Cross-cultural Adaptation and Psychometric Validation of the Perceived Quality of University Experiences Scale (PQUES) among Business Students in Chile

Supplementary material and analysis code.

Authors: C. Frex; R. Monge; R. Fuentes; H. Fernández

Contact: rmonge@udla.cl

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Overview

This repository contains the code and data required to reproduce the analyses reported in the manuscript. Two complementary analyses are provided:

• Psychometric validation (R): split-sample exploratory and confirmatory factor analysis (EFA/CFA), reliability, convergent and discriminant validity, exploratory graph analysis (EGA), and measurement invariance.
• Content validity (Python / Google Colab): Aiken's V coefficient and its 95% confidence interval from expert-judge ratings.

Repository structure

.
├── README.md
├── PQUES_psychometric_validation.R       # EFA/CFA, reliability, validity, EGA, invariance
├── PQUES_content_validity_AikenV.ipynb   # content validity (Aiken's V)
└── data/
    ├── data.csv                          # student survey dataset (PQUES)
    ├── demo_judges.csv                   # expert-judge profile
    └── data_Aiken.csv                    # expert ratings (relevance / wording)

Data

All datasets are placed in the data/ folder.

data/data.csv — student responses used for the psychometric validation:

• Sociodemographic variables: sex, edad, universidad, carrera, anio_ingreso, modalidad, trabaja, donde_trabaja, financiamiento.
• Instrument-evaluation items: aceptacion, comprension, satisfaccion.
• 34 ordinal PQUES items organized into five subscales — IS (6 items), CC (7), PA (8), CA (8), and DC (5).

data/demo_judges.csv — sociodemographic profile of the expert judges: sexo, grado_academico, especializacion, experiencia_profesional, anios_experiencia, exp_valid_inst.

data/data_Aiken_NPS.csv — expert ratings in wide format: a dimension column (relevance / wording) plus one column per item; cell values are expert ratings on a 4-point scale.

Requirements

R (>= 4.3). Install the required packages once:

install.packages(c(
  "dplyr", "tidyverse", "readr", "purrr", "tibble",
  "gtsummary", "huxtable", "labelled", "matrixStats",
  "ggplot2", "gridExtra", "corrplot", "RColorBrewer", "likert", "scales",
  "psych", "lavaan", "semTools", "semPlot",
  "parameters", "performance", "nFactors", "FactoMineR", "factoextra",
  "QuantPsyc", "nortest", "MVN", "EGAnet", "see", "knitr"
))

Python (Google Colab). Packages: pandas, numpy, scipy, matplotlib, seaborn, tabulate (all preinstalled in Colab).

How to reproduce

1. Clone the repository and keep the data files inside data/.
2. Psychometric validation (R): open PQUES_psychometric_validation.R with the repository root as the working directory (so that data/data.csv resolves), then run the script. A fixed seed makes the EFA/CFA split reproducible; package versions are reported by sessionInfo() at the end.
3. Content validity (Aiken's V): open PQUES_content_validity_AikenV.ipynb in Google Colab and run the cells in order. The notebook reads the judge datasets from data/.

Outputs

• R script: summary tables (participant characteristics, comparability, reliability, CR/AVE, Fornell–Larcker, HTMT, invariance) printed to the console.
• Aiken's V notebook: a results table per item with V and its 95% CI, and a forest-plot figure exported as V_Aiken_final.pdf and V_Aiken_final.png.

License

Released under the MIT License.

Citation

Frex, C., Monge, R., Fuentes, R., & Fernández, H. (2026). Cross-cultural adaptation and psychometric validation of the Perceived Quality of University Experiences Scale among business students in Chile. [Journal / DOI]