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Re: st: Need help with ordinal reliability
From
Marta Garcia-Granero <[email protected]>
To
[email protected]
Subject
Re: st: Need help with ordinal reliability
Date
Tue, 05 Nov 2013 18:49:45 +0100
Hi again:
I must be really tired (18:30 CET...), I didn't elaborate my question,
and the information I provided was really scanty.
I'm using 64 bits Stata, v 12.1 . The author of the original code is
Joseph Coveney, and there was a couple of extra lines at the bottom of
my first message that should have been eliminated (alpha latent....).
Also, the output for -polychoric- was:
Polychoric correlation matrix
das1 das2 das3 das4 das5 das6
das7
das1 1
das2 .42024951 1
das3 .24509033 .32364968 1
das4 .36344476 .52974183 .29672763 1
das5 .33015234 .50295552 .31500762 .43689823 1
das6 .40680867 .49900034 .27843518 .69339713 .37167844 1
das7 .29380929 .32790657 .29356005 .38405985 .39296997
.33400672 1
das8 .35677412 .46419956 .40604081 .49256452 .4567259 .46454955
.53930077
das9 .37077838 .34527103 .26974445 .38840096 .36997004
.35312515 .3571874
das10 .4256425 .51589102 .33813277 .4370863 .48154865 .3910091
.4668058
das11 .37272216 .59244225 .29880755 .52523425 .43404926 .47464957
.34626293
das12 .5121788 .51715508 .32776665 .43895648 .4803188 .42322721
.46064299
das13 .37993855 .37775355 .14527508 .34385484 .34732168 .33653105
.3241444
das14 .32637852 .7394756 .24964994 .54102318 .48400845 .46558826
.28458065
das15 .43274919 .39017236 .25867438 .38537742 .40293415 .36018827
.4156234
das16 .3474 .46423544 .33881092 .48665781 .48483778 .47053561
.38825415
das17 .27212954 .32632387 .21638421 .29795486 .33135255 .30779252
.31076419
das18 .37998676 .54349127 .28253174 .56671336 .43986064 .57194078
.39590204
das19 .28731551 .37032004 .17092521 .42829171 .36295912 .42772512
.31525391
das20 .34803246 .41226741 .30207845 .50186841 .41863183 .49193995
.36804831
das21 .31516767 .46410866 .25604889 .48067802 .4013685 .41396665
.37932535
das22 .32576574 .4746789 .24764487 .52425967 .42027355 .43977791
.41068211
das23 .27030853 .37106997 .16708249 .50227817 .2928019 .47093511
.23610837
das24 .22180702 .48066541 .19659108 .46905576 .32300993 .38518647
.26956413
das25 .2485514 .41517796 .15882277 .46634462 .33945453 .38547609
.3262669
das26 .30558935 .40363268 .20375388 .55666758 .39058928 .46668209
.34172138
das27 .34826363 .45727001 .1819646 .48968223 .37207247 .392942
.32741759
das28 .30102988 .35688643 .09771854 .37671756 .26926171 .33082686
.23635546
das29 .17060376 .26941245 .15150186 .31490059 .21765541 .457789
.11360179
das30 .23004565 .40100813 .17788273 .5200932 .33359417 .51155108
.26112867
das31 .30294671 .50141191 .27341805 .60021678 .44015522 .53789039
.38973157
das32 .17851874 .28451353 .17292657 .29183327 .2629968 .25458053
.21833671
das8 das9 das10 das11 das12 das13
das14
das8 1
das9 .39974386 1
das10 .67476763 .43931616 1
das11 .50594364 .32861706 .52631507 1
das12 .57449347 .35833765 .67020656 .55697966 1
das13 .22995568 .30662879 .28584647 .34872446 .33847967 1
das14 .47828855 .31375469 .50503804 .60258201 .45629234
.39982639 1
das15 .44764559 .38888315 .5150241 .45929063 .59085042 .30721258
.38580768
das16 .53914135 .40808404 .548167 .48414852 .53424112 .29754421
.42090407
das17 .37858619 .27487912 .36552225 .30119579 .40823621 .22405586
.26188821
das18 .53667085 .40097699 .54474508 .54758093 .54306969 .307511
.5172758
das19 .41073243 .25659421 .44896752 .39782274 .43872791 .23747139
.35624799
das20 .50370828 .38665834 .53478484 .47388992 .53354992 .33501667
.3960891
das21 .45082815 .40043999 .41305077 .40661984 .4814231 .30269254
.41616912
das22 .49334693 .41727551 .44567218 .44527364 .51258513 .3301241
.47886932
das23 .42055161 .27078847 .35860358 .41942107 .38881377 .26157417
.39512935
das24 .40644921 .25634044 .35810732 .46382043 .37930243 .25365179
.48636124
das25 .44719107 .22144828 .43444377 .45265554 .41230003 .20935814
.42296945
das26 .4656409 .30134818 .48658089 .50974805 .48665406 .27002113
