90 | .944 | Excellent | | RMSEA | ≤.08 | .052 (90% CI .047–.069) | Good | | SRMR | ≤.08 | .041 | Excellent | | χ2/df | ≤3.00 | 2.11 | Acceptable | Goodness-of-fit indices demonstrating the adequacy of the six-factor green skills measurement model. The CFA results validate the six-factor structure, in line with contemporary psychometric instrument validations (Omar et al., 2025; De Beer et al., 2024). Table 4 presents internal consistency and convergent validity indicators. All constructs demonstrated strong Cronbach’s alpha, composite reliability, and AVE values exceeding recommended thresholds. High reliability and AVE values confirm strong construct integrity, aligning with validation patterns observed in social-emotional and cognitive competency assessments (Furlong et al., 2020). Measurement invariance across the three groups—primary, lower-secondary, vocational—was tested using hierarchical multi-group CFA. Table 5 presented Summary of configural, metric, scalar, and partial scalar invariance models with their respective fit indices. Partial scalar invariance allows meaningful latent mean comparisons, consistent with findings in developmental and cross-cultural measurement studies (van Dijk et al., 2022; Omar et al., 2025). Table 6 Presented that the model was tested across three groups (Primary, Lower-Secondary, and Vocational). The table reports standardized coefficients (β), robust standard errors (SE), z-values, and p-values for each group, along with an indicator of whether each coefficient is invariant (i.e., not significantly different across groups) based on multi-group SEM comparisons (Wald test/chi-square difference; α = .05). Invariance decisions are determined using p > .05, indicating that the parameter does not significantly differ across groups. Table 7 summarizes latent mean differences across educational levels, using primary-level learners as the reference group (β = 0). The results empirically support the theoretical progression from foundational to technical green skills across educational pathways. | Construct | Lower-Secondary (β) | Vocational (β) | Interpretation | |---|---|---|---| | EA | +0.21* | +0.08 | Slight developmental gain | | SB | +0.17 | +0.14 | Comparable across groups | | CST | +0.42** | +0.55** | Sharp conceptual growth | | SP | +0.36** | +0.48** | Increased action participation | | RMS | +0.28* | +0.63** | Strong skills maturation | | TGP | +0.11 | +1.24** | Vocational specialization | Table 8 show the primary group is used as a reference (latent mean = 0). The table shows the estimated latent means for Lower-Secondary and Vocational, SE, z, p, and Cohen’s d (effect size) for comparison with Primary. | Construct | Lower-Secondary Mean (SE) | z | p | Vocational Mean (SE) | z | p | Cohen’s d (Lower-Sec) | Cohen’s d (Vocational) | |---|---|---|---|---|---|---|---|---| | EA | +0.21 (0.09) | 2.33 | .020* | +0.08 (0.08) | 1.00 | .317 | 0.26 | 0.10 | | SB | +0.17 (0.10) | 1.70 | .089 | +0.14 (0.09) | 1.56 | .119 | 0.21 | 0.18 | | CST | +0.42 (0.11) | 3.82 | <.001** | +0.55 (0.10) | 5.50 | <.001** | 0.51 | 0.67 | | SP | +0.36 (0.10) | 3.60 | <.001** | +0.48 (0.09) | 5.33 | <.001** | 0.44 | 0.59 | | RMS | +0.28 (0.11) | 2.55 | .011* | +0.63 (0.10) | 6.30 | <.001** | 0.33 | 0.75 | | TGP | +0.11 (0.12) | 0.92 | .357 | +1.24 (0.11) | 11.27 | <.001** | 0.12 | 1.48 | Rasch diagnostics indicated stable item functioning across participants, with INFIT and OUTFIT values within the acceptable range. This aligns with patterns observed in digital literacy and self-regulation instruments validated using Rasch modelling (Avinç & Doğan, 2024; Merino-Soto et al., 2022). No significant gender-based DIF was detected, consistent with recent findings on green communication patterns across demographics (Ali et al., 2025). The purpose of this study was to develop and validate a unified measurement framework capable of assessing green skills implementation across basic, lower-secondary, and vocational education. Through rigorous psychometric procedures—including EFA, CFA, reliability testing, and multi-group measurement invariance analysis—the findings not only confirm the robustness of the six-construct green skills model but also demonstrate its applicability across diverse educational stages. This section discusses the implications of these findings in relation to theoretical development, psychometric rigor, educational practice, and methodological advancement. The validated six-factor structure provides evidence that green skills encompass multidimensional competencies including environmental awareness, sustainable behaviour, critical sustainability thinking, sustainability participation, resource management skills, and technical green practices. Consistent with prior literature emphasizing the complexity of sustainability competencies (Saari et al., 2024), the results confirm that green skills are not monolithic but instead comprise interrelated yet distinct domains. Measurement invariance results further revealed full configural and metric invariance and partial scalar invariance across primary, lower-secondary, and vocational groups. This indicates that although the factor structure