Combining multiple variables

[Alyssa Sadres]

Hello!

I just started learning Stata and I am using a consensus data set and trying to combine a bunch of different variables to make one "Happy People" vs "Unhappy People" variable and then see which group is more active in Political Rallys. The variables I want to use are:

p_psych 1 (Self- Described Anxious rated 1 - 5)
p_mental (Mental Health rated 1 - 5)
p_life_ideal (In most ways my life is ideal rated 1 - 5)
p_life_sat (I am satisfied with my life rated 1 - 5)

If there's a way to combine these I would love to hear your thoughts! I have no idea what I'm doing so anything would help!

Thank you!

[Marc Kaulisch]

I would look at

egen p_combined = group(p_psych p_mental p_life_ideal p_life_sat)
or help egen

but maybe there are more advanced options out there

[Joseph Coveney]

Maybe something like one or the other of the following. Begin at the "Begin here" comment. You'd use the predictions (factor scores) in further exploration of political rally participation.

.ÿ
.ÿversionÿ16.1

.ÿ
.ÿclearÿ*

.ÿ
.ÿsetÿseedÿ`=strreverse("1583987")'

.ÿ
.ÿtempnameÿCorr

.ÿmatrixÿdefineÿ`Corr'ÿ=ÿJ(4,ÿ4,ÿ0.5)ÿ+ÿI(4)ÿ*ÿ0.5

.ÿquietlyÿdrawnormÿp_psychÿp_mentalÿp_life_idealÿp_life_sat,ÿ///
>ÿÿÿÿÿÿÿÿÿdoubleÿcorr(`Corr')ÿn(250)

.ÿquietlyÿreplaceÿp_psychÿ=ÿ-p_psych

.ÿ
.ÿtempvarÿtmpvar0

.ÿforeachÿvarÿofÿvarlistÿp_*ÿ{
ÿÿ2.ÿÿÿÿÿÿÿÿÿegenÿbyteÿ`tmpvar0'ÿ=ÿcut(`var'),ÿgroup(5)
ÿÿ3.ÿÿÿÿÿÿÿÿÿquietlyÿreplaceÿ`var'ÿ=ÿ`tmpvar0'
ÿÿ4.ÿÿÿÿÿÿÿÿÿdropÿ`tmpvar0'
ÿÿ5.ÿ}

.ÿ
.ÿ*
.ÿ*ÿBeginÿhere
.ÿ*
.ÿpolychoricÿp_*

Polychoricÿcorrelationÿmatrix

ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿp_psychÿÿÿÿÿÿp_mentalÿÿp_life_idealÿÿÿÿp_life_sat
ÿÿÿÿÿp_psychÿÿÿÿÿÿÿÿÿÿÿÿÿ1
ÿÿÿÿp_mentalÿÿÿÿ-.53475763ÿÿÿÿÿÿÿÿÿÿÿÿÿ1
p_life_idealÿÿÿÿ-.59249676ÿÿÿÿÿ.54580398ÿÿÿÿÿÿÿÿÿÿÿÿÿ1
ÿÿp_life_satÿÿÿÿ-.50420777ÿÿÿÿÿ.52567423ÿÿÿÿÿ.48187567ÿÿÿÿÿÿÿÿÿÿÿÿÿ1

.ÿtempnameÿRho

.ÿmatrixÿdefineÿ`Rho'ÿ=ÿr(R)

.ÿlocalÿNÿ`r(N)'

.ÿ
.ÿ//ÿUsingÿexploratoryÿfactorÿanalysis
.ÿfactormatÿ`Rho',ÿfactors(4)ÿn(`N')ÿforcepsd
(obs=250)

Factorÿanalysis/correlationÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿNumberÿofÿobsÿÿÿÿ=ÿÿÿÿÿÿÿÿ250
ÿÿÿÿMethod:ÿprincipalÿfactorsÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿRetainedÿfactorsÿ=ÿÿÿÿÿÿÿÿÿÿ1
ÿÿÿÿRotation:ÿ(unrotated)ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿNumberÿofÿparamsÿ=ÿÿÿÿÿÿÿÿÿÿ4

