Network analysis approach to Likert-style surveys

TL;DR

This paper introduces a network analysis methodology for Likert-style surveys, enabling the modeling of item interconnectedness and thematic identification, offering a novel perspective compared to traditional methods like PCA.

Contribution

It presents a new network analysis approach for Likert surveys, demonstrating its effectiveness in identifying themes and differences in survey data.

Findings

Successful creation of meaningful survey item networks

Identification of key themes in student experience data

Network analysis outperforms principal component analysis in thematic detection

Abstract

Likert-style surveys are a widely used research instrument to assess respondents' preferences, beliefs, or experiences. In this paper, we propose and demonstrate how network analysis (NA) can be employed to model and evaluate the interconnectedness of items in Likert-style surveys. We explore the advantages of this approach by applying the methodology to the aspects of student experience scale datasets and compare the results to the principal component analysis. We successfully create a meaningful network based on survey item response similarity and use modular analysis of the network to identify larger themes built from the connections of particular aspects. The modular NA of the network of survey items identifies important themes that highlight differences in students' overall experiences. Our network analysis for Likert-style surveys methodology is widely applicable and provides a new way to investigate phenomena assessed by Likert-style surveys.