Just as a preface, I struggle with statistics in general, so using this type of model is hard for me.
I have a dataset that is a complex setup, has dyadic data, and my DV is ordered probit quality. Hence why I think I need to use the surveyset structure and need something like a seemingly unrelated regression due to the data being couples. Here is the command that I created:
svy: cmp (wife_rs = wife_fininf##wife_cr wife_mp wife_cmt prb8p1_w6 wife_race wife_religion wife_edu wife_work wife_income) (husband_rs = husband_fininf##husband_cr husband_mp husband_cmt prb8p2_w6 husband_race husband_religion husband_edu husband_work husband_income), ind($cmp_oprobit $cmp_oprobit) nolr tech(dfp) qui
Here is the output:
Survey: Mixed-process regression
Number of strata = 1 Number of obs = 1,205
Number of PSUs = 1,205 Population size = 1,232.622
Design df = 1,204
F(55, 1150) = 2050.65
Prob > F = 0.0000
-------------------------------------------------------------------------------------------
| Linearized
| Coefficient std. err. t P>|t| [95% conf. interval]
--------------------------+----------------------------------------------------------------
wife_rs |
1.wife_fininf | -.1439225 .2728693 -0.53 0.598 -.6792747 .3914298
|
wife_cr |
6 | 5.27523 .5668748 9.31 0.000 4.163058 6.387402
7 | 5.210153 .7420652 7.02 0.000 3.754268 6.666037
8 | .1698807 .4447723 0.38 0.703 -.7027342 1.042496
9 | 6.143339 .3407378 18.03 0.000 5.474834 6.811845
10 | 4.107221 .6312215 6.51 0.000 2.868805 5.345637
11 | 4.06554 .6328532 6.42 0.000 2.823922 5.307157
12 | 6.284378 .559698 11.23 0.000 5.186286 7.38247
13 | 4.50307 .3932454 11.45 0.000 3.731548 5.274593
14 | 5.193338 .6838276 7.59 0.000 3.851712 6.534964
15 | 5.314731 .7031987 7.56 0.000 3.9351 6.694362
16 | 5.247294 .4434105 11.83 0.000 4.377351 6.117237
17 | 4.929052 .4954272 9.95 0.000 3.957055 5.901048
18 | 5.796641 .5280795 10.98 0.000 4.760583 6.832699
19 | 5.095001 .6075197 8.39 0.000 3.903086 6.286916
20 | 5.672239 .577104 9.83 0.000 4.539997 6.80448
21 | 5.869622 .4396353 13.35 0.000 5.007085 6.732158
22 | 5.639016 .4373406 12.89 0.000 4.780981 6.49705
23 | 5.671711 .4516751 12.56 0.000 4.785553 6.557869
24 | 6.084247 .4592531 13.25 0.000 5.183222 6.985272
25 | 5.916991 .4614776 12.82 0.000 5.011602 6.822381
26 | 5.656893 .4779102 11.84 0.000 4.719264 6.594522
27 | 6.249046 .4863568 12.85 0.000 5.294845 7.203246
28 | 6.386974 .5024775 12.71 0.000 5.401146 7.372803
29 | 6.402607 .4951193 12.93 0.000 5.431214 7.374
|
wife_fininf#wife_cr |
1 6 | -6.46932 .6528212 -9.91 0.000 -7.750113 -5.188526
1 8 | 3.30076 .4525057 7.29 0.000 2.412972 4.188547
1 9 | -1.180545 .3901033 -3.03 0.003 -1.945903 -.4151871
1 10 | .1363156 .8464264 0.16 0.872 -1.524319 1.79695
1 11 | 1.845903 .6689476 2.76 0.006 .5334708 3.158336
1 12 | -2.465362 .6132178 -4.02 0.000 -3.668456 -1.262268
1 13 | .0564253 .5516644 0.10 0.919 -1.025905 1.138756
1 14 | .3454595 .6786976 0.51 0.611 -.986102 1.677021
