Exploratory Factor Analysis

[Maryam Mushtaq]

Hi,

I'm using exploratory factor analysis on a 5 point Likert Scale. The problem I'm facing is that the questions in my questionnaire are less (4 questions), so after conducting the analysis, I end up with 2 questions only. To retain the factors, I am considering a cut off of 0.3 and 0.4 for factor loadings and communality respectively. Also, if I calculate the cronbach's alpha for the 2 questions I get, it is above 0.7 but if I consider all 4, the alpha is between 0.4 and 0.5. What should I do? I read somewhere that the construct validity is affected by deleting the items.

Also, since I'm doing an exploratory research, is it okay to use a chronbach's alpha value between 0.4 and 0.5?

[Phil Bromiley]

Welcome to Statalist. I really don't understand your problem, which may be why you didn't get a quick answer. You'll increase your chances of a useful answer by following the FAQ on asking questions - provide Stata code in code delimiters, readable Stata output, and sample data using dataex.

So, you have a questionnaire with only 4 questions all of which are on 5-point likert scales? I don't understand why factor analysis would eliminate questions per se. Are you saying you only get two extracted factors? [It is absolutely essential that you use the correct terminology so we understand what you are saying.]

Try to repost following the FAQ and rewriting your posting to be clearer.

[Weiwen Ng]

A side note: if you have a scale that was designed to measure one construct, then I'd argue that you are wasting your time with EFA. You'd be justified in moving to CFA.

That said, if you're saying that each question only has a loading of around 0.3 on its primary factor, then it sounds like you have a very poor scale to begin with. 0.5 is not really a good value for Cronbach's Alpha.

[Anand Sunny]

I have a doubt on using Exploratory Factor Analysis. In every references I read EFA is used for ordinal variables or likert scale variables. My question is can EFA be used on nominal categorical variables?

[Joseph Coveney]

MPlus won't let you. Maybe they know something.

Anyway, take look at the following naïve sortie.

.ÿ
.ÿversionÿ16.1

.ÿ
.ÿclearÿ*

.ÿ
.ÿsetÿseedÿ`=strreverse("1599137")'

.ÿ
.ÿquietlyÿsetÿobsÿ500

.ÿ
.ÿforvaluesÿfactorÿ=ÿ1/2ÿ{
ÿÿ2.ÿ
.ÿÿÿÿÿÿÿÿÿgenerateÿdoubleÿfÿ=ÿrnormal()
ÿÿ3.ÿÿÿÿÿÿÿÿÿgenerateÿdoubleÿxbÿ=ÿfÿ+ÿ2ÿ*ÿln(1.25)
ÿÿ4.ÿÿÿÿÿÿÿÿÿgenerateÿdoubleÿp1ÿ=ÿ1ÿ/ÿ(1ÿ+ÿexp(xb)ÿ+ÿexp(xb))
ÿÿ5.ÿÿÿÿÿÿÿÿÿgenerateÿdoubleÿp2ÿ=ÿexp(xb)ÿ/ÿ(1ÿ+ÿexp(xb)ÿ+ÿexp(xb))
ÿÿ6.ÿ
.ÿÿÿÿÿÿÿÿÿforvaluesÿiÿ=ÿ1/6ÿ{
ÿÿ7.ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿgenerateÿdoubleÿuÿ=ÿruniform()
ÿÿ8.ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿlocalÿitemÿ=ÿ6ÿ*ÿ(`factor'ÿ-ÿ1)ÿ+ÿ`i'
ÿÿ9.ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿgenerateÿbyteÿy`item'ÿ=ÿcond(uÿ<ÿp1,ÿ1,ÿ(cond(uÿ<ÿp1ÿ+ÿp2,ÿ2,ÿ3)))
ÿ10.ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿdropÿu
ÿ11.ÿÿÿÿÿÿÿÿÿ}
ÿ12.ÿ
.ÿÿÿÿÿÿÿÿÿdropÿfÿxbÿp1ÿp2
ÿ13.ÿ}