and item loadings operate similarly across levels, some intercept variations reflect expected developmental differences. Such findings mirror patterns in cross-age and cross-context psychometric studies, where partial invariance is common due to developmental diversity (Omar et al., 2025). The results contribute to the theoretical understanding of green skills as a developmental continuum spanning foundational, transitional, and technical competencies. Primary-level learners demonstrated stronger environmental awareness, whereas vocational students showed the highest proficiency in technical green practices. This progression aligns with existing frameworks of environmentally responsible behaviour and sustainability learning trajectories, which propose that awareness and attitudes form early while technical and applied skills emerge later through contextualized learning experiences (Tao, 2025). Furthermore, the increasing strength of constructs such as critical sustainability thinking and participation among older learners supports the notion that cognitive maturity, exposure to real-world problems, and experiential learning environments are central to sustainability-related competence development. This is consistent with broader educational research showing that contextual exposure and guided inquiry foster higher-order competencies (Martinez & Ellis, 2023; Sutrisno et al., 2025). By integrating the principles of modern test theory (McDonald, 2013) with contemporary SEM-based validation procedures, this study reinforces the necessity of robust measurement design for sustainability education instruments. The strong reliability indices (Cronbach’s α and CR) and adequate convergent/discriminant validity demonstrate that the instrument adheres to psychometric standards required for developmental and comparative educational research. Importantly, the application of measurement invariance analysis establishes that observed differences across educational levels reflect true latent differences rather than measurement bias. This significantly enhances the interpretability of green skills comparisons and supports valid inferences, consistent with best practices in cross-group assessment (Rashideh et al., 2025; Anthony Jr et al., 2023). One of the most significant contributions of this study is demonstrating that green skills can be consistently measured across different school levels using a unified framework, something not previously addressed in sustainability education research. The findings highlight several implications: (a) Early Schooling: Building Foundational Green Competencies Higher scores in environmental awareness among primary students indicate that early education plays a crucial role in shaping sustainability consciousness. This reinforces calls for environmental literacy to be embedded in early curricula, as foundational attitudes significantly influence later behaviour (Saari et al., 2024). (b) Lower Secondary Level: Growth of Critical Sustainability Thinking The substantial increase in critical sustainability thinking among lower-secondary students aligns with cognitive development stages, where adolescents begin to reason abstractly and evaluate systemic environmental issues. (c) Vocational Level: Maturation of Technical Green Skills Vocational learners’ significantly higher scores in technical green practices confirm that TVET institutions serve as key platforms for cultivating applied green competencies aligned with workforce needs. This supports findings that sectoral education must integrate sustainability-specific technical practice to promote green leadership and behavioural translation in workplaces (Tao, 2025). Methodologically, this study offers a comprehensive approach to validating sustainability-related instruments by combining classical factor analytic techniques with invariance testing. Such approaches align with emerging trends in psychometric scale development where researchers employ multi-method validation to strengthen empirical claims (Rashideh et al., 2025; Martinez & Ellis, 2023). The use of MLR estimation, Rasch diagnostics, and multi-group CFA strengthens the robustness of findings and demonstrates methodological sophistication comparable to recent studies in technology adoption (Anthony Jr et al., 2023) and artificial intelligence–based learning readiness (Sutrisno et al., 2025). Furthermore, the high survey response quality aligns with evidence that well-structured sustainability and competency assessments achieve higher engagement and valid responses when designed with clear constructs and developmental relevance (Holtom et al., 2022). The validated unified framework holds practical significance for educators, school administrators, and policymakers. (a) Curriculum Alignment and Progression Mapping The demonstrated developmental differences provide empirical basis for constructing aligned sustainability curricula from primary to vocational education. This supports systematic skill progression rather than fragmented or inconsistent programs. (b) Teacher Professional Development Since green skills manifest differently across school levels, teacher training efforts must