ÿÿÿÿ--------------------------------------------------------------------------
ÿÿÿÿÿÿÿÿÿFactorÿÿ|ÿÿÿEigenvalueÿÿÿDifferenceÿÿÿÿÿÿÿÿProportionÿÿÿCumulative
ÿÿÿÿ-------------+------------------------------------------------------------
ÿÿÿÿÿÿÿÿFactor1ÿÿ|ÿÿÿÿÿÿ2.01221ÿÿÿÿÿÿ2.06909ÿÿÿÿÿÿÿÿÿÿÿÿ1.2069ÿÿÿÿÿÿÿ1.2069
ÿÿÿÿÿÿÿÿFactor2ÿÿ|ÿÿÿÿÿ-0.05688ÿÿÿÿÿÿ0.06966ÿÿÿÿÿÿÿÿÿÿÿ-0.0341ÿÿÿÿÿÿÿ1.1728
ÿÿÿÿÿÿÿÿFactor3ÿÿ|ÿÿÿÿÿ-0.12654ÿÿÿÿÿÿ0.03506ÿÿÿÿÿÿÿÿÿÿÿ-0.0759ÿÿÿÿÿÿÿ1.0969
ÿÿÿÿÿÿÿÿFactor4ÿÿ|ÿÿÿÿÿ-0.16159ÿÿÿÿÿÿÿÿÿÿÿÿ.ÿÿÿÿÿÿÿÿÿÿÿ-0.0969ÿÿÿÿÿÿÿ1.0000
ÿÿÿÿ--------------------------------------------------------------------------
ÿÿÿÿLRÿtest:ÿindependentÿvs.ÿsaturated:ÿÿchi2(6)ÿÿ=ÿÿ332.47ÿProb>chi2ÿ=ÿ0.0000

Factorÿloadingsÿ(patternÿmatrix)ÿandÿuniqueÿvariances

ÿÿÿÿ---------------------------------------
ÿÿÿÿÿÿÿÿVariableÿ|ÿÿFactor1ÿ|ÿÿÿUniquenessÿ
ÿÿÿÿ-------------+----------+--------------
ÿÿÿÿÿÿÿÿÿp_psychÿ|ÿÿ-0.7312ÿ|ÿÿÿÿÿÿ0.4654ÿÿ
ÿÿÿÿÿÿÿÿp_mentalÿ|ÿÿÿ0.7136ÿ|ÿÿÿÿÿÿ0.4908ÿÿ
ÿÿÿÿp_life_idealÿ|ÿÿÿ0.7267ÿ|ÿÿÿÿÿÿ0.4719ÿÿ
ÿÿÿÿÿÿp_life_satÿ|ÿÿÿ0.6636ÿ|ÿÿÿÿÿÿ0.5596ÿÿ
ÿÿÿÿ---------------------------------------

.ÿpredictÿdoubleÿF*,ÿregressÿ//ÿUseÿtheÿpredictedÿfactorÿvalues

Scoringÿcoefficientsÿ(methodÿ=ÿregression)

ÿÿÿÿ------------------------
ÿÿÿÿÿÿÿÿVariableÿ|ÿÿFactor1ÿ
ÿÿÿÿ-------------+----------
ÿÿÿÿÿÿÿÿÿp_psychÿ|ÿ-0.29583ÿ
ÿÿÿÿÿÿÿÿp_mentalÿ|ÿÿ0.27654ÿ
ÿÿÿÿp_life_idealÿ|ÿÿ0.28995ÿ
ÿÿÿÿÿÿp_life_satÿ|ÿÿ0.22937ÿ
ÿÿÿÿ------------------------

(variableÿmeansÿassumedÿ0;ÿuseÿmeans()ÿoptionÿofÿfactormatÿforÿnonzeroÿmeans)
(variableÿstd.ÿdeviationsÿassumedÿ1;ÿuseÿsds()ÿoptionÿofÿfactormatÿtoÿchange)

.ÿ
.ÿ//ÿAlternative
.ÿgsemÿ(p_psychÿp_mentalÿp_life_idealÿp_life_satÿ<-ÿF,ÿoprobit),ÿ///
>ÿÿÿÿÿÿÿÿÿnocnsreportÿnodvheaderÿnolog

GeneralizedÿstructuralÿequationÿmodelÿÿÿÿÿÿÿÿÿÿÿNumberÿofÿobsÿÿÿÿÿ=ÿÿÿÿÿÿÿÿ250
Logÿlikelihoodÿ=ÿ-1474.3523