1 15 | -.1630083 .7071619 -0.23 0.818 -1.550415 1.224398
1 16 | -.147146 .4322433 -0.34 0.734 -.9951798 .7008878
1 17 | .6431959 .4482745 1.43 0.152 -.2362901 1.522682
1 18 | -.5001383 .4858583 -1.03 0.304 -1.453361 .4530847
1 19 | .5372688 .5375507 1.00 0.318 -.5173715 1.591909
1 20 | .0110651 .5460897 0.02 0.984 -1.060328 1.082458
1 21 | -.0761442 .3513139 -0.22 0.828 -.7653997 .6131112
1 22 | .2265374 .3519743 0.64 0.520 -.4640137 .9170885
1 23 | .212833 .3512567 0.61 0.545 -.4763103 .9019763
1 24 | -.2780515 .3676753 -0.76 0.450 -.9994069 .4433039
1 25 | -.0863773 .3407129 -0.25 0.800 -.7548344 .5820797
1 26 | .4986869 .3580414 1.39 0.164 -.2037675 1.201141
1 27 | .028695 .3338009 0.09 0.932 -.6262011 .683591
1 28 | -.2387306 .3696347 -0.65 0.518 -.9639303 .4864692
1 29 | 0 (omitted)
|
wife_mp | .0971367 .0119291 8.14 0.000 .0737327 .1205408
wife_cmt | .0752912 .0072174 10.43 0.000 .0611312 .0894513
prb8p1_w6 | -.1561138 .0506693 -3.08 0.002 -.2555238 -.0567038
wife_race | .0109032 .0288897 0.38 0.706 -.0457765 .067583
wife_religion | -.0037811 .0099986 -0.38 0.705 -.0233977 .0158355
wife_edu | .0271869 .0312376 0.87 0.384 -.0340993 .0884732
wife_work | .0010714 .0151245 0.07 0.944 -.0286019 .0307447
wife_income | -.0069358 .0097307 -0.71 0.476 -.0260268 .0121553
--------------------------+----------------------------------------------------------------
husband_rs |
1.husband_fininf | -4.711775 .3492478 -13.49 0.000 -5.396977 -4.026573
|
husband_cr |
6 | -4.830211 .4288378 -11.26 0.000 -5.671563 -3.988858
7 | 3.822245 .3402387 11.23 0.000 3.154718 4.489772
8 | 4.286137 .6571447 6.52 0.000 2.996861 5.575413
9 | 3.992487 .6394327 6.24 0.000 2.737961 5.247013
10 | .3059873 .3125253 0.98 0.328 -.3071675 .919142
11 | -.3225115 .4095126 -0.79 0.431 -1.125949 .4809261
12 | .8236079 .750912 1.10 0.273 -.6496336 2.29685
13 | -.486873 .198755 -2.45 0.014 -.8768177 -.0969282
14 | .2665348 .3824815 0.70 0.486 -.4838695 1.016939
15 | .3463772 .2152249 1.61 0.108 -.0758804 .7686347
16 | 1.176179 .4642643 2.53 0.011 .2653224 2.087036
17 | .3408291 .3557633 0.96 0.338 -.3571557 1.038814
18 | .4448263 .3835474 1.16 0.246 -.3076692 1.197322
19 | 1.095258 .3238835 3.38 0.001 .4598196 1.730697
20 | 1.000613 .3068454 3.26 0.001 .398602 1.602624
21 | .9262332 .3196484 2.90 0.004 .2991034 1.553363
22 | 1.39153 .3085047 4.51 0.000 .7862636 1.996797
23 | 1.359676 .3338178 4.07 0.000 .7047464 2.014605
24 | 1.211177 .310887 3.90 0.000 .6012366 1.821118
25 | 1.712215 .3362546 5.09 0.000 1.052505 2.371925
26 | 1.566263 .2967141 5.28 0.000 .9841293 2.148398
27 | 1.498822 .3233683 4.64 0.000 .864394 2.13325
28 | 1.808706 .37417 4.83 0.000 1.074609 2.542804
29 | 1.988294 .3171807 6.27 0.000 1.366005 2.610582
|
husband_fininf#husband_cr |
1 6 | 9.443411 .6345768 14.88 0.000 8.198412 10.68841
1 10 | 4.301232 .6198205 6.94 0.000 3.085183 5.51728
1 11 | 5.238215 .5630549 9.30 0.000 4.133537 6.342893
