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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_factor_analysisdm.wasp

Title produced by software

Factor Analysis

Date of computation

Fri, 18 May 2012 13:21:32 -0400

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/May/18/t13373617212o5w8rkz8sijxpf.htm/, Retrieved Fri, 03 May 2024 21:56:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=166690, Retrieved Fri, 03 May 2024 21:56:45 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

138

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Factor Analysis] [Factor Analysis] [2012-05-18 17:21:32] [63b8a9573feb7e5c5af439dd6e45c15a] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'AstonUniversity' @ aston.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 & 3 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166690&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166690&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166690&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Rotated Factor Loadings
Variables	Factor1	Factor2
C1	-0.094	0.558
C3	-0.023	0.589
C5	-0.002	0.628
C7	-0.024	0.603
C9	0.262	0.498
C11	0.238	0.493
C13	0.345	0.42
C15	0.307	0.456
C17	0.277	0.475
C19	0.264	0.39
C21	0.16	0.516
C23	0.139	0.564
C25	0.292	0.568
C27	0.343	0.421
C29	0.254	0.463
C31	0.466	0.389
C33	0.031	0.678
C35	0.011	0.678
C37	0.002	0.65
C39	-0.01	0.437
C41	0.067	0.563
C43	0.072	0.608
C45	0.142	0.522
C47	0.17	0.532
C2	0.557	0.143
C4	0.652	0.016
C6	0.704	-0.021
C8	0.633	-0.064
C10	0.635	-0.004
C12	0.636	0.03
C14	0.521	0.19
C16	0.581	0.064
C18	0.446	0.24
C20	0.518	0.148
C22	0.472	0.164
C24	0.61	0.096
C26	0.678	0.038
C28	0.537	0.208
C30	0.628	0.105
C32	0.558	0.122
C34	0.51	0.221
C36	0.477	0.189
C38	0.479	0.121
C40	0.38	0.047
C42	0.585	0.106
C44	0.636	0.024
C46	0.633	0.077
C48	0.514	0.174

\begin{tabular}{lllllllll}
\hline
Rotated Factor Loadings \tabularnewline
Variables & Factor1 & Factor2 \tabularnewline
C1 & -0.094 & 0.558 \tabularnewline
C3 & -0.023 & 0.589 \tabularnewline
C5 & -0.002 & 0.628 \tabularnewline
C7 & -0.024 & 0.603 \tabularnewline
C9 & 0.262 & 0.498 \tabularnewline
C11 & 0.238 & 0.493 \tabularnewline
C13 & 0.345 & 0.42 \tabularnewline
C15 & 0.307 & 0.456 \tabularnewline
C17 & 0.277 & 0.475 \tabularnewline
C19 & 0.264 & 0.39 \tabularnewline
C21 & 0.16 & 0.516 \tabularnewline
C23 & 0.139 & 0.564 \tabularnewline
C25 & 0.292 & 0.568 \tabularnewline
C27 & 0.343 & 0.421 \tabularnewline
C29 & 0.254 & 0.463 \tabularnewline
C31 & 0.466 & 0.389 \tabularnewline
C33 & 0.031 & 0.678 \tabularnewline
C35 & 0.011 & 0.678 \tabularnewline
C37 & 0.002 & 0.65 \tabularnewline
C39 & -0.01 & 0.437 \tabularnewline
C41 & 0.067 & 0.563 \tabularnewline
C43 & 0.072 & 0.608 \tabularnewline
C45 & 0.142 & 0.522 \tabularnewline
C47 & 0.17 & 0.532 \tabularnewline
C2 & 0.557 & 0.143 \tabularnewline
C4 & 0.652 & 0.016 \tabularnewline
C6 & 0.704 & -0.021 \tabularnewline
C8 & 0.633 & -0.064 \tabularnewline
C10 & 0.635 & -0.004 \tabularnewline
C12 & 0.636 & 0.03 \tabularnewline
C14 & 0.521 & 0.19 \tabularnewline
C16 & 0.581 & 0.064 \tabularnewline
C18 & 0.446 & 0.24 \tabularnewline
C20 & 0.518 & 0.148 \tabularnewline
C22 & 0.472 & 0.164 \tabularnewline
C24 & 0.61 & 0.096 \tabularnewline
C26 & 0.678 & 0.038 \tabularnewline
C28 & 0.537 & 0.208 \tabularnewline
C30 & 0.628 & 0.105 \tabularnewline
C32 & 0.558 & 0.122 \tabularnewline
C34 & 0.51 & 0.221 \tabularnewline
C36 & 0.477 & 0.189 \tabularnewline
C38 & 0.479 & 0.121 \tabularnewline
C40 & 0.38 & 0.047 \tabularnewline
C42 & 0.585 & 0.106 \tabularnewline
C44 & 0.636 & 0.024 \tabularnewline
C46 & 0.633 & 0.077 \tabularnewline
C48 & 0.514 & 0.174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166690&T=1

