Eigenvalues-Based Approaches to Identifying the Optimal Number of Factors in Multilevel Exploratory Factor Analysis

• In Event: Marriott Roundtable Session Five
  In Roundtable Session: Advanced Research Methodology (Table 1)

Abstract

Multilevel exploratory factor analysis (MEFA) is a technique to extract latent factors underlying observed items in multilevel data. This study investigated parallel analysis (PA) criterion, in identifying the correct number of factors in MEFA. The PA approach performed well in extracting the Level-1 model factor number, and the correct rate of identifying the two-factor model was more than 60%. However, when the PA approach was used to identify the Level-2 model factor number, results were not as good as at Level-1.

Authors

• Yuhong Ji, Pearson Education, Inc.
  Presenting Author

• Wen Luo, Texas A&M University
  Non-Presenting Author

• Hok Chio Mark Lai, University of Southern California
  Non-Presenting Author

• Oiman Kwok, Texas A&M University
  Non-Presenting Author

• Myeongsun Yoon, Texas A&M University
  Non-Presenting Author