.ÿ
.ÿ*
.ÿ*ÿBeginÿhere
.ÿ*
.ÿgsemÿ///
>ÿÿÿÿÿÿÿÿÿ(y1ÿy2ÿy3ÿy4ÿy5ÿy6ÿy7@0ÿy8ÿy9ÿy10ÿy11ÿy12ÿ<-ÿF1,ÿmlogit)ÿ///
>ÿÿÿÿÿÿÿÿÿ(y1@0ÿy2ÿy3ÿy4ÿy5ÿy6ÿy7ÿy8ÿy9ÿy10ÿy11ÿy12ÿ<-ÿF2,ÿmlogit),ÿ///
>ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿvariance(F1@1ÿF2@1)ÿcovariance(F1*F2)ÿ///
>ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿdifficultÿnocnsreportÿnodvheader

Fittingÿfixed-effectsÿmodel:

Iterationÿ0:ÿÿÿlogÿlikelihoodÿ=ÿ-6548.3658ÿÿ
Iterationÿ1:ÿÿÿlogÿlikelihoodÿ=ÿ-6548.3658ÿÿ

Refiningÿstartingÿvalues:

Gridÿnodeÿ0:ÿÿÿlogÿlikelihoodÿ=ÿ-6543.8038

Fittingÿfullÿmodel:

Iterationÿ0:ÿÿÿlogÿlikelihoodÿ=ÿ-6543.8038ÿÿ(notÿconcave)
Iterationÿ1:ÿÿÿlogÿlikelihoodÿ=ÿÿ-6515.666ÿÿ(notÿconcave)
Iterationÿ2:ÿÿÿlogÿlikelihoodÿ=ÿ-6478.6934ÿÿ(notÿconcave)
Iterationÿ3:ÿÿÿlogÿlikelihoodÿ=ÿ-6460.6929ÿÿ(notÿconcave)
Iterationÿ4:ÿÿÿlogÿlikelihoodÿ=ÿ-6441.0513ÿÿ(notÿconcave)
Iterationÿ5:ÿÿÿlogÿlikelihoodÿ=ÿ-6418.3837ÿÿ(notÿconcave)
Iterationÿ6:ÿÿÿlogÿlikelihoodÿ=ÿ-6407.0985ÿÿ(notÿconcave)
Iterationÿ7:ÿÿÿlogÿlikelihoodÿ=ÿ-6399.6901ÿÿ
Iterationÿ8:ÿÿÿlogÿlikelihoodÿ=ÿ-6399.0225ÿÿ(backedÿup)
Iterationÿ9:ÿÿÿlogÿlikelihoodÿ=ÿ-6395.9812ÿÿ
Iterationÿ10:ÿÿlogÿlikelihoodÿ=ÿ-6395.1558ÿÿ
Iterationÿ11:ÿÿlogÿlikelihoodÿ=ÿÿ-6395.122ÿÿ
Iterationÿ12:ÿÿlogÿlikelihoodÿ=ÿ-6395.1218ÿÿ

GeneralizedÿstructuralÿequationÿmodelÿÿÿÿÿÿÿÿÿÿÿNumberÿofÿobsÿÿÿÿÿ=ÿÿÿÿÿÿÿÿ500
Logÿlikelihoodÿ=ÿ-6395.1218

