#
#
############### Principal Components Analysis & Factor Analysis ###############
#
# This script assumes you have worked through all the previous notes from
# the web page and you have downloaded, installed, and updated all available
# R packages.
# Load the following libraries if you have not already; when an additional
# library is needed, it will be listed in the script and loaded.
library(foreign)
library(Rcmdr)
# Start by using the 'foreign' library to import 'Example Data 5.sav' (available
# from the web page) assigning the name 'example5'.
example5 <- read.spss("http://www.unt.edu/rss/class/Jon/R_SC/Module3/ExampleData5.sav",
use.value.labels=FALSE, max.value.labels=Inf, to.data.frame=TRUE)
attach(example5)
nrow(example5)
names(example5)
# Remove missing data so the matrix/data set is made of complete observations.
ex5 <- data.frame(na.omit(example5[,c("b1","b2","b3","b4","b5","b6","b7","b8",
"b9","b10","bt","ap1","ap2","ap3","ap4","ap5","ap6","ap7","ap8","ap9","ap10",
"ap11","ap12","ap13","ap14","ap15","apt")]))
nrow(ex5)
detach(example5)
attach(ex5)
###############################################################################
# Before we begin with the analysis script; let's take a moment to address and
# hopefully clarify one of the most confusing and misarticulated issues in
# statistical teaching and practice literature. An ambitious goal to be sure.
# First, Principal Components Analysis (PCA) is a variable reduction technique which
# maximizes the amount of variance accounted for in the observed variables by a
# smaller group of variables called COMPONENTS. As an example, consider the
# following situation. Let's say, we have 500 questions on a survey we designed
# to measure stubbornness. We want to reduce the number of questions so that it
# does not take someone 3 hours to complete the survey. It would be appropriate to
# use PCA to reduce the number of questions by identifying and removing redundant
# questions. For instance, if question 122 and question 356 are virtually identical
# (i.e. they ask the exact same thing but in different ways), then one of them is
# not necessary. The PCA process allows us to reduce the number of questions or
# variables down to their PRINCIPAL COMPONENTS.
# PCA is commonly, but very confusingly, called exploratory factor analysis (EFA).
# The use of the word factor in EFA is inappropriate and confusing because we are
# really interested in COMPONENTS, not factors. This issue is made more confusing
# by some software packages (e.g. PASW / SPSS, SAS) which list or use PCA under the
# heading factor analysis.
# Second, Factor Analysis (FA) is typically used to confirm the latent factor
# structure for a group of measured variables. Latent factors are unobserved
# variables which typically can not be directly measured; but, they are assumed to
# cause the scores we observe on the measured or indicator variables. FA is a model
# based technique. It is concerned with modeling the relationships between measured
# variables, latent factors, and error.
# As stated in O'Rourke, Hatcher, and Stepanski (2005):
# "Both (PCA & FA) are methods that can be used to identify groups of observed
# variables that tend to hang together empirically. Both procedures can also
# be performed with the SAS FACTOR procedure and they generally tend to provide
# similar results. Nonetheless, there are some important conceptual differences
# between principal component analysis and factor analysis that should be
# understood at the outset. Perhaps the most important deals with the assumption
# of an underlying causal structure. Factor analysis assumes that the covariation
# in the observed variables is due to the presence of one or more latent variables
# (factors) that exert causal influence on these observed variables" (p. 436).