be differentiated to enhance competency in facilitating both foundational and technical green learning experiences. (c) Institutional Sustainability Evaluation The unified framework enables institutions to benchmark green skills implementation across levels, aiding accreditation, school improvement planning, and national sustainability targets. (d) Informing National Workforce and Environmental Strategies By identifying green skill gaps across schooling levels, policymakers can better tailor TVET expansions, green industry partnerships, and workforce development programs aligned with environmental goals. (e) Supporting Digital and IoT-based Green Transitions Findings resonate with emerging frameworks advocating for green digitalization and GIoT adoption in educational settings (Jalil et al., 2025), suggesting that technological initiatives should complement competency-based sustainability education. Despite its contributions, the study has limitations. First, the sample size, although adequate for CFA and invariance analysis, could be expanded to enhance generalizability across regions. Second, the cross-sectional nature of the data restricts examination of longitudinal growth patterns, which future studies should address to capture developmental trajectories over time. Third, contextual school variables such as teacher expertise or institutional green policies were not incorporated into the model, limiting the explanatory scope regarding sources of variance in green skills. Future research may expand the framework to include: This study developed and validated a unified measurement framework for assessing green skills implementation across basic, lower-secondary, and vocational education. Through a rigorous multi-phase validation process involving exploratory and confirmatory factor analyses, reliability assessments, and multi-group measurement invariance testing, the instrument demonstrated strong psychometric robustness. The six-factor model—comprising environmental awareness, sustainable behaviour, critical sustainability thinking, sustainability participation, resource management skills, and technical green practices—was shown to be structurally stable across educational levels. Partial scalar invariance further enabled meaningful latent mean comparisons, revealing a clear developmental progression from foundational to technical green competencies. The findings underscore that green skills are not isolated attributes of vocational education but develop cumulatively throughout the schooling continuum. Primary and lower-secondary education lay cognitive and behavioural foundations, while vocational pathways refine these into specialized technical competencies. This developmental alignment provides a much-needed empirical basis for integrating sustainability competencies coherently across curricula and instructional practices. Several implications emerge for policymakers, curriculum designers, and educators: a. Strengthening Early Education Sustainability Foundations. The results highlight the importance of embedding environmental awareness and sustainable behaviour in primary curricula, as these competencies form the basis for more advanced green skills. b. Curriculum Alignment Across Levels. A unified progression model enables policymakers to design vertically aligned sustainability curricula, preventing fragmentation and ensuring coherent skill development from basic to vocational education. c. Teacher Professional Development. Differentiated training is needed to equip teachers at each level with appropriate pedagogical strategies—from foundational environmental literacy to applied technical sustainability practices. d. Monitoring and Evaluation Systems. The validated instrument can serve as a national or regional monitoring tool to assess green skills readiness, track progress, and inform targeted interventions. e. Support for Green Workforce Transitions. Aligning schooling outcomes with green economy demands strengthens national preparedness for environmental, technological, and economic shifts. Overall, the unified framework offers a robust empirical foundation for advancing sustainability education and shaping policies aimed at cultivating environmentally responsible future generations. The data generated and analyzed during this study are publicly available in the Zenodo repository at https://doi.org/10.5281/zenodo.18062420 and https://doi.org/10.5281/zenodo.18150660 (Tanggu Mara, 2025), under the CC0 1.0 Public Domain Dedication. All data supporting the findings of this study are provided in this repository. The authors confirm that the data have not been published elsewhere. The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Doctoral Scholarship Program for Indonesian Lecturers (PDDI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of the Republic of Indonesia, and the Indonesian Endowment Fund for Education (LPDP). - Albertz A, Pilz M: Green Alignment, Green Vocational Education and Training, Green Skills and Related Subjects: A Literature Review on Actors, Contents and Regional Contexts. Int. J. Train. Dev. 2025; 29(2): 243–254. - Ali A, Benuyenah V, Rajput SKO, et al.: Sustainable Signals: A Global Exploration of Environmental Green Communication Dynamics “A Cross-Continental, Cross-Generational, Cross-Gender and Cross-Qualification Study”. Corp. Soc. Responsib. Environ. Manag. 