A modified procedure for parallel analysis of ordered categorical data

Parallel analysis has been well documented to be an effective and accurate method for determining the number of factors to retain in exploratory factor analysis. The O'Connor (2000) procedure for parallel analysis has many benefits and is widely applied, yet it has a few shortcomings in dealing with missing data and ordinal variables. To address these technical issues, we adapted and modified the O'Connor procedure to provide an alternative method that better approximates the ordinal data by factoring in the frequency distributions of the variables (e.g., the number of response categories and the frequency of each response category per variable). The theoretical and practical differences between the modified procedure and the O'Connor procedure are discussed. The SAS syntax for implementing this modified procedure is also provided.     

A FORTRAN program for lag sequential analysis of contingency and cyclicity in behavioral interaction data

One method for identifying contingency relationships in a sequence of observed behaviors studies occurrences of matching behaviors at successive event or time steps from a criterion event. This paper briefly summarizes the method and presents a computer program for generating lag conditional probabilities and associated measures for assessing statistical significance.