1 12 | 4.193574 .9276966 4.52 0.000 2.373492 6.013655
1 13 | 4.449591 .6655328 6.69 0.000 3.143859 5.755324
1 14 | 4.671867 .5661592 8.25 0.000 3.561099 5.782635
1 15 | 4.988248 .5607673 8.90 0.000 3.888059 6.088438
1 16 | 4.430131 .6041316 7.33 0.000 3.244863 5.615398
1 17 | 5.182295 .5582485 9.28 0.000 4.087047 6.277543
1 18 | 5.02207 .5115426 9.82 0.000 4.018456 6.025684
1 19 | 4.779548 .5153186 9.27 0.000 3.768526 5.79057
1 20 | 4.672066 .4241354 11.02 0.000 3.83994 5.504193
1 21 | 5.049 .4419093 11.43 0.000 4.182002 5.915998
1 22 | 4.50194 .4056876 11.10 0.000 3.706007 5.297873
1 23 | 4.69598 .468935 10.01 0.000 3.77596 5.616001
1 24 | 5.198496 .4289527 12.12 0.000 4.356918 6.040074
1 25 | 4.496101 .4326663 10.39 0.000 3.647237 5.344965
1 26 | 4.891119 .3873849 12.63 0.000 4.131095 5.651144
1 27 | 4.450895 .4210476 10.57 0.000 3.624826 5.276963
1 28 | 4.308915 .445484 9.67 0.000 3.434904 5.182926
1 29 | 4.518423 .4091807 11.04 0.000 3.715636 5.321209
|
husband_mp | .0725569 .0114523 6.34 0.000 .0500882 .0950256
husband_cmt | .079294 .007573 10.47 0.000 .0644363 .0941516
prb8p2_w6 | -.071961 .0723069 -1.00 0.320 -.2138226 .0699006
husband_race | -.0640938 .0282692 -2.27 0.024 -.1195562 -.0086314
husband_religion | -.0205083 .0102404 -2.00 0.045 -.0405994 -.0004173
husband_edu | -.0883359 .0278685 -3.17 0.002 -.143012 -.0336597
husband_work | .0072213 .0256229 0.28 0.778 -.0430493 .0574919
husband_income | -.0059427 .0107708 -0.55 0.581 -.0270743 .0151889
--------------------------+----------------------------------------------------------------
/cut_1_1 | 6.549065 .2798637 23.40 0.000 5.99999 7.09814
/cut_1_2 | 6.802506 .2570238 26.47 0.000 6.298242 7.30677
/cut_1_3 | 6.906985 .2456882 28.11 0.000 6.424961 7.38901
/cut_1_4 | 7.533448 .1933042 38.97 0.000 7.154198 7.912699
/cut_1_5 | 7.853433 .1570681 50.00 0.000 7.545275 8.16159
/cut_1_6 | 8.433101 .1292298 65.26 0.000 8.17956 8.686642
/cut_1_7 | 8.554424 .1141611 74.93 0.000 8.330447 8.778401
/cut_1_8 | 8.766625 .1064207 82.38 0.000 8.557834 8.975415
/cut_1_9 | 9.0919 .078695 115.53 0.000 8.937506 9.246295
/cut_1_10 | 9.356956 .0732434 127.75 0.000 9.213257 9.500655
/cut_1_11 | 9.516326 .0645774 147.36 0.000 9.389629 9.643023
/cut_1_12 | 9.877443 .0592259 166.78 0.000 9.761246 9.99364
/cut_1_13 | 10.36697 .0610518 169.81 0.000 10.24719 10.48675
/cut_1_14 | 10.77941 .0529587 203.54 0.000 10.67551 10.88331
/cut_1_15 | 11.05626 .0510384 216.63 0.000 10.95612 11.15639
/cut_1_16 | 11.38935 .0518334 219.73 0.000 11.28766 11.49105
/cut_1_17 | 11.75172 .054696 214.86 0.000 11.64441 11.85903
/cut_1_18 | 12.16083 .0587889 206.86 0.000 12.04549 12.27617
/cut_1_19 | 12.49868 .0636536 196.35 0.000 12.37379 12.62356
/cut_1_20 | 12.8212 .0695179 184.43 0.000 12.68481 12.95759
/cut_1_21 | 13.95337 .106686 130.79 0.000 13.74406 14.16268
/cut_2_1 | .9714216 .3779561 2.57 0.010 .2298957 1.712947
/cut_2_2 | 1.399563 .3469212 4.03 0.000 .7189262 2.080201
/cut_2_3 | 1.773911 .3699576 4.79 0.000 1.048077 2.499744