------------------------------------------------------------------------------
ÿÿÿÿÿÿÿÿÿÿÿÿÿ|ÿÿÿÿÿÿCoef.ÿÿÿStd.ÿErr.ÿÿÿÿÿÿzÿÿÿÿP>|z|ÿÿÿÿÿ[95%ÿConf.ÿInterval]
-------------+----------------------------------------------------------------
1.y1ÿÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y1ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿÿ1.02307ÿÿÿ.2189883ÿÿÿÿÿ4.67ÿÿÿ0.000ÿÿÿÿÿ.5938608ÿÿÿÿ1.452279
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿÿÿÿÿÿÿÿÿ0ÿÿ(omitted)
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.3717671ÿÿÿ.1470239ÿÿÿÿÿ2.53ÿÿÿ0.011ÿÿÿÿÿ.0836055ÿÿÿÿ.6599288
-------------+----------------------------------------------------------------
3.y1ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.9403023ÿÿÿ.2139659ÿÿÿÿÿ4.39ÿÿÿ0.000ÿÿÿÿÿ.5209369ÿÿÿÿ1.359668
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿÿÿÿÿÿÿÿÿ0ÿÿ(omitted)
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.4913014ÿÿÿ.1431567ÿÿÿÿÿ3.43ÿÿÿ0.001ÿÿÿÿÿ.2107193ÿÿÿÿ.7718834
-------------+----------------------------------------------------------------
1.y2ÿÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y2ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ1.185725ÿÿÿ.2666155ÿÿÿÿÿ4.45ÿÿÿ0.000ÿÿÿÿÿÿ.663168ÿÿÿÿ1.708281
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.1011826ÿÿÿ.2836225ÿÿÿÿ-0.36ÿÿÿ0.721ÿÿÿÿ-.6570725ÿÿÿÿ.4547073
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.5727492ÿÿÿ.1534799ÿÿÿÿÿ3.73ÿÿÿ0.000ÿÿÿÿÿ.2719342ÿÿÿÿ.8735642
-------------+----------------------------------------------------------------
3.y2ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ1.045324ÿÿÿ.2614398ÿÿÿÿÿ4.00ÿÿÿ0.000ÿÿÿÿÿ.5329116ÿÿÿÿ1.557737
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.1137081ÿÿÿ.2759412ÿÿÿÿ-0.41ÿÿÿ0.680ÿÿÿÿÿ-.654543ÿÿÿÿ.4271268
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.4528805ÿÿÿ.1546731ÿÿÿÿÿ2.93ÿÿÿ0.003ÿÿÿÿÿ.1497269ÿÿÿÿ.7560342
-------------+----------------------------------------------------------------
1.y3ÿÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y3ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.8093333ÿÿÿ.2313031ÿÿÿÿÿ3.50ÿÿÿ0.000ÿÿÿÿÿ.3559876ÿÿÿÿ1.262679
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿÿÿ.035278ÿÿÿ.2424523ÿÿÿÿÿ0.15ÿÿÿ0.884ÿÿÿÿ-.4399197ÿÿÿÿ.5104757
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.5192775ÿÿÿÿ.144392ÿÿÿÿÿ3.60ÿÿÿ0.000ÿÿÿÿÿ.2362743ÿÿÿÿ.8022808
-------------+----------------------------------------------------------------
3.y3ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ1.115718ÿÿÿ.2549892ÿÿÿÿÿ4.38ÿÿÿ0.000ÿÿÿÿÿ.6159484ÿÿÿÿ1.615488
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿÿ.0099192ÿÿÿ.2688366ÿÿÿÿÿ0.04ÿÿÿ0.971ÿÿÿÿ-.5169908ÿÿÿÿ.5368292
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.4369933ÿÿÿÿ.149058ÿÿÿÿÿ2.93ÿÿÿ0.003ÿÿÿÿÿ.1448451ÿÿÿÿ.7291415
-------------+----------------------------------------------------------------
1.y4ÿÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y4ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.6775989ÿÿÿ.2250897ÿÿÿÿÿ3.01ÿÿÿ0.003ÿÿÿÿÿ.2364311ÿÿÿÿ1.118767
