Simulated Intervention on Cross-Sectional Nested Data: Development of a Multilevel NIRA Approach

TL;DR

This paper introduces a multilevel extension of the NIRA algorithm to analyze cross-sectional nested data, enabling causal simulation in psychological network research with hierarchical structures.

Contribution

The study develops a novel multilevel NIRA algorithm that extends causal simulation capabilities to nested cross-sectional data structures, addressing a key limitation of existing methods.

Findings

Provides a detailed explanation of the multilevel NIRA algorithm

Discusses potential applications and practical implications

Identifies limitations and future research directions

Abstract

With the rise of the network perspective, researchers have made numerous important discoveries over the past decade by constructing psychological networks. Unfortunately, most of these networks are based on cross-sectional data, which can only reveal associations between variables but not their directional or causal relationships. Recently, the development of the nodeIdentifyR algorithm (NIRA) technique has provided a promising method for simulating causal processes based on cross-sectional network structures. However, this algorithm is not capable of handling cross-sectional nested data, which greatly limits its applicability. In response to this limitation, the present study proposes a multilevel extension of the NIRA algorithm, referred to as multilevel NIRA. We provide a detailed explanation of the algorithm's core principles and modeling procedures. Finally, we discuss the potential applications and practical implications of this approach, as well as its limitations and directions for future research.