2025. - andryananda. 2023, A. A. P. T. MHamid HWR, Angeline MI, et al.: Bringing Numbers to Life and Reducing Anxiety: An Augmented Reality and Haptic Feedback-Based Mathematics Game for Primary School Students. F1000Res. 2025; 14: 1131. - Anthony B Jr, Kamaludin A, Romli A: Predicting academic staffs behaviour intention and actual use of blended learning in higher education: Model development and validation. Technol. Knowl. Learn. 2023; 28(3): 1223–1269. - Auktor GV: Green industrial skills for a sustainable future. Vienna: United Nations Industrial Development Organization; 2020. - Avinç E, Doğan F: Digital literacy scale: Validity and reliability study with the rasch model. Educ. Inf. Technol. 2024; 29(17): 22895–22941. - Baştuğ M: Psychometric Properties of the Pre-Literacy Test: Assessing Literacy Readiness Skills. J. Intelligence. 2025; 13(12): 155. - Cao W, Mai NT, Liu W: Adaptive knowledge assessment via symmetric hierarchical Bayesian neural networks with graph symmetry-aware concept dependencies. Symmetry. 2025; 17(8): 1332. - Chen P: Green skills in logistics vocational education: a comparative study of curriculum integration in China and Germany. Int. J. Train. Dev. 2025; 29(2): 124–139. Publisher Full Text - De Beer LT, Van Der Vaart L, Escaffi-Schwarz M, et al.: Maslach Burnout Inventory—General survey: A systematic review and meta-analysis of measurement properties. Eur. J. Psychol. Assess. 2024. - van Dijk W , Schatschneider C, Al Otaiba S, et al.: Assessing measurement invariance across multiple groups: When is fit good enough? Educ. Psychol. Meas. 2022; 82(3): 482–505. PubMed Abstract | Publisher Full Text - Fan J, Wu R, Tang C, et al.: Empowering vocational training for middle-aged, elderly, and low-educated individuals: A design approach for entertainment-based learning. International Journal of Human–Computer Interaction. 2025; 1–25. - Furlong MJ, Dowdy E, Nylund-Gibson K, et al.: Enhancement and standardization of a universal social-emotional health measure for students’ psychological strengths. Journal of well-being assessment. 2020; 4(3): 245–267. - Galiana L, Oliver A, Arena F, et al.: Development and validation of the Short Professional Quality of Life Scale based on versions IV and V of the Professional Quality of Life Scale. Health Qual. Life Outcomes. 2020; 18(1): 364. PubMed Abstract | Publisher Full Text - González-Navarro P, Córdoba-Iñesta AI, Casino-García AM, et al.: Evaluating employability in contexts of change: validation of a scale. Front. Psychol. 2023; 14: 1150008. - Haloho AA, Pardjono S, In S, et al.: Implementation of Green Skills in Vocational Education: Perceptions about Students’ and Teachers’ Behavioral Activities. Jurnal Pendidikan Dan Pengajaran. 2023; 56(1): 65–79. - Hjort AV, Christiansen TB, Stage M, et al.: Programme theory and realist evaluation of the ‘Smoke-Free Vocational Schools’ research and intervention project: a study protocol. BMJ Open. 2021; 11(2): e042728. PubMed Abstract | Publisher Full Text - Holtom B, Baruch Y, Aguinis H, et al.: Survey response rates: Trends and a validity assessment framework. Hum. Relat. 2022; 75(8): 1560–1584. - Hu B, Na-Nan K, Kittichotsatsawat Y: Toward sustainable farming: Assessing and validating green skills for agricultural professionals in China. Environmental Challenges. 2025; 18: 101067. - Ibrahim Z, Lai CS, Zaime AF, et al.: Green skills in knowledge and attitude dimensions from the industrial perspective. IOP conference series: Materials science and engineering. IOP Publishing; 2020, September; Vol. 917(1): p. 012025. - Ismail I, Riandi R, Kaniawati I, et al.: Gender roles in understanding and implementing green energy technology in indonesian schools: rasch analysis. Qubahan Academic Journal. 2024; 4(3): 298–314. - Jalil MF, Marikan DABA, Jais MB, et al.: A Green Internet of Things (GIoT) Adoption Framework to Enhance Environmental Performance of SMEs Through the Digital Social Networking. Business Strategy & Development. 2025; 8(3): e70179. - Jovanovski B, Shamsuzzoha A, Polenakovikj R, et al.: GreenCompass-A self-assessment tool for fostering integration of green skills.2024. - Kamis A, Hussain MAM, Kob CGC, et al.: Validity and reliability of green skills instrument. Sains Humanika. 2018; 10(3-3). - Koob C, Reuschenbach B, Godsey JA: Translation and validation of the German version of the Nursing Brand Image Scale (NBIS-PG). BMC Nurs. 2025; 24(1): 1–18. - Lombardi AR, Rifenbark GG, Poppen M, et al.: Development and validation of the secondary transition fidelity assessment. Assess. Eff. Interv. 2022; 47(3): 147–156. - Maertens R, Götz FM, Golino HF, et al.: The Misinformation Susceptibility Test (MIST): A psychometrically validated measure of news veracity discernment. Behav. Res. Methods. 2024; 56(3): 1863–1899. PubMed Abstract | Publisher Full Text - Manyati TK, Kalima BG, Owolabi T, et al.: Exploring the potential for enhancing green skills training, innovation and sustainable livelihoods in informal spaces of Harare, Zimbabwe: identifying gaps and opportunities. IIMBG Journal of Sustainable Business and Innovation. 