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿÿ.0529742ÿÿÿ.2260317ÿÿÿÿÿ0.23ÿÿÿ0.815ÿÿÿÿ-.3900397ÿÿÿÿ.4959882
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.1631845ÿÿÿ.1358936ÿÿÿÿÿ1.20ÿÿÿ0.230ÿÿÿÿÿ-.103162ÿÿÿÿÿ.429531
-------------+----------------------------------------------------------------
3.y4ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ1.003615ÿÿÿÿ.237794ÿÿÿÿÿ4.22ÿÿÿ0.000ÿÿÿÿÿ.5375476ÿÿÿÿ1.469683
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.3300879ÿÿÿ.2655188ÿÿÿÿ-1.24ÿÿÿ0.214ÿÿÿÿ-.8504951ÿÿÿÿ.1903194
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.3754924ÿÿÿ.1325061ÿÿÿÿÿ2.83ÿÿÿ0.005ÿÿÿÿÿ.1157853ÿÿÿÿ.6351995
-------------+----------------------------------------------------------------
1.y5ÿÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y5ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ1.670547ÿÿÿÿ.371996ÿÿÿÿÿ4.49ÿÿÿ0.000ÿÿÿÿÿ.9414484ÿÿÿÿ2.399646
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.2656408ÿÿÿÿ.360449ÿÿÿÿ-0.74ÿÿÿ0.461ÿÿÿÿ-.9721078ÿÿÿÿ.4408263
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.4420706ÿÿÿ.1874183ÿÿÿÿÿ2.36ÿÿÿ0.018ÿÿÿÿÿ.0747374ÿÿÿÿ.8094038
-------------+----------------------------------------------------------------
3.y5ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ1.496031ÿÿÿ.3558567ÿÿÿÿÿ4.20ÿÿÿ0.000ÿÿÿÿÿ.7985646ÿÿÿÿ2.193497
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.3000823ÿÿÿ.3374749ÿÿÿÿ-0.89ÿÿÿ0.374ÿÿÿÿ-.9615209ÿÿÿÿ.3613562
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.5799403ÿÿÿ.1827861ÿÿÿÿÿ3.17ÿÿÿ0.002ÿÿÿÿÿ.2216861ÿÿÿÿ.9381946
-------------+----------------------------------------------------------------
1.y6ÿÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y6ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ1.183436ÿÿÿ.2621509ÿÿÿÿÿ4.51ÿÿÿ0.000ÿÿÿÿÿ.6696294ÿÿÿÿ1.697242
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.2141923ÿÿÿ.2740741ÿÿÿÿ-0.78ÿÿÿ0.435ÿÿÿÿ-.7513676ÿÿÿÿ.3229831
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿÿ.376677ÿÿÿ.1444303ÿÿÿÿÿ2.61ÿÿÿ0.009ÿÿÿÿÿ.0935988ÿÿÿÿ.6597552
-------------+----------------------------------------------------------------
3.y6ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.9694153ÿÿÿ.2535339ÿÿÿÿÿ3.82ÿÿÿ0.000ÿÿÿÿÿÿ.472498ÿÿÿÿ1.466333
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.1962268ÿÿÿ.2540872ÿÿÿÿ-0.77ÿÿÿ0.440ÿÿÿÿ-.6942286ÿÿÿÿÿ.301775
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.2963404ÿÿÿ.1437125ÿÿÿÿÿ2.06ÿÿÿ0.039ÿÿÿÿÿ.0146691ÿÿÿÿ.5780117
-------------+----------------------------------------------------------------
1.y7ÿÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y7ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿÿÿÿÿÿÿÿ0ÿÿ(omitted)
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-1.191631ÿÿÿ.2752597ÿÿÿÿ-4.33ÿÿÿ0.000ÿÿÿÿÿ-1.73113ÿÿÿ-.6521317
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.5466811ÿÿÿ.1720251ÿÿÿÿÿ3.18ÿÿÿ0.001ÿÿÿÿÿ.2095181ÿÿÿÿ.8838441
-------------+----------------------------------------------------------------
3.y7ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿÿÿÿÿÿÿÿ0ÿÿ(omitted)