/cut_2_4 | 2.135802 .3330923 6.41 0.000 1.482296 2.789308
/cut_2_5 | 2.281279 .3351432 6.81 0.000 1.62375 2.938809
/cut_2_6 | 2.439405 .3435383 7.10 0.000 1.765405 3.113405
/cut_2_7 | 2.590713 .3479788 7.45 0.000 1.908001 3.273426
/cut_2_8 | 2.827636 .3538327 7.99 0.000 2.133439 3.521833
/cut_2_9 | 3.463741 .3814875 9.08 0.000 2.715287 4.212196
/cut_2_10 | 3.759348 .3847036 9.77 0.000 3.004584 4.514111
/cut_2_11 | 3.982628 .3838137 10.38 0.000 3.22961 4.735646
/cut_2_12 | 4.319697 .3876242 11.14 0.000 3.559203 5.080191
/cut_2_13 | 4.828428 .3957337 12.20 0.000 4.052024 5.604832
/cut_2_14 | 5.133628 .4042446 12.70 0.000 4.340526 5.92673
/cut_2_15 | 5.39824 .4068624 13.27 0.000 4.600001 6.196478
/cut_2_16 | 5.808665 .4103875 14.15 0.000 5.003511 6.613819
/cut_2_17 | 6.074721 .4143376 14.66 0.000 5.261817 6.887625
/cut_2_18 | 6.543236 .419843 15.58 0.000 5.71953 7.366941
/cut_2_19 | 6.859554 .4239055 16.18 0.000 6.027878 7.691229
/cut_2_20 | 7.155699 .4287537 16.69 0.000 6.314512 7.996887
/cut_2_21 | 8.177527 .4405744 18.56 0.000 7.313148 9.041906
/atanhrho_12 | .35454 .0469131 7.56 0.000 .2624994 .4465805
-------------------------------------------------------------------------------------------
My questions are:
1) Do I need to run margins on this to interpret it properly? Or do I just look at the co-efficients and p-values?
2) Is the correct interpretation, for example on the variable "wife_cr", that a level 6 results in a 5.27523 increase in wife_rs?
Thank you so much!!
I have a dataset that is a complex setup, has dyadic data, and my DV is ordered probit quality. Hence why I think I need to use the surveyset structure and need something like a seemingly unrelated regression due to the data being couples. Here is the command that I created:
svy: cmp (wife_rs = wife_fininf##wife_cr wife_mp wife_cmt prb8p1_w6 wife_race wife_religion wife_edu wife_work wife_income) (husband_rs = husband_fininf##husband_cr husband_mp husband_cmt prb8p2_w6 husband_race husband_religion husband_edu husband_work husband_income), ind($cmp_oprobit $cmp_oprobit) nolr tech(dfp) qui
Here is the output:
Survey: Mixed-process regression
Number of strata = 1 Number of obs = 1,205
Number of PSUs = 1,205 Population size = 1,232.622
Design df = 1,204
F(55, 1150) = 2050.65
Prob > F = 0.0000
-------------------------------------------------------------------------------------------
| Linearized
| Coefficient std. err. t P>|t| [95% conf. interval]
--------------------------+----------------------------------------------------------------
wife_rs |
1.wife_fininf | -.1439225 .2728693 -0.53 0.598 -.6792747 .3914298
|
wife_cr |
6 | 5.27523 .5668748 9.31 0.000 4.163058 6.387402
7 | 5.210153 .7420652 7.02 0.000 3.754268 6.666037
8 | .1698807 .4447723 0.38 0.703 -.7027342 1.042496
9 | 6.143339 .3407378 18.03 0.000 5.474834 6.811845
10 | 4.107221 .6312215 6.51 0.000 2.868805 5.345637
11 | 4.06554 .6328532 6.42 0.000 2.823922 5.307157
