Vietnam V-STEM2030 Dataset: Competencies of Pre-Service and In-Service STEM Teachers Across Nine Provinces
Description
This article presents the V-STEM2030 dataset, a multi-site collection of responses from 612 participants, including 412 pre-service teachers and 200 in-service teachers across nine provinces in Vietnam. The dataset was developed to document the competency profiles of Vietnam’s emerging STEM teacher workforce, using a 40-item Likert scale aligned with five domains: pedagogical STEM design, interdisciplinary integration, inquiry and problem solving, technology/AI integration, and engineering practices. Data were gathered through supervised in-class administrations and online distribution managed by provincial training centers. To support replicability, the article includes the full questionnaire, variable codebook, and analysis scripts used for reliability testing, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). The dataset is intended to support comparative studies, instrument validation, policy planning, and competency-based curriculum development for teacher education in the Global South.
Files
Steps to reproduce
To enable full transparency and reproducibility, all analytic steps used in the study can be replicated using the publicly shared dataset, codebook, and syntax files. Step 1 — Access the raw data Download the dataset package from the repository: VSTEM2030_dataset.csv VSTEM2030_codebook.txt README.txt These files contain variable names, descriptions, and the raw numerical responses of 612 participants. Step 2 — Load the data into a statistical environment Any of the following can be used: R data <- read.csv("VSTEM2030_dataset.csv") SPSS Open VSTEM2030_dataset.csv → define variable formats following codebook.txt Step 3 — Inspect missing data and prepare the dataset Run frequency check Apply EM-based estimation for <1% missing values (syntax provided) (Automatic handling included in R_analysis_syntax.R or SPSS_syntax.sps) Step 4 — Compute reliability Using provided syntax: R: library(psych) alpha(data[,factor_items]) SPSS: Run block RELIABILITY commands in SPSS_syntax.sps. Step 5 — Conduct Exploratory Factor Analysis (EFA) Run: ✔ Principal Axis Factoring ✔ Oblimin rotation ✔ Scree plot/parallel analysis for factor retention The command block is included in: R_analysis_syntax.R SPSS_syntax.sps Figures: scree_plot.png and parallel_analysis.png Step 6 — Conduct Confirmatory Factor Analysis (CFA) Open AMOS_model_template.txt or run R lavaan syntax in R_analysis_syntax.R. Models tested: (a) One-factor model (b) Five correlated factors (c) Second-order hierarchical model Figures such as cfa_diagram.png visualise the fitted structure. Step 7 — Conduct Structural Equation Modelling (SEM) Execute SEM paths: Pedagogical → Integration → Inquiry → AI Integration → Engineering Practice Follow the AMOS specification or lavaan syntax. SEM output is illustrated in sem_diagram.png. Step 8 — Test measurement invariance Run configural, metric, and scalar invariance models comparing: Pre-service vs in-service teachers Syntax blocks provided in R script. Step 9 — Validate and visualise results Generated figures include: Scree plot Parallel analysis Heatmap of loadings CFA model diagram SEM pathway map These are provided in /figures folder. Step 10 — Report findings using the data dictionary Interpretation of scale scores and factor loadings must refer to VSTEM2030_codebook.txt, which describes each competency dimension and wording of each item.