A Visual-Graphical Exploratory Data Analysis Framework for Analyzing Multilevel Data

• In Event: AERA Poster Session 4
  In Poster Session: Division D Section 2 Poster Session 1

Abstract

Multilevel models have been widely used for the analysis of data with complex patterns of variability. Visual-graphical analysis can be used as an auxiliary tool to help researchers intuitively understand the structure of their data. The present study has two goals. The first goal is to establish a sequence of visual-graphical procedures to help researchers better examine multilevel data structures before carrying out more formal modeling. The second goal is to demonstrate the usefulness and effectiveness of our visual-graphical analysis scheme in unearthing credible and actionable insights from empirical datasets. We aim to show how our methodology can facilitate scholars from variegated domains address important research questions and establish empirically supported theoretical models.

Authors

• Shu-Ping Chen, National Cheng Kung University

• Chung-Ping Cheng