# Looking for a step through an example of a factor analysis on dichotomous data (binary variables) using R

I have some dichotomous data, only binary variables, and my boss asked me to perform a factor analysis using the tetrachoric correlations matrix. I’ve previously been able to teach myself how to run different analyses based on the examples here and at the UCLA’s stat site and other sites like it, but I can’t seem to find a step through an example of a factor analysis on dichotomous data (binary variables) using R.

I did see chl's response to a somewhat simular question and I also saw ttnphns' answer, but I am looking for something even more spelled out, a step through an example I can work with.

Does anyone here know of such a step through an example of a factor analysis on binary variables using R?

### Update 2012-07-11 22:03:35Z

I should also add that I am working with an established instrument, that have three dimension, to which we have added some additional questions and we now hope to find four distinct dimension. Furthermore, our sample size is only $n=153$, and we currently have $19$ items. I compared our sample size and our number of items to a number of psychology articles and we are definitely in the lower end, but we wanted to try it anyway. Though, this is not important for the step through example I am looking for and caracal’s example below looks really amazing. I will work my way thru it using my data first thing in the morning.

• As FA might not necessarily be the best choice, depending on the question you are interested in, could you say more on the context of your study?
– chl
Jul 11, 2012 at 18:44
• @chl, thank you for responding to my question, we are investigating the underlying factor structure of some questions regarding PTSD. We are interested in 1) identifying some domains (clusters) and 2) investigate how much the different questions load on each domain. Jul 11, 2012 at 19:33
• Just to be sure, (a) what is your sample size, (b) is this an existing (already validated) instrument or a self-made questionnaire?
– chl
Jul 11, 2012 at 19:41
• @chl, I really appreciate your questions. (a) Our sample size is $n=153$, and we currently have 19 items. I compared our sample size and our number of items to what I could find in Journal of Traumatic Stress and we are definitely in the lower end, but we wanted to try it anyway. (b) We are using an existing instrument, but with some self-made questions added as we believe they are missing. Jul 11, 2012 at 19:51
• Ok, thanks for this. That should be easy to set up a working example with illustration in R.
– chl
Jul 11, 2012 at 19:54

I take it the focus of the question is less on the theoretical side, and more on the practical side, i.e., how to implement a factor analysis of dichotomous data in R.

First, let's simulate 200 observations from 6 variables, coming from 2 orthogonal factors. I'll take a couple of intermediate steps and start with multivariate normal continuous data that I later dichotomize. That way, we can compare Pearson correlations with polychoric correlations, and compare factor loadings from continuous data with that from dichotomous data and the true loadings.

set.seed(1.234)
N <- 200                             # number of observations
P <- 6                               # number of variables
Q <- 2                               # number of factors

Lambda <- matrix(c(0.7,-0.4, 0.8,0, -0.2,0.9, -0.3,0.4, 0.3,0.7, -0.8,0.1),
nrow=P, ncol=Q, byrow=TRUE)


Now simulate the actual data from the model $x = \Lambda f + e$, with $x$ being the observed variable values of a person, $\Lambda$ the true loadings matrix, $f$ the latent factor score, and $e$ iid, mean 0, normal errors.

library(mvtnorm)                      # for rmvnorm()
FF  <- rmvnorm(N, mean=c(5, 15), sigma=diag(Q))    # factor scores (uncorrelated factors)
E   <- rmvnorm(N, rep(0, P), diag(P)) # matrix with iid, mean 0, normal errors
X   <- FF %*% t(Lambda) + E           # matrix with variable values
Xdf <- data.frame(X)                  # data also as a data frame


Do the factor analysis for the continuous data. The estimated loadings are similar to the true ones when ignoring the irrelevant sign.