12 | 6.284378 .559698 11.23 0.000 5.186286 7.38247
13 | 4.50307 .3932454 11.45 0.000 3.731548 5.274593
14 | 5.193338 .6838276 7.59 0.000 3.851712 6.534964
15 | 5.314731 .7031987 7.56 0.000 3.9351 6.694362
16 | 5.247294 .4434105 11.83 0.000 4.377351 6.117237
17 | 4.929052 .4954272 9.95 0.000 3.957055 5.901048
18 | 5.796641 .5280795 10.98 0.000 4.760583 6.832699
19 | 5.095001 .6075197 8.39 0.000 3.903086 6.286916
20 | 5.672239 .577104 9.83 0.000 4.539997 6.80448
21 | 5.869622 .4396353 13.35 0.000 5.007085 6.732158
22 | 5.639016 .4373406 12.89 0.000 4.780981 6.49705
23 | 5.671711 .4516751 12.56 0.000 4.785553 6.557869
24 | 6.084247 .4592531 13.25 0.000 5.183222 6.985272
25 | 5.916991 .4614776 12.82 0.000 5.011602 6.822381
26 | 5.656893 .4779102 11.84 0.000 4.719264 6.594522
27 | 6.249046 .4863568 12.85 0.000 5.294845 7.203246
28 | 6.386974 .5024775 12.71 0.000 5.401146 7.372803
29 | 6.402607 .4951193 12.93 0.000 5.431214 7.374
|
wife_fininf#wife_cr |
1 6 | -6.46932 .6528212 -9.91 0.000 -7.750113 -5.188526
1 8 | 3.30076 .4525057 7.29 0.000 2.412972 4.188547
1 9 | -1.180545 .3901033 -3.03 0.003 -1.945903 -.4151871
1 10 | .1363156 .8464264 0.16 0.872 -1.524319 1.79695
1 11 | 1.845903 .6689476 2.76 0.006 .5334708 3.158336
1 12 | -2.465362 .6132178 -4.02 0.000 -3.668456 -1.262268
1 13 | .0564253 .5516644 0.10 0.919 -1.025905 1.138756
1 14 | .3454595 .6786976 0.51 0.611 -.986102 1.677021
1 15 | -.1630083 .7071619 -0.23 0.818 -1.550415 1.224398
1 16 | -.147146 .4322433 -0.34 0.734 -.9951798 .7008878
1 17 | .6431959 .4482745 1.43 0.152 -.2362901 1.522682
1 18 | -.5001383 .4858583 -1.03 0.304 -1.453361 .4530847
1 19 | .5372688 .5375507 1.00 0.318 -.5173715 1.591909
1 20 | .0110651 .5460897 0.02 0.984 -1.060328 1.082458
1 21 | -.0761442 .3513139 -0.22 0.828 -.7653997 .6131112
1 22 | .2265374 .3519743 0.64 0.520 -.4640137 .9170885
1 23 | .212833 .3512567 0.61 0.545 -.4763103 .9019763
1 24 | -.2780515 .3676753 -0.76 0.450 -.9994069 .4433039
1 25 | -.0863773 .3407129 -0.25 0.800 -.7548344 .5820797
1 26 | .4986869 .3580414 1.39 0.164 -.2037675 1.201141
1 27 | .028695 .3338009 0.09 0.932 -.6262011 .683591
1 28 | -.2387306 .3696347 -0.65 0.518 -.9639303 .4864692
1 29 | 0 (omitted)
|
wife_mp | .0971367 .0119291 8.14 0.000 .0737327 .1205408
wife_cmt | .0752912 .0072174 10.43 0.000 .0611312 .0894513
prb8p1_w6 | -.1561138 .0506693 -3.08 0.002 -.2555238 -.0567038
wife_race | .0109032 .0288897 0.38 0.706 -.0457765 .067583
wife_religion | -.0037811 .0099986 -0.38 0.705 -.0233977 .0158355
wife_edu | .0271869 .0312376 0.87 0.384 -.0340993 .0884732
wife_work | .0010714 .0151245 0.07 0.944 -.0286019 .0307447
wife_income | -.0069358 .0097307 -0.71 0.476 -.0260268 .0121553
--------------------------+----------------------------------------------------------------
husband_rs |
1.husband_fininf | -4.711775 .3492478 -13.49 0.000 -5.396977 -4.026573
|
husband_cr |
6 | -4.830211 .4288378 -11.26 0.000 -5.671563 -3.988858
