Is categorization of random data necessary for parallel analysis on Likert-type data?

Li Jen Weng, Chung Ping Cheng

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Random eigenvalues are the key elements in parallel analysis. When analyzing Likert-type data, is it necessary to convert the continuous random data to discrete type before estimating eigenvalues? The study compared the random eigenvalues obtained from continuous and categorized random data from two popular computer programs to be used as the basis for comparison in conducting parallel analysis on Likert-type data. Results indicated that categorized random data gave eigenvalues and number of factors similar to those obtained from continuous random data. It is suggested that when conducting parallel analysis on Likert-type data by the two programs, the conversion is unnecessary.

Original languageEnglish
Pages (from-to)5367-5377
Number of pages11
JournalCommunications in Statistics: Simulation and Computation
Volume46
Issue number7
DOIs
Publication statusPublished - 2017 Aug 9

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation

Fingerprint Dive into the research topics of 'Is categorization of random data necessary for parallel analysis on Likert-type data?'. Together they form a unique fingerprint.

Cite this