2024; 2(1): 60–79. - Martin JD: Examining the measurement invariance of the Transition Assessment and Goal Generator across percent of time spent in general education.2013. - Martinez RR Jr, Ellis JM: A national study exploring factors promoting adolescent college readiness in math and science (STEM-CR). Educ. Res. 2023; 52(9): 553–569. - Maulana A, Luthfiyah F, Arthur R: Validation of Construct Vocational Literacy: An Analysis of the Suitability of the Needs of SMK Students in Indonesia. 20th AsiaTEFL-68th TEFLIN-5th iNELTAL Conference (ASIATEFL 2022). Atlantis Press; 2023, May; pp. 54–69. - McDonald RP: Test theory: A unified treatment. psychology press; 2013. - Merino-Soto C, Chavez-Ventura G, Lopez-Fernandez V, et al.: Learning Self-Regulation Questionnaire (SRQ-L): Psychometric and measurement invariance evidence in Peruvian undergraduate students. Sustainability. 2022; 14(18): 11239. - Mutohhari F, Sudira P, Isnantyo FD, et al.: Generic green skills: Maturity level of vocational education teachers and students in Indonesia. International Journal of Evaluation and Research in Education. 2025; 14(1): 179–187. - Nurumov K, Hernández-Torrano D, Ait Si Mhamed A, et al.: Measuring social desirability in collectivist countries: a psychometric study in a representative sample from Kazakhstan. Front. Psychol. 2022; 13: 822931. - Omar J, Sulik MJ, Obradović J: The Emotion Matching Task (EMT): Anti-Bias Adaptations, Measurement Invariance, and Longitudinal Growth in Preschoolers. Early Educ. Dev. 2025; 1–28. - Owusu EA, Zhou L, Sarpong FA, et al.: Exploring the Impact of Green Performance on Employees’ Pro-Environmental Behaviors: A Multi-Group Analysis From Developed and Emerging Economies Healthcare Industries. Bus. Strateg. Environ. 2025; 34(4): 4717–4746. - Rashideh W, Papastathopoulos A, Treiblmaier H: Developing a Scale for Blockchain Adoption Determinants: A Set-Exploratory Structural Equation Modeling Approach. ACM SIGMIS Database: the DATABASE for Advances in Information Systems. 2025; 56(3): 143–179. - Ristanović V, Andrlić B, Ekiz E: A Multi-Criteria Assessment of Green Tourism Potential in Rural Regions: The Role of Green Skills and Institutional Readiness. Economies. 2025; 13(11): 332. - Rosario-Hernández E, Rovira-Millán LV, Merino-Soto C: Review of the internal structure, psychometric properties, and measurement invariance of the work-related rumination scale–Spanish version. Front. Psychol. 2021; 12: 774472. - Saari UA, Ojasoo M, Venesaar U, et al.: Assessing engineering students’ attitudes towards corporate social responsibility principles. Eur. J. Eng. Educ. 2024; 49(3): 492–513. - Sepasgozar SM: Construction digital technology assimilation and absorption capability using measurement invariance of composite modeling. J. Constr. Eng. Manag. 2023; 149(7): 04023041. - Shultz KS, Whitney D, Zickar MJ: Measurement theory in action: Case studies and exercises. Routledge; 2020. - Steffgen G, Sischka PE, Fernandez de Henestrosa M: The quality of work index and the quality of employment index: a multidimensional approach of job quality and its links to well-being at work. Int. J. Environ. Res. Public Health. 2020; 17(21): 7771. - Sukri A, Rizka MA, Purwanti E, et al.: Validating Students’ Green Character Instrument Using Factor and Rasch Model. Eurasian J. Educ. Res. 2022; 11(2): 859–872. - Sutrisno S, Azis A, Setyawan MB, et al.: Assessing students’ readiness for artificial intelligence-based project learning to strengthen local wisdom values in Indonesia. Cogent Education. 2025; 12(1): 2582948. - Tanggu Mara A: A Unified Validation Framework for Assessing Green Skills Implementation from Basic to Vocational Education: A Measurement Invariance Approach. Zenodo. 2025. Publisher Full Text - Tanggu Mara AAP, Jaya DJ, Alfiyandri A, et al.: Systematic Review of TVET and Indigenous Cultural Integration in Indonesia: Pathways Toward Contextualized Skills Education. F1000Res. 2025; 14: 1159. - Tao Z: Green leadership, green action: How environmentally specific transformational leadership drives employee green behavior. Bus. Soc. Rev. 2025; 130(3): 368–413. - Thompson MS: Assessing measurement invariance of scales using multiple-group structural equation modeling. Principles and methods of test construction: Standards and recent advances. 2016; 218–244. - Unfried A, Faber M, Stanhope DS, et al.: The development and validation of a measure of student attitudes toward science, technology, engineering, and math (S-STEM). J. Psychoeduc. Assess. 2015; 33(7): 622–639. - Vona F, Marin G, Consoli D, et al.: Green skills (No. w21116). National Bureau of Economic Research; 2015. - de Vries DA , Piotrowski JT, de Vreese C : Developing the DigIQ: A measure of digital competence. PLoS One. 2025; 20(5): e0322995. PubMed Abstract | Publisher Full Text - Wegenberger O, Ponocny I: Green Skills Are Not Enough: Three Levels of Competences from an Applied Perspective. Sustainability. 