Parallel Analysis with Unidimensional Binary Data

The present simulation investigated the performance of parallel analysis for unidimensional binary data. Single-factor models with 8 and 20 indicators were examined, and sample size (50, 100, 200, 500, and 1,000), factor loading (.45, .70, and .90), response ratio on two categories (50/50, 60/40, 70...

Full description

Saved in:
Bibliographic Details
Published in:Educational and psychological measurement 2005-10, Vol.65 (5), p.697-716
Main Authors: Weng, Li-Jen, Cheng, Chung-Ping
Format: Article
Language:English
Subjects:
Comparative analysis
Correlation
Correlation analysis
Data Analysis
Eigenvalues
Evaluation Methods
Factor Analysis
Responses
Sample Size
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The present simulation investigated the performance of parallel analysis for unidimensional binary data. Single-factor models with 8 and 20 indicators were examined, and sample size (50, 100, 200, 500, and 1,000), factor loading (.45, .70, and .90), response ratio on two categories (50/50, 60/40, 70/30, 80/20, and 90/10), and types of correlation coefficients (phi and tetrachoric correlations) were manipulated. The results indicated that parallel analysis performed well in identifying the number of factors. The performance improved as factor loading and sample size increased and as the percentages of responses on two categories became close. Using the 95th and 99th percentiles of the random data eigenvalues as the criteria for comparison in parallel analysis yielded higher correct rate than using mean eigenvalues.
ISSN:0013-1644
1552-3888
DOI:10.1177/0013164404273941