# Final thoughts. Both PCA and FA can be used as exploratory analysis. But; PCA is
# predominantly used in an exploratory fashion and almost never used in a confirmatory
# fashion. FA can be used in an exploratory fashion, but most of the time it is used
# in a confirmatory fashion because it is concerned with modeling factor structure.
# The choice of which is used should be driven by the goals of the analyst. If you
# are interested in reducing the observed variables down to their principal components
# while maximizing the variance accounted for in the observed variables by the components,
# then you should be using PCA. If you are concerned with modeling the latent factors
# (and their relationships) which cause the scores on your observed variables, then
# you should be using FA.
#### REFERENCE####
# O'Rourke, N., Hatcher, L., & Stepanski, E.J. (2005). A step-by-step approach to using
# SAS for univariate and multivariate statistics, Second Edition. Cary, NC: SAS
# Institute Inc.
##############################################################################
############ PRINCIPAL COMPONENT ANALYSIS ############
# Using PRINCIPAL COMPONENTS EXTRACTION
# Commonly called Principal Components Analysis (PCA)
##### FIRST PCA using 'princomp' function (as would be done using menus in Rcmdr).
pca.1 <- princomp(~ap1+ap2+ap3+ap4+ap5+ap6+ap7+ap8+ap9+ap10+ap11+ap12+ap13+ap14+ap15+b1+b2+b3+b4+b5+b6+b7+b8+b9+b10,
cor=TRUE, data=ex5, scores = TRUE)
unclass(loadings(pca.1)) # component loadings
pca.1$sd^2 # component variances
summary(pca.1) # proportions of variance
screeplot(pca.1) # if you would like a scree plot
# To extract the component scores use below, where the [,1] through [,nc] where
# 'nc' represents the number of components retained.
first.component.scores <- pca.1$scores[,1]
summary(first.component.scores)
length(first.component.scores)
second.component.scores <- pca.1$scores[,2]
summary(second.component.scores)
length(second.component.scores)
##### SECOND & THIRD PCA using library(psych) 'principal' function.
# Create a correlation matrix (also called a matrix of association) object
# of the items for PCA.
cor.matrix.1 <- cor(ex5[,c("b1","b2","b3","b4","b5","b6","b7","b8","b9",
"b10","ap1","ap2","ap3","ap4","ap5","ap6","ap7","ap8","ap9","ap10","ap11",
"ap12","ap13","ap14","ap15")])
cor.matrix.1
library(psych)
# Script for PCA with VARIMAX rotation (an orthogonal rotation strategy).
pca.2 <- principal(r = cor.matrix.1, nfactors = 25, residuals = FALSE, rotate = "varimax")
pca.2
# Load library for using oblimin rotation.
library(GPArotation)
# Script for PCA with Direct Oblimin rotation (an oblique rotation strategy).
pca.3 <- principal(r = cor.matrix.1, nfactors = 25, residuals = FALSE, rotate = "oblimin")
pca.3
##### FOURTH & FIFTH PCA using Spearman correlations.
# New correlation matrix using Spearman correlation instead of default Pearson.
# Spearman if preferred when using Likert response style items, because the items
# provide nominal or ordinal data rather than interval/ratio data which is required
# for a Pearson correlation.
cor.matrix.2 <- cor(ex5[,c("b1","b2","b3","b4","b5","b6","b7","b8","b9",
"b10","ap1","ap2","ap3","ap4","ap5","ap6","ap7","ap8","ap9","ap10","ap11",
"ap12","ap13","ap14","ap15")], method = "spearman")
cor.matrix.2
# Script for PCA with VARIMAX rotation; with Spearman correlations.
pca.4 <- principal(r = cor.matrix.2, nfactors = 25, residuals = FALSE, rotate = "varimax")
pca.4
# Script for PCA with Direct Oblimin rotation; with Spearman correlations.
pca.5 <- principal(r = cor.matrix.2, nfactors = 25, residuals = FALSE, rotate = "oblimin")
pca.5
# Take a look at the residuals; first use the 'names' function to find the
# the output part of the output we want (residuals; which are not displayed by
# default), then use the '$' convention to reference the residual values.
names(pca.4)
pca.4$residual
head(pca.4$residual)
tail(pca.4$residual)
summary(pca.4$residual)
hist(pca.4$residual)