7 | 3.822245 .3402387 11.23 0.000 3.154718 4.489772
8 | 4.286137 .6571447 6.52 0.000 2.996861 5.575413
9 | 3.992487 .6394327 6.24 0.000 2.737961 5.247013
10 | .3059873 .3125253 0.98 0.328 -.3071675 .919142
11 | -.3225115 .4095126 -0.79 0.431 -1.125949 .4809261
12 | .8236079 .750912 1.10 0.273 -.6496336 2.29685
13 | -.486873 .198755 -2.45 0.014 -.8768177 -.0969282
14 | .2665348 .3824815 0.70 0.486 -.4838695 1.016939
15 | .3463772 .2152249 1.61 0.108 -.0758804 .7686347
16 | 1.176179 .4642643 2.53 0.011 .2653224 2.087036
17 | .3408291 .3557633 0.96 0.338 -.3571557 1.038814
18 | .4448263 .3835474 1.16 0.246 -.3076692 1.197322
19 | 1.095258 .3238835 3.38 0.001 .4598196 1.730697
20 | 1.000613 .3068454 3.26 0.001 .398602 1.602624
21 | .9262332 .3196484 2.90 0.004 .2991034 1.553363
22 | 1.39153 .3085047 4.51 0.000 .7862636 1.996797
23 | 1.359676 .3338178 4.07 0.000 .7047464 2.014605
24 | 1.211177 .310887 3.90 0.000 .6012366 1.821118
25 | 1.712215 .3362546 5.09 0.000 1.052505 2.371925
26 | 1.566263 .2967141 5.28 0.000 .9841293 2.148398
27 | 1.498822 .3233683 4.64 0.000 .864394 2.13325
28 | 1.808706 .37417 4.83 0.000 1.074609 2.542804
29 | 1.988294 .3171807 6.27 0.000 1.366005 2.610582
|
husband_fininf#husband_cr |
1 6 | 9.443411 .6345768 14.88 0.000 8.198412 10.68841
1 10 | 4.301232 .6198205 6.94 0.000 3.085183 5.51728
1 11 | 5.238215 .5630549 9.30 0.000 4.133537 6.342893
1 12 | 4.193574 .9276966 4.52 0.000 2.373492 6.013655
1 13 | 4.449591 .6655328 6.69 0.000 3.143859 5.755324
1 14 | 4.671867 .5661592 8.25 0.000 3.561099 5.782635
1 15 | 4.988248 .5607673 8.90 0.000 3.888059 6.088438
1 16 | 4.430131 .6041316 7.33 0.000 3.244863 5.615398
1 17 | 5.182295 .5582485 9.28 0.000 4.087047 6.277543
1 18 | 5.02207 .5115426 9.82 0.000 4.018456 6.025684
1 19 | 4.779548 .5153186 9.27 0.000 3.768526 5.79057
1 20 | 4.672066 .4241354 11.02 0.000 3.83994 5.504193
1 21 | 5.049 .4419093 11.43 0.000 4.182002 5.915998
1 22 | 4.50194 .4056876 11.10 0.000 3.706007 5.297873
1 23 | 4.69598 .468935 10.01 0.000 3.77596 5.616001
1 24 | 5.198496 .4289527 12.12 0.000 4.356918 6.040074
1 25 | 4.496101 .4326663 10.39 0.000 3.647237 5.344965
1 26 | 4.891119 .3873849 12.63 0.000 4.131095 5.651144
1 27 | 4.450895 .4210476 10.57 0.000 3.624826 5.276963
1 28 | 4.308915 .445484 9.67 0.000 3.434904 5.182926
1 29 | 4.518423 .4091807 11.04 0.000 3.715636 5.321209
|
husband_mp | .0725569 .0114523 6.34 0.000 .0500882 .0950256
husband_cmt | .079294 .007573 10.47 0.000 .0644363 .0941516
prb8p2_w6 | -.071961 .0723069 -1.00 0.320 -.2138226 .0699006
husband_race | -.0640938 .0282692 -2.27 0.024 -.1195562 -.0086314
husband_religion | -.0205083 .0102404 -2.00 0.045 -.0405994 -.0004173
husband_edu | -.0883359 .0278685 -3.17 0.002 -.143012 -.0336597
husband_work | .0072213 .0256229 0.28 0.778 -.0430493 .0574919
husband_income | -.0059427 .0107708 -0.55 0.581 -.0270743 .0151889
--------------------------+----------------------------------------------------------------