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-1.304706ÿÿÿ.2825807ÿÿÿÿ-4.62ÿÿÿ0.000ÿÿÿÿ-1.858554ÿÿÿ-.7508583
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.5043192ÿÿÿ.1759137ÿÿÿÿÿ2.87ÿÿÿ0.004ÿÿÿÿÿ.1595347ÿÿÿÿ.8491037
-------------+----------------------------------------------------------------
1.y8ÿÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y8ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.1143209ÿÿÿÿ.226098ÿÿÿÿÿ0.51ÿÿÿ0.613ÿÿÿÿÿ-.328823ÿÿÿÿ.5574648
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-1.015854ÿÿÿ.2448638ÿÿÿÿ-4.15ÿÿÿ0.000ÿÿÿÿ-1.495779ÿÿÿÿÿ-.53593
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.5485492ÿÿÿ.1460614ÿÿÿÿÿ3.76ÿÿÿ0.000ÿÿÿÿÿ.2622741ÿÿÿÿ.8348243
-------------+----------------------------------------------------------------
3.y8ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.1221159ÿÿÿ.2429582ÿÿÿÿÿ0.50ÿÿÿ0.615ÿÿÿÿ-.3540734ÿÿÿÿ.5983052
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-1.162939ÿÿÿ.2708954ÿÿÿÿ-4.29ÿÿÿ0.000ÿÿÿÿ-1.693885ÿÿÿÿ-.631994
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.2875985ÿÿÿ.1524499ÿÿÿÿÿ1.89ÿÿÿ0.059ÿÿÿÿ-.0111978ÿÿÿÿ.5863949
-------------+----------------------------------------------------------------
1.y9ÿÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y9ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.7076047ÿÿÿ.3620767ÿÿÿÿÿ1.95ÿÿÿ0.051ÿÿÿÿ-.0020527ÿÿÿÿ1.417262
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿÿ-1.76664ÿÿÿ.4289424ÿÿÿÿ-4.12ÿÿÿ0.000ÿÿÿÿ-2.607351ÿÿÿ-.9259282
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.4943757ÿÿÿ.1808227ÿÿÿÿÿ2.73ÿÿÿ0.006ÿÿÿÿÿ.1399696ÿÿÿÿ.8487818
-------------+----------------------------------------------------------------
3.y9ÿÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.4664961ÿÿÿ.2974223ÿÿÿÿÿ1.57ÿÿÿ0.117ÿÿÿÿÿ-.116441ÿÿÿÿ1.049433
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-1.353195ÿÿÿ.3560929ÿÿÿÿ-3.80ÿÿÿ0.000ÿÿÿÿ-2.051124ÿÿÿ-.6552658
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.4422925ÿÿÿÿÿ.18538ÿÿÿÿÿ2.39ÿÿÿ0.017ÿÿÿÿÿ.0789544ÿÿÿÿ.8056307
-------------+----------------------------------------------------------------
1.y10ÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y10ÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.0077073ÿÿÿ.2144065ÿÿÿÿÿ0.04ÿÿÿ0.971ÿÿÿÿ-.4125218ÿÿÿÿ.4279363
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.7531672ÿÿÿ.2210235ÿÿÿÿ-3.41ÿÿÿ0.001ÿÿÿÿ-1.186365ÿÿÿ-.3199691
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿÿ.382196ÿÿÿ.1356822ÿÿÿÿÿ2.82ÿÿÿ0.005ÿÿÿÿÿ.1162637ÿÿÿÿ.6481283
-------------+----------------------------------------------------------------
3.y10ÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.2928235ÿÿÿ.2372646ÿÿÿÿÿ1.23ÿÿÿ0.217ÿÿÿÿ-.1722066ÿÿÿÿ.7578536
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.9653018ÿÿÿ.2395377ÿÿÿÿ-4.03ÿÿÿ0.000ÿÿÿÿ-1.434787ÿÿÿ-.4958166
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿÿ.382724ÿÿÿ.1370657ÿÿÿÿÿ2.79ÿÿÿ0.005ÿÿÿÿÿ.1140801ÿÿÿÿ.6513679
-------------+----------------------------------------------------------------