############ FACTOR ANALYSIS ############
# Using maximum likelihood extraction with the 'factanal' function as would
# be done through the menus of Rcmdr.
##### FIRST Factor Analysis (FA).
# Specifying: rotation 'none' and factors '2'. Automatically suppresses
# loadings less than 0.100 in the output.
fa.1 <- factanal(~ap1+ap2+ap3+ap4+ap5+ap6+ap7+ap8+ap9+ap10+ap11+ap12+ap13+ap14
+ap15+b1+b2+b3+b4+b5+b6+b7+b8+b9+b10,factors=2,
rotation="none", scores="none", data=ex5)
fa.1
##### SECOND FA; specifying: rotation 'varimax' and factors '2'.
fa.2 <- factanal(~ap1+ap2+ap3+ap4+ap5+ap6+ap7+ap8+ap9+ap10+ap11+ap12+ap13+ap14
+ap15+b1+b2+b3+b4+b5+b6+b7+b8+b9+b10,factors=2,
rotation="varimax", scores="none", data=ex5)
fa.2
##### THIRD FA; specifying: rotation 'oblimin' and factors '2'.
fa.3 <- factanal(~ap1+ap2+ap3+ap4+ap5+ap6+ap7+ap8+ap9+ap10+ap11+ap12+ap13+ap14
+ap15+b1+b2+b3+b4+b5+b6+b7+b8+b9+b10,factors=2,
rotation="oblimin", scores="none", data=ex5)
fa.3
##### Saving Thompson's regression style factor scores from "fa.2" above.
fa.4 <- factanal(~ap1+ap2+ap3+ap4+ap5+ap6+ap7+ap8+ap9+ap10+ap11+ap12+ap13+ap14
+ap15+b1+b2+b3+b4+b5+b6+b7+b8+b9+b10,factors=2,
rotation="varimax", scores="regression", data=ex5)
fa.4
# Display just the factor scores, and assign them to an object for later use (if desired).
fa.4$scores
fa4scores <- fa.4$scores
# Display the loadings without suppression.
print(fa.4,digits = 3, cutoff = .000001)
##### Using polycor/hetcor
library(polycor)
# Create the correlation matrix.
h.cor <- hetcor(ex5)$cor
h.cor
# Run the factor analysis using the (hetergeneous) correlation matrix.
fa.5 <- factanal(covmat = h.cor, factors=2, rotation="varimax", scores="none")
fa.5
############ Total Scale Score Correlations ############
# Checking the correlation between each scale's total score.
cor(bt,apt)
cor(bt,apt,method="spearman")
# With correlations so low (r = -0.04); it would be more appropriate to use
# an orthogonal rotation strategy (such as Varimax) rather than an oblique
# rotation strategy (such as Direct Oblimin).
##############################################################################
############ Scale Reliability/Internal Consistency ############
# Keep in mind, Cronbach's Alpha coefficient is one of those statistics that
# just seems to continue hanging around long after it is recognized as being
# not terribly useful. A much better metric for conveying the reliability is
# the omega coefficient (covered below).
# Reliability coefficient (Cronbach's Alpha) for the AP scale.
reliability(cov(ex5[,c("ap1","ap2","ap3","ap4","ap5","ap6","ap7","ap8",
"ap9","ap10","ap11","ap12","ap13","ap14","ap15")], use="complete.obs"))
# Reliability coefficient (Cronbach's Alpha) for the B scale.
reliability(cov(ex5[,c("b1","b2","b3","b4","b5","b6","b7","b8","b9","b10")],
use="complete.obs"))
### Another (easier) way to get Cronbach's Alpha (here on all the items & total scores together):
library(psy)
cronbach(ex5)
###### USE OMEGA RATHER THAN ALPHA.
# The omega coefficient is a much better estimate than alpha for estimating
# the reliability of a scale. For a brief discussion of this issue, please
# read the RSS Matters article from June 2012.
# http://web3.unt.edu/benchmarks/issues/2012/06/rss-matters
# Using a different data set and the 'psych' package, we can easily compute the
# omega coefficient.
data.df <- read.table("http://www.unt.edu/rss/class/Jon/R_SC/Module7/bifactor_data.txt",
header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
summary(data.df)
nrow(data.df)
sem.cor <- cor(data.df)
o1 <- omega(sem.cor, nfactors = 3, n.iter = 1, fm = "ml", poly = F, digits = 3,
n.obs = 5000, rotate = "oblimin")
o1
help(omega)
###############################################################################
# As is the case with most things in R; there are a great number of ways to do
# factor analysis. So, please do not consider this script an exhaustive review
# of factor analytic methods available in R.
#
# For those interested in moving beyond Classical Test Theory construction and
# analysis; you can research Item Response Theory (IRT) analysis in R using
# the packages/libraries 'irtProb' & 'irtoys'. See http://cran.r-project.org/
# and then 'Packages' to find documentation on those two packages/libraries.
#
# Last updated (omega) June 15, 2012.