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

Li Jen Weng, Chung Ping Cheng

研究成果: Article同行評審

7 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)5367-5377
頁數11
期刊Communications in Statistics: Simulation and Computation
46
發行號7
DOIs
出版狀態Published - 2017 8月 9

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 建模與模擬

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