/cut_1_1 | 6.549065 .2798637 23.40 0.000 5.99999 7.09814
/cut_1_2 | 6.802506 .2570238 26.47 0.000 6.298242 7.30677
/cut_1_3 | 6.906985 .2456882 28.11 0.000 6.424961 7.38901
/cut_1_4 | 7.533448 .1933042 38.97 0.000 7.154198 7.912699
/cut_1_5 | 7.853433 .1570681 50.00 0.000 7.545275 8.16159
/cut_1_6 | 8.433101 .1292298 65.26 0.000 8.17956 8.686642
/cut_1_7 | 8.554424 .1141611 74.93 0.000 8.330447 8.778401
/cut_1_8 | 8.766625 .1064207 82.38 0.000 8.557834 8.975415
/cut_1_9 | 9.0919 .078695 115.53 0.000 8.937506 9.246295
/cut_1_10 | 9.356956 .0732434 127.75 0.000 9.213257 9.500655
/cut_1_11 | 9.516326 .0645774 147.36 0.000 9.389629 9.643023
/cut_1_12 | 9.877443 .0592259 166.78 0.000 9.761246 9.99364
/cut_1_13 | 10.36697 .0610518 169.81 0.000 10.24719 10.48675
/cut_1_14 | 10.77941 .0529587 203.54 0.000 10.67551 10.88331
/cut_1_15 | 11.05626 .0510384 216.63 0.000 10.95612 11.15639
/cut_1_16 | 11.38935 .0518334 219.73 0.000 11.28766 11.49105
/cut_1_17 | 11.75172 .054696 214.86 0.000 11.64441 11.85903
/cut_1_18 | 12.16083 .0587889 206.86 0.000 12.04549 12.27617
/cut_1_19 | 12.49868 .0636536 196.35 0.000 12.37379 12.62356
/cut_1_20 | 12.8212 .0695179 184.43 0.000 12.68481 12.95759
/cut_1_21 | 13.95337 .106686 130.79 0.000 13.74406 14.16268
/cut_2_1 | .9714216 .3779561 2.57 0.010 .2298957 1.712947
/cut_2_2 | 1.399563 .3469212 4.03 0.000 .7189262 2.080201
/cut_2_3 | 1.773911 .3699576 4.79 0.000 1.048077 2.499744
/cut_2_4 | 2.135802 .3330923 6.41 0.000 1.482296 2.789308
/cut_2_5 | 2.281279 .3351432 6.81 0.000 1.62375 2.938809
/cut_2_6 | 2.439405 .3435383 7.10 0.000 1.765405 3.113405
/cut_2_7 | 2.590713 .3479788 7.45 0.000 1.908001 3.273426
/cut_2_8 | 2.827636 .3538327 7.99 0.000 2.133439 3.521833
/cut_2_9 | 3.463741 .3814875 9.08 0.000 2.715287 4.212196
/cut_2_10 | 3.759348 .3847036 9.77 0.000 3.004584 4.514111
/cut_2_11 | 3.982628 .3838137 10.38 0.000 3.22961 4.735646
/cut_2_12 | 4.319697 .3876242 11.14 0.000 3.559203 5.080191
/cut_2_13 | 4.828428 .3957337 12.20 0.000 4.052024 5.604832
/cut_2_14 | 5.133628 .4042446 12.70 0.000 4.340526 5.92673
/cut_2_15 | 5.39824 .4068624 13.27 0.000 4.600001 6.196478
/cut_2_16 | 5.808665 .4103875 14.15 0.000 5.003511 6.613819
/cut_2_17 | 6.074721 .4143376 14.66 0.000 5.261817 6.887625
/cut_2_18 | 6.543236 .419843 15.58 0.000 5.71953 7.366941
/cut_2_19 | 6.859554 .4239055 16.18 0.000 6.027878 7.691229
/cut_2_20 | 7.155699 .4287537 16.69 0.000 6.314512 7.996887
/cut_2_21 | 8.177527 .4405744 18.56 0.000 7.313148 9.041906
/atanhrho_12 | .35454 .0469131 7.56 0.000 .2624994 .4465805
-------------------------------------------------------------------------------------------
My questions are:
1) Do I need to run margins on this to interpret it properly? Or do I just look at the co-efficients and p-values?
2) Is the correct interpretation, for example on the variable "wife_cr", that a level 6 results in a 5.27523 increase in wife_rs?
Thank you so much!!
Comment