2025; 17(1): 327. Author details Author details 1 Educational Research and Evaluation, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 2 Primary School Teacher Education, STKIP Pelita Nusantara Buton, Buton, Bau Bau, Indonesia 3 Department of Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 4 Primary Madrasah Teacher Education, Universitas Nahdlatul Ulama Sidoarjo, Sidoarjo, Jawa Timur, Indonesia 5 Primary School Teacher Education, Universitas PGRI Ronggolawe Tuban, Tuban, East Java, Indonesia 6 Mathematics Education, Universitas Sriwijaya Fakultas Matematika dan Ilmu Pengetahuan Alam, Palembang, South Sumatra, Indonesia 7 Department of Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 8 Department of Vocational Education in Electronics Engineering, Universitas Muhammadiyah Riau, Pekanbaru, Riau, Indonesia 9 Primary School Teacher Education, Universitas Pendidikan Indonesia Fakultas Ilmu Pendidikan, Bandung, West Java, Indonesia 10 Teacher Professional Education, Universitas Musamus Fakultas Keguruan dan Ilmu Pendidikan, Merauke, Papua, Indonesia 11 Primary School Teacher Education, STKIP Taman Siswa Bima, Bima, West Nusa Tenggara, Indonesia 12 Information Technology Education, Universitas Stella Maris Sumba, Tambolaka, Indonesia 2 Primary School Teacher Education, STKIP Pelita Nusantara Buton, Buton, Bau Bau, Indonesia 3 Department of Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 4 Primary Madrasah Teacher Education, Universitas Nahdlatul Ulama Sidoarjo, Sidoarjo, Jawa Timur, Indonesia 5 Primary School Teacher Education, Universitas PGRI Ronggolawe Tuban, Tuban, East Java, Indonesia 6 Mathematics Education, Universitas Sriwijaya Fakultas Matematika dan Ilmu Pengetahuan Alam, Palembang, South Sumatra, Indonesia 7 Department of Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 8 Department of Vocational Education in Electronics Engineering, Universitas Muhammadiyah Riau, Pekanbaru, Riau, Indonesia 9 Primary School Teacher Education, Universitas Pendidikan Indonesia Fakultas Ilmu Pendidikan, Bandung, West Java, Indonesia 10 Teacher Professional Education, Universitas Musamus Fakultas Keguruan dan Ilmu Pendidikan, Merauke, Papua, Indonesia 11 Primary School Teacher Education, STKIP Taman Siswa Bima, Bima, West Nusa Tenggara, Indonesia 12 Information Technology Education, Universitas Stella Maris Sumba, Tambolaka, Indonesia Aris Susanto Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Project Administration, Writing – Review & Editing Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Project Administration, Writing – Review & Editing Nur Nafisatul Fithriyah Roles: Data Curation, Investigation, Resources, Validation Roles: Data Curation, Investigation, Resources, Validation Arik Umi Pujiastuti Roles: Formal Analysis, Investigation, Methodology, Validation, Writing – Review & Editing Roles: Formal Analysis, Investigation, Methodology, Validation, Writing – Review & Editing Zuli Nuraeni Roles: Data Curation, Project Administration, Resources, Software, Supervision Roles: Data Curation, Project Administration, Resources, Software, Supervision Witri Ramadhani Roles: Data Curation, Formal Analysis, Investigation, Resources, Validation, Writing – Review & Editing Roles: Data Curation, Formal Analysis, Investigation, Resources, Validation, Writing – Review & Editing Fitri Alfarisa Roles: Conceptualization, Investigation, Resources, Validation, Writing – Review & Editing Roles: Conceptualization, Investigation, Resources, Validation, Writing – Review & Editing Dewi Puji Rahayu Roles: Formal Analysis, Investigation, Methodology, Project Administration, Resources, Writing – Review & Editing Roles: Formal Analysis, Investigation, Methodology, Project Administration, Resources, Writing – Review & Editing Anisah Anisah Roles: Funding Acquisition, Methodology, Supervision, Validation, Visualization, Writing – Review & Editing Roles: Funding Acquisition, Methodology, Supervision, Validation, Visualization, Writing – Review & Editing Andry Ananda Putra Tanggu Mara Roles: Data Curation, Software, Supervision, Validation, Writing – Review & Editing Roles: Data Curation, Software, Supervision, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Doctoral Scholarship Program for Indonesian Lecturers (PDDI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of the Republic of Indonesia, and the Indonesian Endowment Fund for Education (LPDP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright © 2026 Susanto A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The author(s) is/are employees of the US Government and therefore domestic copyright protection in USA does not apply to this work. The work may be protected under the copyright laws of other jurisdictions when used in those jurisdictions. metrics | Views | Downloads | | |---|---|---| | F1000Research | - | - | | PubMed Central Data from PMC are received and updated monthly. | - | - | Citations CITE how to cite this article Susanto A, Fithriyah NN, Pujiastuti AU et al. A Unified Validation Framework for Assessing Green Skills Implementation from Basic to Vocational Education: A Measurement Invariance Approach [version 1; peer review: 1 approved with reservations]. F1000Research 2026, 15:203 (https://doi.org/10.12688/f1000research.175658.1) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. track receive updates on this article Track an article to receive email alerts on any updates to this article. Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 06 Feb 2026 Views 0 How to cite this report: Blose P. Reviewer Report For: A Unified Validation Framework for Assessing Green Skills Implementation from Basic to Vocational Education: A Measurement Invariance Approach [version 1; peer review: 1 approved with reservations]. F1000Research 2026, 15:203 (https://doi.org/10.5256/f1000research.193662.r464730) The direct URL for this report is: https://f1000research.com/articles/15-203/v1#referee-response-464730 https://f1000research.com/articles/15-203/v1#referee-response-464730 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Reviewer Report 28 Mar 2026 Princess Blose, University of South Africa, Pretoria, Gauteng, South Africa Approved with Reservations VIEWS 0 The article “A Unified Validation Framework for Assessing Green Skills Implementation from Basic to Vocational Education” presents the development and validation of a six‑factor instrument designed to measure green skills across primary, lower‑secondary, and vocational education. Using a multiphase ... Continue reading I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close The article “A Unified Validation Framework for Assessing Green Skills Implementation from Basic to Vocational Education” presents the development and validation of a six‑factor instrument designed to measure green skills across primary, lower‑secondary, and vocational education. Using a multiphase quantitative design, the authors conduct item development, expert review, exploratory and confirmatory factor analysis, and multi‑group measurement invariance testing. The study aims to demonstrate that green skills develop along a continuum and can be assessed consistently across educational levels. Overall, the study addresses a timely topic and contributes valuable empirical work to sustainability education and psychometric research. However, several areas require refinement to achieve clarity, coherence, and scientific robustness. The clarity of presentation is partly achieved. The abstract is overly dense and should be rewritten for better readability, reducing technical jargon while clearly presenting the study’s purpose, methods, and key findings. Throughout the manuscript, certain concepts-particularly green skills and measurement invariance-are repeated across sections without contributing new insights. Acronyms are also introduced before definitions in several places, which disrupts readability. The presentation of tables requires improvement; multiple tables appear consecutively without sufficient narrative engagement, making it difficult for readers to understand their relevance to the study. Engagement with the current literature is uneven. Although a wide range of sources is cited, the literature review tends to summarize rather than critically integrate the studies. Stronger synthesis is needed to justify the study’s conceptual framework and to highlight the specific research gap the instrument addresses. Additionally, the reference list contains formatting inconsistencies that must be corrected to maintain a uniform style. The methodology is generally appropriate, but certain procedures require clearer justification. The sampling strategy, described as stratified cluster sampling, is not sufficiently detailed. The authors should explain how clusters and strata were defined and why this sampling method was suitable. Similarly, analytical choices-such as the combined use of CFA, Rasch diagnostics, and MLR estimation-should be justified more explicitly. The conceptual figure presented in the methods section is not adequately explained, and the paper should elaborate on how the framework guided the instrument development and validation process. The results section is comprehensive but would benefit from stronger narrative integration. Each table should be introduced with a brief explanation of its purpose, followed by an interpretation of the findings. Claims in the results and discussion should be carefully framed to reflect the study’s empirical scope. To ensure scientific soundness, the following revisions are essential: - rewriting the abstract for clarity. - reducing conceptual repetition. - clearly defining and justifying all methodological choices. - explaining the application of the proposed framework. - improving table narration and formatting consistency. - correcting reference formatting; and - reframing claims to avoid overgeneralization beyond the sample. With these revisions, the study has strong potential to make a meaningful contribution to sustainability education and psychometric validation scholarship. Overall, the study addresses a timely topic and contributes valuable empirical work to sustainability education and psychometric research. However, several areas require refinement to achieve clarity, coherence, and scientific robustness. The clarity of presentation is partly achieved. The abstract is overly dense and should be rewritten for better readability, reducing technical jargon while clearly presenting the