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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 computationTue, 01 May 2012 07:45:53 -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/01/t13358730695nlfh5f5suuvlji.htm/, Retrieved Sun, 17 May 2026 21:32:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=165473, Retrieved Sun, 17 May 2026 21:32:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact290

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Notched Boxplots] [Notched Boxplots] [2012-05-01 09:51:24] [5c12c14850e1dddd68cd7e26a7cf987c]
- RMP   [Hierarchical Clustering] [Workshop 5] [2012-05-01 10:50:18] [5c12c14850e1dddd68cd7e26a7cf987c]
- RMP       [Factor Analysis] [Workshop 5] [2012-05-01 11:45:53] [12a6074303e7dbf450a4f3ff6a9ce824] [Current]
-   P         [Factor Analysis] [Workshop 5] [2012-05-01 12:11:30] [5c12c14850e1dddd68cd7e26a7cf987c]
- RMP         [Social Networking] [Workshop 5] [2012-05-01 12:40:14] [5c12c14850e1dddd68cd7e26a7cf987c]
- RM            [Social Networking] [Workshop 5] [2012-05-01 13:02:18] [5c12c14850e1dddd68cd7e26a7cf987c]
- RM            [Recursive Partitioning (Regression Trees)] [Workshop 5] [2012-05-01 13:14:31] [5c12c14850e1dddd68cd7e26a7cf987c]
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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 time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=165473&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=165473&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=165473&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 time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Rotated Factor Loadings
VariablesFactor1Factor2
A10.563-0.106
A2-0.1260.726
A30.6450.018
A40.2380.449
A50.4950.114
A60.6330.099
A70.2770.5
A80.5850.325
A90.0090.691
A100.5220.149

\begin{tabular}{lllllllll}
\hline
Rotated Factor Loadings \tabularnewline
Variables & Factor1 & Factor2 \tabularnewline
A1 & 0.563 & -0.106 \tabularnewline
A2 & -0.126 & 0.726 \tabularnewline
A3 & 0.645 & 0.018 \tabularnewline
A4 & 0.238 & 0.449 \tabularnewline
A5 & 0.495 & 0.114 \tabularnewline
A6 & 0.633 & 0.099 \tabularnewline
A7 & 0.277 & 0.5 \tabularnewline
A8 & 0.585 & 0.325 \tabularnewline
A9 & 0.009 & 0.691 \tabularnewline
A10 & 0.522 & 0.149 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=165473&T=1

[TABLE]
[ROW][C]Rotated Factor Loadings[/C][/ROW]
[ROW][C]Variables[/C][C]Factor1[/C][C]Factor2[/C][/ROW]
[ROW][C]A1[/C][C]0.563[/C][C]-0.106[/C][/ROW]
[ROW][C]A2[/C][C]-0.126[/C][C]0.726[/C][/ROW]
[ROW][C]A3[/C][C]0.645[/C][C]0.018[/C][/ROW]
[ROW][C]A4[/C][C]0.238[/C][C]0.449[/C][/ROW]
[ROW][C]A5[/C][C]0.495[/C][C]0.114[/C][/ROW]
[ROW][C]A6[/C][C]0.633[/C][C]0.099[/C][/ROW]
[ROW][C]A7[/C][C]0.277[/C][C]0.5[/C][/ROW]
[ROW][C]A8[/C][C]0.585[/C][C]0.325[/C][/ROW]
[ROW][C]A9[/C][C]0.009[/C][C]0.691[/C][/ROW]
[ROW][C]A10[/C][C]0.522[/C][C]0.149[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=165473&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=165473&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
VariablesFactor1Factor2
A10.563-0.106
A2-0.1260.726
A30.6450.018
A40.2380.449
A50.4950.114
A60.6330.099
A70.2770.5
A80.5850.325
A90.0090.691
A100.5220.149


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = all ; par3 = all ; par4 = all ; par5 = ATTLES connected ;
Parameters (R input):
par1 = 2 ; par2 = all ; par3 = all ; par4 = all ; par5 = ATTLES connected ;
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')