# Information-Reduction Ability Assessment in the Context of Complex Problem-Solving

## TL;DR

This paper introduces a new tool to assess the ability to reduce information in complex problem-solving scenarios, validated with students and showing strong reliability and validity.

## Contribution

The novel Little Monster Clinic (LMC) assessment tool captures information-reduction ability in complex problem-solving with high ecological validity.

## Key findings

- The LMC tool identified six key indicators of information reduction with high internal consistency (α = 0.83).
- Confirmatory factor analysis supported a one-factor model of information reduction with strong fit indices.
- LMC showed significant criterion-related validity with a correlation of r = 0.43 to the Genetics Lab.

## Abstract

In this era with an increasing overabundance of information, the ability to distill relevant information, i.e., “information reduction”, is becoming more crucial to daily functioning. However, the fact that information reduction is most prominent in complex situations poses challenges for measuring and quantifying this ability. Existing assessments tend to suffer from either too little complexity, compromising ecological validity, or too much complexity, which makes distinguishing and measuring information-reduction behavior difficult. To address this gap in the literature, our study developed a novel assessment tool, the Little Monster Clinic (LMC), designed to capture the information-reduction process within complex problem-solving scenarios. Following the classic complex problem-solving (CPS) framework, LMC simulates real-world medical situations and provides a sufficiently complex task for assessing information-reduction ability. We recruited 303 students to validate our tool and identified six key indicators for information reduction, which demonstrated a high degree of internal consistency (α = 0.83). Structural validity from the confirmatory factor analysis (CFA) supported a one-factor model of information reduction based on the extracted indicators (χ2 = 14.872, df = 5, χ2/df = 2.774, CFI = 0.989, TLI = 0.967, RMSEA = 0.077, SRMR = 0.024). The significant correlation (r = 0.43, p < 0.01) between LMC and Genetics Lab demonstrated its criterion-related validity. Furthermore, exploratory analysis highlighted the importance of identifying key relevant information during the process of information reduction. These findings lend support to both the theoretical foundation and practical applications of information-reduction assessment.