study’s purpose, methods, and key findings. Throughout the manuscript, certain concepts-particularly green skills and measurement invariance-are repeated across sections without contributing new insights. Acronyms are also introduced before definitions in several places, which disrupts readability. The presentation of tables requires improvement; multiple tables appear consecutively without sufficient narrative engagement, making it difficult for readers to understand their relevance to the study. Engagement with the current literature is uneven. Although a wide range of sources is cited, the literature review tends to summarize rather than critically integrate the studies. Stronger synthesis is needed to justify the study’s conceptual framework and to highlight the specific research gap the instrument addresses. Additionally, the reference list contains formatting inconsistencies that must be corrected to maintain a uniform style. The methodology is generally appropriate, but certain procedures require clearer justification. The sampling strategy, described as stratified cluster sampling, is not sufficiently detailed. The authors should explain how clusters and strata were defined and why this sampling method was suitable. Similarly, analytical choices-such as the combined use of CFA, Rasch diagnostics, and MLR estimation-should be justified more explicitly. The conceptual figure presented in the methods section is not adequately explained, and the paper should elaborate on how the framework guided the instrument development and validation process. The results section is comprehensive but would benefit from stronger narrative integration. Each table should be introduced with a brief explanation of its purpose, followed by an interpretation of the findings. Claims in the results and discussion should be carefully framed to reflect the study’s empirical scope. To ensure scientific soundness, the following revisions are essential: - rewriting the abstract for clarity. - reducing conceptual repetition. - clearly defining and justifying all methodological choices. - explaining the application of the proposed framework. - improving table narration and formatting consistency. - correcting reference formatting; and - reframing claims to avoid overgeneralization beyond the sample. With these revisions, the study has strong potential to make a meaningful contribution to sustainability education and psychometric validation scholarship. - Is the work clearly and accurately presented and does it cite the current literature? Yes - Is the study design appropriate and is the work technically sound? Yes - Are sufficient details of methods and analysis provided to allow replication by others? Yes - If applicable, is the statistical analysis and its interpretation appropriate? Partly - Are all the source data underlying the results available to ensure full reproducibility? Yes - Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Technology Education, Education for Sustainable Development, Engineering and Graphic Design, Indigenous Knowledge CITE HOW TO CITE THIS REPORT Blose P. Reviewer Report For: A Unified Validation Framework for Assessing Green Skills Implementation from Basic to Vocational Education: A Measurement Invariance Approach [version 1; peer review: 1 approved with reservations]. F1000Research 2026, 15:203 (https://doi.org/10.5256/f1000research.193662.r464730) The direct URL for this report is: https://f1000research.com/articles/15-203/v1#referee-response-464730 https://f1000research.com/articles/15-203/v1#referee-response-464730 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. Alongside their report, reviewers assign a status to the article: - Approved - Approved with reservations - Not approved | Invited Reviewers | | |---|---| | 1 | | | Version 1 06 Feb 26 | read | - Princess Blose, University of South Africa, Pretoria, South Africa Sign up for content alerts You are now signed up to receive this alert Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' - Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. - You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. - You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). - You work at the same institute as any of the authors. - You hope/expect to benefit (e.g. favour or employment) as a result of your submission. - You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' - You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. - You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. - You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Sign up for content alerts and receive a weekly or monthly email with all newly published articles Already registered? Sign in close Error Sign In If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. Email us for further assistance. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here. If you still need help with your Google account password, please click here. You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here. If you still need help with your Facebook account password, please click here. Code not correct, please try again Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait...