How to reduce the number of rating scale items without predictability loss?

TL;DR

This paper introduces an innovative method using AUC ROC to significantly reduce rating scale items while maintaining predictability, verified through GRM and CFA analyses, thus streamlining data collection without loss of reliability.

Contribution

The paper presents a novel application of AUC ROC for reducing rating scale items without compromising their predictive power or reliability.

Findings

Reduced rating scale items to 28.57% of original

Maintained scale reliability after reduction

Validated method with GRM and CFA analyses

Abstract

Rating scales are used to elicit data about qualitative entities (e.g., research collaboration). This study presents an innovative method for reducing the number of rating scale items without the predictability loss. The "area under the receiver operator curve method" (AUC ROC) is used. The presented method has reduced the number of rating scale items (variables) to 28.57\% (from 21 to 6) making over 70\% of collected data unnecessary. Results have been verified by two methods of analysis: Graded Response Model (GRM) and Confirmatory Factor Analysis (CFA). GRM revealed that the new method differentiates observations of high and middle scores. CFA proved that the reliability of the rating scale has not deteriorated by the scale item reduction. Both statistical analysis evidenced usefulness of the AUC ROC reduction method.