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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_factor_analysisdm.wasp
Title produced by softwareFactor Analysis
Date of computationMon, 21 May 2012 10:58:21 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/May/21/t13376123101t0dgf50ntg3lmn.htm/, Retrieved Wed, 11 Feb 2026 09:27:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=166965, Retrieved Wed, 11 Feb 2026 09:27:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact268

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2012-05-01 12:45:50] [0cacbd6f25ea662f229a505efea21410]
- RM    [Hierarchical Clustering] [] [2012-05-01 16:25:01] [0cacbd6f25ea662f229a505efea21410]
- R P     [Hierarchical Clustering] [] [2012-05-21 14:20:14] [0cacbd6f25ea662f229a505efea21410]
- RMP       [Factor Analysis] [] [2012-05-21 14:52:54] [0cacbd6f25ea662f229a505efea21410]
- R P           [Factor Analysis] [] [2012-05-21 14:58:21] [46e17293cd0520480fa187e99449b207] [Current]
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Dataset

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166965&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166965&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166965&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Rotated Factor Loadings
VariablesFactor1Factor2Factor3
BC0.2370.4350.614
NNZFG0.0340.0740.86
MRT0.168-0.0360.634
AFL0.9330.1240.088
LPM0.9460.1910.101
LPC0.8130.0970.269
W0.1150.8370.135
WPA0.1460.852-0.007

\begin{tabular}{lllllllll}
\hline
Rotated Factor Loadings \tabularnewline
Variables & Factor1 & Factor2 & Factor3 \tabularnewline
BC & 0.237 & 0.435 & 0.614 \tabularnewline
NNZFG & 0.034 & 0.074 & 0.86 \tabularnewline
MRT & 0.168 & -0.036 & 0.634 \tabularnewline
AFL & 0.933 & 0.124 & 0.088 \tabularnewline
LPM & 0.946 & 0.191 & 0.101 \tabularnewline
LPC & 0.813 & 0.097 & 0.269 \tabularnewline
W & 0.115 & 0.837 & 0.135 \tabularnewline
WPA & 0.146 & 0.852 & -0.007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166965&T=1

[TABLE]
[ROW][C]Rotated Factor Loadings[/C][/ROW]
[ROW][C]Variables[/C][C]Factor1[/C][C]Factor2[/C][C]Factor3[/C][/ROW]
[ROW][C]BC[/C][C]0.237[/C][C]0.435[/C][C]0.614[/C][/ROW]
[ROW][C]NNZFG[/C][C]0.034[/C][C]0.074[/C][C]0.86[/C][/ROW]
[ROW][C]MRT[/C][C]0.168[/C][C]-0.036[/C][C]0.634[/C][/ROW]
[ROW][C]AFL[/C][C]0.933[/C][C]0.124[/C][C]0.088[/C][/ROW]
[ROW][C]LPM[/C][C]0.946[/C][C]0.191[/C][C]0.101[/C][/ROW]
[ROW][C]LPC[/C][C]0.813[/C][C]0.097[/C][C]0.269[/C][/ROW]
[ROW][C]W[/C][C]0.115[/C][C]0.837[/C][C]0.135[/C][/ROW]
[ROW][C]WPA[/C][C]0.146[/C][C]0.852[/C][C]-0.007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166965&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166965&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Rotated Factor Loadings
VariablesFactor1Factor2Factor3
BC0.2370.4350.614
NNZFG0.0340.0740.86
MRT0.168-0.0360.634
AFL0.9330.1240.088
LPM0.9460.1910.101
LPC0.8130.0970.269
W0.1150.8370.135
WPA0.1460.852-0.007


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = all ; par3 = all ; par4 = all ; par5 = Learning Activities ;
Parameters (R input):
par1 = 3 ; par2 = all ; par3 = all ; par4 = all ; par5 = Learning Activities ;
R code (references can be found in the software module):
library(psych)
x <- as.data.frame(read.table(file='https://automated.biganalytics.eu/download/utaut.csv',sep=',',header=T))
x$U25 <- 6-x$U25
if(par2 == 'female') x <- x[x$Gender==0,]
if(par2 == 'male') x <- x[x$Gender==1,]
if(par3 == 'prep') x <- x[x$Pop==1,]
if(par3 == 'bachelor') x <- x[x$Pop==0,]
if(par4 != 'all') {
x <- x[x$Year==as.numeric(par4),]
}
cAc <- with(x,cbind( A1, A2, A3, A4, A5, A6, A7, A8, A9,A10))
cAs <- with(x,cbind(A11,A12,A13,A14,A15,A16,A17,A18,A19,A20))
cA <- cbind(cAc,cAs)
cCa <- with(x,cbind(C1,C3,C5,C7, C9,C11,C13,C15,C17,C19,C21,C23,C25,C27,C29,C31,C33,C35,C37,C39,C41,C43,C45,C47))
cCp <- with(x,cbind(C2,C4,C6,C8,C10,C12,C14,C16,C18,C20,C22,C24,C26,C28,C30,C32,C34,C36,C38,C40,C42,C44,C46,C48))
cC <- cbind(cCa,cCp)
cU <- with(x,cbind(U1,U2,U3,U4,U5,U6,U7,U8,U9,U10,U11,U12,U13,U14,U15,U16,U17,U18,U19,U20,U21,U22,U23,U24,U25,U26,U27,U28,U29,U30,U31,U32,U33))
cE <- with(x,cbind(BC,NNZFG,MRT,AFL,LPM,LPC,W,WPA))
cX <- with(x,cbind(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18))
if (par5=='ATTLES connected') x <- cAc
if (par5=='ATTLES separate') x <- cAs
if (par5=='ATTLES all') x <- cA
if (par5=='COLLES actuals') x <- cCa
if (par5=='COLLES preferred') x <- cCp
if (par5=='COLLES all') x <- cC
if (par5=='CSUQ') x <- cU
if (par5=='Learning Activities') x <- cE
if (par5=='Exam Items') x <- cX
ncol <- length(x[1,])
for (jjj in 1:ncol) {
x <- x[!is.na(x[,jjj]),]
}
par1 <- as.numeric(par1)
nrows <- length(x[,1])
rownames(x) <- 1:nrows
y <- x
fit <- principal(y, nfactors=par1, rotate='varimax')
fit
fs <- factor.scores(y,fit)
fs
bitmap(file='test1.png')
fa.diagram(fit)
dev.off()
bitmap(file='test2.png')
plot(fs,pch=20)
text(fs,labels=rownames(y),pos=3)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Rotated Factor Loadings',par1+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variables',1,TRUE)
for (i in 1:par1) {
a<-table.element(a,paste('Factor',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (j in 1:length(fit$loadings[,1])) {
a<-table.row.start(a)
a<-table.element(a,rownames(fit$loadings)[j],header=TRUE)
for (i in 1:par1) {
a<-table.element(a,round(fit$loadings[j,i],3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')