.40664821
das27 .44547295 .30125276 .46307111 .49444787 .46038074 .25635778
.43403325
das28 .33775888 .22580941 .40599368 .41224761 .38915379 .19157961
.36126268
das29 .16210088 .14310961 .16657815 .26335942 .20955204 .25445594
.26346291
das30 .32219318 .31208264 .35928233 .43881299 .3438433 .25124765
.35528767
das31 .5594746 .40275532 .53951207 .53746186 .52556581
.28186361 .489413
das32 .39308945 .20784672 .40532155 .27294979 .34535153 .15746432
.22744404
das15 das16 das17 das18 das19 das20
das21
das15 1
das16 .42843025 1
das17 .2715098 .55145526 1
das18 .40177144 .61553926 .40024254 1
das19 .33440658 .42463585 .27899256 .59261299 1
das20 .36273551 .64974173 .46385731 .63766106 .49851409 1
das21 .3495031 .54791334 .46836068 .50849809 .29701768
.44893774 1
das22 .35754035 .58076167 .43313462 .6023346 .4015379 .53049849
.75844258
das23 .22245339 .42709582 .26960073 .52450195 .47138197 .48407088
.34800332
das24 .28869687 .39769693 .23907163 .42040602 .42213481 .43759911
.36103298
das25 .30213549 .42560391 .32038514 .50009688 .54269902 .43683276
.32682595
das26 .3780645 .49861168 .32256891 .55895062 .50612039 .52789705
.3799089
das27 .33505036 .46936441 .34072844 .52474993 .51335194 .46497783
.4306266
das28 .27806576 .36007783 .21866912 .43091786 .39498232 .4013719
.29679069
das29 .14391492 .27002725 .16091784 .22858012 .16480877 .30077832
.21453991
das30 .2687126 .42673629 .3295372 .49543879 .36399201 .47287811
.38799431
das31 .37635398 .67924448 .40919612 .72687442 .54662431 .66736428
.52480879
das32 .22394362 .37040292 .25326946 .30576045 .39058949 .45059708
.18703795
das22 das23 das24 das25 das26 das27
das28
das22 1
das23 .42681021 1
das24 .41124172 .42101534 1
das25 .42970114 .45255165 .50364222 1
das26 .50953651 .57670573 .47293366 .68971886 1
das27 .50659321 .47978179 .49151468 .71981116 .70539631 1
das28 .38998676 .35492021 .46455837 .59195162 .54833839
.63541313 1
das29 .24271537 .2348952 .16562635 .20240707 .22845612 .19466781
.16714515
das30 .43590664 .42594789 .34133411 .36934575 .47968264 .415452
.27268197
das31 .62639611 .61326334 .48390416 .56170046 .6438142 .57205315
.44793834
das32 .27699136 .3621606 .26743391 .30494233 .3731506 .2863538
.28644285
das29 das30 das31 das32
das29 1
das30 .6029357 1
das31 .33279594 .53289225 1
das32 .13314184 .22366825 .45268499 1
El 05/11/2013 18:24, Marta Garcia-Granero escribió:
I am working with a translation of the Dyadic Adjustment Scale
(Spanier, 1976) in 915 cases. The scale is formed by 32 (das1...das32)
categorical items, 30 are 5-point Likert and 2 of them are binary. I
have been asked to get ordinal alphas for the total score and the 4
subscales (the author's got the paper in stand-by until corrected, and
came to me for help, I have less than two weeks to answer all the
questions asked by the reviewers) . After searching the Statalist
archive I found this solution:
http://www.stata.com/statalist/archive/2012-02/msg00696.html
I can't get pass the 3rd line (-factormat- command). I get the
following error:
factor estimation result not found
r(301);
My code (adapted from the Statalist thread mentioned above):
polychoric das*
matrix define `C' = r(R)
factormat `C', n(915) factors(1)
tempname L Psi
matrix define `L' = e(L)
matrix define `Psi' = e(Psi)
local p = rowsof(`L')
tempname f f2 u2
scalar define `f' = 0
scalar define `f2' = 0
scalar define `u2' = 0
forvalues i = 1/`p' {
scalar define `f' = `f' + `L'[`i', 1]
scalar define `f2' = `f2' + `L'[`i', 1] * `L'[`i', 1]
scalar define `u2' = `u2' + `Psi'[1, `i']
}
scalar define `f' = `f' / `p'
scalar define `f2' = `f2' / `p'
scalar define `u2' = `u2' / `p'
tempname pf2
scalar define `pf2' = `p' * `f' * `f'
scalar define alpha = `p' / (`p' - 1) * ///
(`pf2' - `f2') / (`pf2' + `u2')
display in smcl as text "Ordinal alpha = " as result %06.4f alpha
alpha latent*
alpha das*, std
I'd be very grateful if somebody could help me.
Thanks in advance,
Marta GG
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