A psychometric data science approach to study latent variables: a case of class quality and student satisfaction

Abstract

Data science follows a data-driven approach focused on non-hypothesised pattern discovery from data in an automatic manner (or semi-automatic). Social science areas frequently address latent variables, whose study is mainly guided by a deductive paradigm and requires a confirmatory scope. Both areas, data science and latent variable research, have ample development, but their combination is not mature. The objective is to develop a practical and reproducible framework for data science projects from a psychometric perspective for the study of latent variables. The framework is practical and integrative, and consists of nine steps, which guide from the construct definition, discovery and confirmed patterns (structural equation modelling and considering goodness-of-fit, reliability, validity, equity and subjects’ segmentation) to the use of patterns. This last step includes three sub-steps: one for structural hypotheses contrast and two computational approaches (supervised: machine learning; and non-supervised: principal component analysis). Additionally, the framework is efficient and accessible due to its semi-automation using the R language. The framework is applied to study the relationship between class quality and student satisfaction and is consistent with the reproducible research paradigm, widely used in computational areas and recently demanded by social science.

Keywords:

• data science
• latent variable
• structural equation modelling
• psychometry
• class quality
• student satisfaction

Acknowledgements

MINCIENCIAS: Ministry of Science, Technology and Innovation, Colombia. COLFUTURO/Fundación para el futuro de Colombia.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

This article includes a supplementary material (using Rmarkdown), which presents evidence of the automation of the framework in R (in a minutes, automatically, it provides all the results of this article) (link: https://www.tandfonline.com/doi/suppl/10.1080/14783363.2021.1980381). This document facilitates reproducibility of the results, using the framework in other contexts, and nourish teaching–learning processes in the implied topics.

Additional information

Funding

This work was supported by Ministry of Science, Technology and Innovation, Colombia (MINCIENCIAS).