Nidhi,
As I said in my previous response, if you want 20 items with equal
factor loadings you can just specify the loading: e.g., for all
loadings of .6
GenData <- congeneric.sim(N=500, loads = rep(.6,20), short = FALSE)
The error variances will all be equal to 1 - loading^2 where loading
is the factor loading you specified.
This is R. If you want to know how a function works, list the source code.
Bill
At 9:47 AM -0500 12/27/08, Nidhi Kohli wrote:
Hi Bill,
Thank you very much for your response. You are right, I want to
simulate data set for 500 examinees across 20 items using Parallel
latent CTT model. As you know in Parallel Latent CTT model all the
error variances and factor loadings are equal across all the items.
Could you please let me know how can I incorporate common error
variance for 20 items in this R-program?
Regards,
Nidhi Kohli
***************************************
Nidhi Kohli, M.Ed.
Doctoral Student
Department of Measurement, Statistics
and Evaluation
University of Maryland
1230 Benjamin Building
College Park, MD 20742-1115
e-mail: nid...@umd.edu
***************************************
---- Original message ----
>Date: Fri, 26 Dec 2008 11:31:10 -0600
>From: William Revelle <li...@revelle.net>
Subject: Re: [R] Simulating dataset using Parallel Latent CTT model?
To: Nidhi Kohli <nid...@umd.edu>, r-help@r-project.org
Nidhi,
Presumably, you are trying to simulate 20 items all sharing one
general factor but having some error.
The model as you specified it has no error. Thus all the
correlations will be 1 and the factors will not make any sense.
Most items have loadings on a general factor of the order of about
.4 to .6. You might try:
>GenData <- congeneric.sim(N=500, loads = rep(.5,20), short = FALSE)
Then you will find that the factor scores found by factor.pa
correlate at .93 with the latent variable.
FactorScore=factor.pa(GenData$observed,1,scores = "TRUE", rotate="none")
round(cor(FactorScore$scores,GenData$latent),2)
Bill
At 8:27 AM -0500 12/26/08, Nidhi Kohli wrote:
I am trying to simulate a dataset using Parallel Latent CTT model
and this is what i have done so far:
(START)
#Importing psych library for all the simulation related functions
library(psych)
# Settting the working directory path to C:/NCME
path="C:/NCME"
setwd(path)
#Using the function to generate the data
GenData <- congeneric.sim(N=500, loads =
c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1), short = FALSE)
#Rounding upto 2 decimal places while showing the correlation matrix
round(cor(GenData$observed),2)
#Factor Score
FactorScore=factor.pa(GenData$observed,1,scores = "TRUE", rotate="none")
round(cor(FactorScore$scores,GenData$latent),2)
(END)
Please let me know if I am moving into the right direction, if not
then, please let me know the correct way to simulate the dataset
Thanks in Advance
Regards,
Nidhi Kohli
***************************************
Nidhi Kohli, M.Ed.
Doctoral Student
Department of Measurement, Statistics
and Evaluation
University of Maryland
1230 Benjamin Building
College Park, MD 20742-1115
e-mail: nid...@umd.edu
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
--
William Revelle http://personality-project.org/revelle.html
Professor http://personality-project.org/personality.html
Department of Psychology http://www.wcas.northwestern.edu/psych/
Northwestern University http://www.northwestern.edu/
Attend ISSID/ARP:2009 http://issid.org/issid.2009/
--
William Revelle http://personality-project.org/revelle.html
Professor http://personality-project.org/personality.html
Department of Psychology http://www.wcas.northwestern.edu/psych/
Northwestern University http://www.northwestern.edu/
Attend ISSID/ARP:2009 http://issid.org/issid.2009/
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
