Anonymous posted on Wednesday, September 17, 2003 - 1:30 am
I conducted EFA based on Pearson correlation with 7-point scale items. However, journal reviewers asked me to conduct EFA treating my data as ordinal and to use a special program such as Mplus or MICROFACT. I understand their point but I assumed that 7-point would be enough for EFA using Pearson correlation. What do you think? Am I correct? If so, could you please introduce any references? Thanks so much in advance.
It depends on whether your variables have strong floor or ceiling effects. If they do, you most likely need to use a method that treats them as categorical not continuous variables. If they do not, you are most likely ok using Pearson corrleations. I don't know of any references specific to this topic. You could generate data like yours and analyze it using Pearson correlations and see how well you recover your parameter estimates. Or you could just analyze the data both ways and see what if any differences you find.
JeremyMiles posted on Wednesday, March 24, 2004 - 6:38 am
A paper related to this issue:
Hsu, T. and L. Feldt (1969). "The effect of limitations of the number of criterion score values on the significance level of the F-test." American Educational Research Journal 6: 515-527.
I prefer treating likert scale variables as ordered categorical versus continuous. When there are no strong floor or ceiling effects, the results may be close when treating them as continuous. Model estimation is computationally more demanding when treating them as categorical depending on the number of items in the analysis.
Theda Radtke posted on Wednesday, February 24, 2010 - 7:21 am
Dear Linda and Bengt Muthen, I`m also interested in the problem of the message MPlus gave me: NO CONVERGENCE. NUMBER OF ITERATIONS EXCEEDED. PROBLEM OCCURRED IN EXPLORATORY ANALYSIS WITH 4 FACTOR(S).
Because I had to analyze 5 data sets, it would be helpful if you can tell me possible reasons for that warning. Is the reason a distribution of the data or something like that? Thank you.