1.y11ÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y11ÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿ-.0793499ÿÿÿÿ.207461ÿÿÿÿ-0.38ÿÿÿ0.702ÿÿÿÿ-.4859661ÿÿÿÿ.3272662
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.7791345ÿÿÿÿ.221766ÿÿÿÿ-3.51ÿÿÿ0.000ÿÿÿÿ-1.213788ÿÿÿÿ-.344481
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.2309923ÿÿÿ.1359175ÿÿÿÿÿ1.70ÿÿÿ0.089ÿÿÿÿ-.0354011ÿÿÿÿ.4973856
-------------+----------------------------------------------------------------
3.y11ÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ-.037212ÿÿÿ.2125374ÿÿÿÿ-0.18ÿÿÿ0.861ÿÿÿÿ-.4537776ÿÿÿÿ.3793536
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.8863697ÿÿÿ.2230929ÿÿÿÿ-3.97ÿÿÿ0.000ÿÿÿÿ-1.323624ÿÿÿ-.4491156
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.3542633ÿÿÿ.1338649ÿÿÿÿÿ2.65ÿÿÿ0.008ÿÿÿÿÿ.0918929ÿÿÿÿ.6166337
-------------+----------------------------------------------------------------
1.y12ÿÿÿÿÿÿÿÿ|ÿÿ(baseÿoutcome)
-------------+----------------------------------------------------------------
2.y12ÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.6108455ÿÿÿ.2584987ÿÿÿÿÿ2.36ÿÿÿ0.018ÿÿÿÿÿ.1041974ÿÿÿÿ1.117494
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-1.019836ÿÿÿÿ.263896ÿÿÿÿ-3.86ÿÿÿ0.000ÿÿÿÿ-1.537062ÿÿÿÿ-.502609
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.4335025ÿÿÿ.1376043ÿÿÿÿÿ3.15ÿÿÿ0.002ÿÿÿÿÿ.1638032ÿÿÿÿ.7032019
-------------+----------------------------------------------------------------
3.y12ÿÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿF1ÿ|ÿÿÿ.4647257ÿÿÿ.2451959ÿÿÿÿÿ1.90ÿÿÿ0.058ÿÿÿÿ-.0158495ÿÿÿÿ.9453009
ÿÿÿÿÿÿÿÿÿÿF2ÿ|ÿÿ-.8925677ÿÿÿ.2592844ÿÿÿÿ-3.44ÿÿÿ0.001ÿÿÿÿ-1.400756ÿÿÿ-.3843795
ÿÿÿÿÿÿÿ_consÿ|ÿÿÿ.2525869ÿÿÿ.1406466ÿÿÿÿÿ1.80ÿÿÿ0.073ÿÿÿÿ-.0230753ÿÿÿÿ.5282491
-------------+----------------------------------------------------------------
ÿÿÿÿÿÿvar(F1)|ÿÿÿÿÿÿÿÿÿÿ1ÿÿ(constrained)
ÿÿÿÿÿÿvar(F2)|ÿÿÿÿÿÿÿÿÿÿ1ÿÿ(constrained)
-------------+----------------------------------------------------------------
ÿÿÿcov(F1,F2)|ÿÿÿ.4072495ÿÿÿ.1862324ÿÿÿÿÿ2.19ÿÿÿ0.029ÿÿÿÿÿ.0422408ÿÿÿÿ.7722582
------------------------------------------------------------------------------

.ÿ
.ÿexit

endÿofÿdo-file


.

Who would have thought, huh? Good luck!

References here (dataset creation) and here (EFA-within-CFA-framework model).

[Weiwen Ng]

I have a doubt on using Exploratory Factor Analysis. In every references I read EFA is used for ordinal variables or likert scale variables. My question is can EFA be used on nominal categorical variables?

Definitely no. You need the questions to be 1) measuring the same thing, and 2) a higher value means a greater quantity of whatever thing is being measured.

If you have a nominal variable, you definitely would violate @2.

It is possible to use nominal items in more advanced techniques related to latent variables, e.g. item response theory (that's like a generalized CFA) has a model for nominal variables (nominal response model). I would probably not suggest jumping to IRT if you're still unfamiliar with EFA.