-------------------------------------------------------------------------------
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿ|ÿÿÿÿÿÿCoef.ÿÿÿStd.ÿErr.ÿÿÿÿÿÿzÿÿÿÿP>|z|ÿÿÿÿÿ[95%ÿConf.ÿInterval]
--------------+----------------------------------------------------------------
p_psychÿÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿÿÿFÿ|ÿÿÿÿÿÿÿÿÿÿ1ÿÿ(constrained)
--------------+----------------------------------------------------------------
p_mentalÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿÿÿFÿ|ÿÿ-.9175627ÿÿÿ.1810519ÿÿÿÿ-5.07ÿÿÿ0.000ÿÿÿÿ-1.272418ÿÿÿ-.5627075
--------------+----------------------------------------------------------------
p_life_idealÿÿ|
ÿÿÿÿÿÿÿÿÿÿÿÿFÿ|ÿÿ-.9694851ÿÿÿ.1820474ÿÿÿÿ-5.33ÿÿÿ0.000ÿÿÿÿ-1.326292ÿÿÿ-.6126788
--------------+----------------------------------------------------------------
p_life_satÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿÿÿÿFÿ|ÿÿ-.7903411ÿÿÿ.1499519ÿÿÿÿ-5.27ÿÿÿ0.000ÿÿÿÿ-1.084241ÿÿÿ-.4964408
--------------+----------------------------------------------------------------
/p_psychÿÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿcut1ÿ|ÿÿ-1.282816ÿÿÿ.1570995ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ-1.590726ÿÿÿ-.9749067
ÿÿÿÿÿÿÿÿÿcut2ÿ|ÿÿ-.3720133ÿÿÿ.1224328ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ-.6119771ÿÿÿ-.1320495
ÿÿÿÿÿÿÿÿÿcut3ÿ|ÿÿÿ.3779058ÿÿÿ.1227081ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ.1374023ÿÿÿÿ.6184092
ÿÿÿÿÿÿÿÿÿcut4ÿ|ÿÿÿ1.280565ÿÿÿ.1559497ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ.9749088ÿÿÿÿÿ1.58622
--------------+----------------------------------------------------------------
/p_mentalÿÿÿÿÿ|
ÿÿÿÿÿÿÿÿÿcut1ÿ|ÿÿ-1.223338ÿÿÿ.1447257ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ-1.506995ÿÿÿ-.9396813
ÿÿÿÿÿÿÿÿÿcut2ÿ|ÿÿ-.3760188ÿÿÿ.1174271ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ-.6061717ÿÿÿ-.1458658
ÿÿÿÿÿÿÿÿÿcut3ÿ|ÿÿÿ.3643868ÿÿÿ.1175808ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ.1339327ÿÿÿÿ.5948409
ÿÿÿÿÿÿÿÿÿcut4ÿ|ÿÿÿ1.236343ÿÿÿ.1462188ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ.9497593ÿÿÿÿ1.522926
--------------+----------------------------------------------------------------
/p_life_idealÿ|
ÿÿÿÿÿÿÿÿÿcut1ÿ|ÿÿ-1.264692ÿÿÿ.1515538ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ-1.561732ÿÿÿ-.9676522
ÿÿÿÿÿÿÿÿÿcut2ÿ|ÿÿ-.3924567ÿÿÿ.1212637ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ-.6301292ÿÿÿ-.1547842
ÿÿÿÿÿÿÿÿÿcut3ÿ|ÿÿÿ.3658428ÿÿÿ.1209823ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ.1287218ÿÿÿÿ.6029638
ÿÿÿÿÿÿÿÿÿcut4ÿ|ÿÿÿ1.274346ÿÿÿ.1537466ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ.9730084ÿÿÿÿ1.575684
--------------+----------------------------------------------------------------
/p_life_satÿÿÿ|
ÿÿÿÿÿÿÿÿÿcut1ÿ|ÿÿ-1.150196ÿÿÿ.1308569ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ-1.406671ÿÿÿ-.8937216
ÿÿÿÿÿÿÿÿÿcut2ÿ|ÿÿ-.3421812ÿÿÿ.1090382ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ-.5558921ÿÿÿ-.1284703
ÿÿÿÿÿÿÿÿÿcut3ÿ|ÿÿÿ.3457604ÿÿÿ.1086097ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ.1328893ÿÿÿÿ.5586315
ÿÿÿÿÿÿÿÿÿcut4ÿ|ÿÿÿ1.137317ÿÿÿ.1302363ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ.8820581ÿÿÿÿ1.392575
--------------+----------------------------------------------------------------
ÿÿÿÿÿÿÿÿvar(F)|ÿÿÿ1.359835ÿÿÿ.3714325ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ.7961305ÿÿÿÿ2.322672
-------------------------------------------------------------------------------

.ÿpredictÿdoubleÿF,ÿlatentÿ//ÿUseÿtheÿpredictedÿfactorÿvalues
(optionÿebmeansÿassumed)
(usingÿ7ÿquadratureÿpoints)

.ÿ
.ÿexit

endÿofÿdo-file


.

For the first alternative (exploratory factor analysis), you'll need to install the user-written command polychoric. You can find out where to get it by typing

search polychoric

at the command line in Stata.