> library(psych) # for fa(), fa.poly(), factor.plot(), fa.diagram(), fa.parallel.poly, vss()
> fa(X, nfactors=2, rotate="varimax")$loadings # factor analysis continuous data Loadings: MR2 MR1 [1,] -0.602 -0.125 [2,] -0.450 0.102 [3,] 0.341 0.386 [4,] 0.443 0.251 [5,] -0.156 0.985 [6,] 0.590  Now let's dichotomize the data. We'll keep the data in two formats: as a data frame with ordered factors, and as a numeric matrix. hetcor() from package polycor gives us the polychoric correlation matrix we'll later use for the FA. # dichotomize variables into a list of ordered factors Xdi <- lapply(Xdf, function(x) cut(x, breaks=c(-Inf, median(x), Inf), ordered=TRUE)) Xdidf <- do.call("data.frame", Xdi) # combine list into a data frame XdiNum <- data.matrix(Xdidf) # dichotomized data as a numeric matrix library(polycor) # for hetcor() pc <- hetcor(Xdidf, ML=TRUE) # polychoric corr matrix -> component correlations  Now use the polychoric correlation matrix to do a regular FA. Note that the estimated loadings are fairly similar to the ones from the continuous data. > faPC <- fa(r=pc$correlations, nfactors=2, n.obs=N, rotate="varimax")
> faPC$loadings Loadings: MR2 MR1 X1 -0.706 -0.150 X2 -0.278 0.167 X3 0.482 0.182 X4 0.598 0.226 X5 0.143 0.987 X6 0.571  You can skip the step of calculating the polychoric correlation matrix yourself, and directly use fa.poly() from package psych, which does the same thing in the end. This function accepts the raw dichotomous data as a numeric matrix. faPCdirect <- fa.poly(XdiNum, nfactors=2, rotate="varimax") # polychoric FA faPCdirect$fa$loadings # loadings are the same as above ...  EDIT: For factor scores, look at package ltm which has a factor.scores() function specifically for polytomous outcome data. An example is provided on this page -> "Factor Scores - Ability Estimates". You can visualize the loadings from the factor analysis using factor.plot() and fa.diagram(), both from package psych. For some reason, factor.plot() accepts only the $fa component of the result from fa.poly(), not the full object.

factor.plot(faPCdirect$fa, cut=0.5) fa.diagram(faPCdirect) Parallel analysis and a "very simple structure" analysis provide help in selecting the number of factors. Again, package psych has the required functions. vss() takes the polychoric correlation matrix as an argument. fa.parallel.poly(XdiNum) # parallel analysis for dichotomous data vss(pc$correlations, n.obs=N, rotate="varimax")   # very simple structure


Parallel analysis for polychoric FA is also provided by the package random.polychor.pa.

library(random.polychor.pa)    # for random.polychor.pa()
random.polychor.pa(data.matrix=XdiNum, nrep=5, q.eigen=0.99) Note that the functions fa() and fa.poly() provide many many more options to set up the FA. In addition, I edited out some of the output which gives goodness of fit tests etc. The documentation for these functions (and package psych in general) is excellent. This example here is just intended to get you started.

• Your step through example looks great. I will work my way thru it using my data in the morning and get back to you. Thank you for taking the time to write this. If you happen to have some theoretical references I would also be interested in them. Chl recommended Revelle's textbook for psychometrics in R and I am definitely going to take a look at that. Thanks Jul 11, 2012 at 22:53
• @caracal: does psych allow somehow to estimate factor scores when poly/tetra-choric correlations are used in place of usual Pearson r? Jul 12, 2012 at 7:35
• Sorry, @caracal, I'm not R user. That's why I'm asking it. Since you've used not original Pearson r but tetrachoric r you've lost straightforward linear-algebraic link between original binary data and the loading matrix. I imagine that in this case some special algo would be used (e.g. based on EM approach) in place of classic regression/Bartlett one. So does psych give its due to the fact that we were dealing with tetrachoric r, not usual r, when it computes factor scores, or doesn't? Jul 12, 2012 at 9:41
• @EricFail As polychoric correlation matrices are estimated by going through the pairwise correlations, a non positive definite end matrix indeed becomes more common as the number of variables increases, and the number of observations is fixed (see this MPlus discussion). Functions like nearcor() from sfsmisc or cor.smooth() from psych are used for this case. Jul 13, 2012 at 9:55
• @ttnphns Sorry, I misunderstood your question. Good question! I originally assumed that something like MPlus technical appendix 11 was implemented, but looking at the code for psych's factor.scores(), this is not the case. Instead, scores are calculated just like in the continuous case. However, the factor.scores() function in package ltm seems to implement the correct procedures, see this example -> "Factor Scores - Ability Estimates" and the help page. Jul 13, 2012 at 10:19