[TABLE]
[ROW][C]Rotated Factor Loadings[/C][/ROW]
[ROW][C]Variables[/C][C]Factor1[/C][C]Factor2[/C][/ROW]
[ROW][C]C1[/C][C]-0.094[/C][C]0.558[/C][/ROW]
[ROW][C]C3[/C][C]-0.023[/C][C]0.589[/C][/ROW]
[ROW][C]C5[/C][C]-0.002[/C][C]0.628[/C][/ROW]
[ROW][C]C7[/C][C]-0.024[/C][C]0.603[/C][/ROW]
[ROW][C]C9[/C][C]0.262[/C][C]0.498[/C][/ROW]
[ROW][C]C11[/C][C]0.238[/C][C]0.493[/C][/ROW]
[ROW][C]C13[/C][C]0.345[/C][C]0.42[/C][/ROW]
[ROW][C]C15[/C][C]0.307[/C][C]0.456[/C][/ROW]
[ROW][C]C17[/C][C]0.277[/C][C]0.475[/C][/ROW]
[ROW][C]C19[/C][C]0.264[/C][C]0.39[/C][/ROW]
[ROW][C]C21[/C][C]0.16[/C][C]0.516[/C][/ROW]
[ROW][C]C23[/C][C]0.139[/C][C]0.564[/C][/ROW]
[ROW][C]C25[/C][C]0.292[/C][C]0.568[/C][/ROW]
[ROW][C]C27[/C][C]0.343[/C][C]0.421[/C][/ROW]
[ROW][C]C29[/C][C]0.254[/C][C]0.463[/C][/ROW]
[ROW][C]C31[/C][C]0.466[/C][C]0.389[/C][/ROW]
[ROW][C]C33[/C][C]0.031[/C][C]0.678[/C][/ROW]
[ROW][C]C35[/C][C]0.011[/C][C]0.678[/C][/ROW]
[ROW][C]C37[/C][C]0.002[/C][C]0.65[/C][/ROW]
[ROW][C]C39[/C][C]-0.01[/C][C]0.437[/C][/ROW]
[ROW][C]C41[/C][C]0.067[/C][C]0.563[/C][/ROW]
[ROW][C]C43[/C][C]0.072[/C][C]0.608[/C][/ROW]
[ROW][C]C45[/C][C]0.142[/C][C]0.522[/C][/ROW]
[ROW][C]C47[/C][C]0.17[/C][C]0.532[/C][/ROW]
[ROW][C]C2[/C][C]0.557[/C][C]0.143[/C][/ROW]
[ROW][C]C4[/C][C]0.652[/C][C]0.016[/C][/ROW]
[ROW][C]C6[/C][C]0.704[/C][C]-0.021[/C][/ROW]
[ROW][C]C8[/C][C]0.633[/C][C]-0.064[/C][/ROW]
[ROW][C]C10[/C][C]0.635[/C][C]-0.004[/C][/ROW]
[ROW][C]C12[/C][C]0.636[/C][C]0.03[/C][/ROW]
[ROW][C]C14[/C][C]0.521[/C][C]0.19[/C][/ROW]
[ROW][C]C16[/C][C]0.581[/C][C]0.064[/C][/ROW]
[ROW][C]C18[/C][C]0.446[/C][C]0.24[/C][/ROW]
[ROW][C]C20[/C][C]0.518[/C][C]0.148[/C][/ROW]
[ROW][C]C22[/C][C]0.472[/C][C]0.164[/C][/ROW]
[ROW][C]C24[/C][C]0.61[/C][C]0.096[/C][/ROW]
[ROW][C]C26[/C][C]0.678[/C][C]0.038[/C][/ROW]
[ROW][C]C28[/C][C]0.537[/C][C]0.208[/C][/ROW]
[ROW][C]C30[/C][C]0.628[/C][C]0.105[/C][/ROW]
[ROW][C]C32[/C][C]0.558[/C][C]0.122[/C][/ROW]
[ROW][C]C34[/C][C]0.51[/C][C]0.221[/C][/ROW]
[ROW][C]C36[/C][C]0.477[/C][C]0.189[/C][/ROW]
[ROW][C]C38[/C][C]0.479[/C][C]0.121[/C][/ROW]
[ROW][C]C40[/C][C]0.38[/C][C]0.047[/C][/ROW]
[ROW][C]C42[/C][C]0.585[/C][C]0.106[/C][/ROW]
[ROW][C]C44[/C][C]0.636[/C][C]0.024[/C][/ROW]
[ROW][C]C46[/C][C]0.633[/C][C]0.077[/C][/ROW]
[ROW][C]C48[/C][C]0.514[/C][C]0.174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166690&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166690&T=1

As an alternative you can also use a QR Code:

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

Rotated Factor Loadings
Variables	Factor1	Factor2
C1	-0.094	0.558
C3	-0.023	0.589
C5	-0.002	0.628
C7	-0.024	0.603
C9	0.262	0.498
C11	0.238	0.493
C13	0.345	0.42
C15	0.307	0.456
C17	0.277	0.475
C19	0.264	0.39
C21	0.16	0.516
C23	0.139	0.564
C25	0.292	0.568
C27	0.343	0.421
C29	0.254	0.463
C31	0.466	0.389
C33	0.031	0.678
C35	0.011	0.678
C37	0.002	0.65
C39	-0.01	0.437
C41	0.067	0.563
C43	0.072	0.608
C45	0.142	0.522
C47	0.17	0.532
C2	0.557	0.143
C4	0.652	0.016
C6	0.704	-0.021
C8	0.633	-0.064
C10	0.635	-0.004
C12	0.636	0.03
C14	0.521	0.19
C16	0.581	0.064
C18	0.446	0.24
C20	0.518	0.148
C22	0.472	0.164
C24	0.61	0.096
C26	0.678	0.038
C28	0.537	0.208
C30	0.628	0.105
C32	0.558	0.122
C34	0.51	0.221
C36	0.477	0.189
C38	0.479	0.121
C40	0.38	0.047
C42	0.585	0.106
C44	0.636	0.024
C46	0.633	0.077
C48	0.514	0.174

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 2 ; par2 = all ; par3 = bachelor ; par4 = all ; par5 = COLLES all ;

Parameters (R input):

par1 = 2 ; par2 = all ; par3 = bachelor ; par4 = all ; par5 = COLLES all ;

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code