.
. pwd
/Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/H1pt1/work

. cd /Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/H1pt1/work
/Users/robertyaffee/Documents/data/research/chwk/phase3/Htests/H1tests/H1pt1/work

.
. use chwide27june2012, clear
(Zero for missing on all icdx)

.
. // Hypothesis 1 part 1 female WHPsleep analysis
. // Analyzing the Dose sleep psychological response for women
.
. cap gen bffsl1 = max(0, BSIposymp - 50)

. label var bffsl1 "max(0, BSIposymp-15)"

.
.
. cap gen bffsl2 = max(0, emplw25 + 1.14467e-009)

. label var bffsl2 " max(0, emplw25 + 1.14467e-009)"

.
.
. cap gen bffsl3 = max(0, deaw1 - 1)* bffsl1

. label var bffsl3 "max(, deaw1 - 1)* bffsl1

.
. cap gen bffsl4 = max(0,1 - deaw1) * bffsl1

. label var bffsl4 "max(0,1- deaw1) * bffsl1

.
. cap gen bffsl5 = (docvisitsw1 == . )* bffsl4

. label var bffsl5 "(docvisitsw1 = . )* bffsl4"

.
.
. cap gen bffsl6 = (docvisitsw1 != . )*bffsl4

. label var bffsl6 "(docvisitsw1 != . )*bffsl4"

.
.
. cap gen bffsl7 = max(0, airw2-10)*bffsl5

. label var bffsl7 "max(0, airw2-10)*bffsl5"

.
. cap gen bffsl8 = max(0, 10-airw2)*bffsl5

. label var bffsl8 "max(0, 10- airw2 )*bffsl5"

.
. cap gen bffsl9 = max(0, BSIhos - 9)*bffsl7

. label var bffsl9 "max(0, BSIhos - 9)*bffsl7"

.
. cap gen bffsl10 = max(0, 9 - BSIhos) * bffsl7

. label var bffsl10 "max(0, 9 - BSIhos) * bffsl7"

.
.
. cap gen bffsl11 =  max(0, cloud - 10) * bffsl6

. label var bffsl11 " max(0, cloud - 10) * bffsl6"

.
.
. cap gen bffsl12 = max(0, 10 - cloud) * bffsl6

. label var bffsl12 "max(0, 10-cloud)*bffsl6

.
. cap gen bffsl13 = max(0, airw3 - 20) * bffsl8

.  label var bffsl13 "max(0, airw3 - 20) * bffsl8"

.
.
. cap gen bffsl15 = (suchrw1 != . ) * bffsl8

.  label var bffsl15 "(suchrw1 != . ) * bffsl8"

.
. cap gen bffsl17 = max(0, suchrw1 + 1.85275e-007) * bffsl15

.  label var bffsl17 "max(0, suchrw1 + 1.85275e-007) * bffsl15"

.
. cap gen bffsl18 = (trgovw2 != . ) * bffsl1

.  label var bffsl18 "(trgovw2 != . ) * bffsl1"

.
. cap gen bffsl19 = (trgovw2 == . ) * bffsl1

.  label var bffsl19 "max(0, trgovw2 - 10) * bffsl18"

.
. cap gen bffsl20 = max(0, trgovw2 - 10) * bffsl18

.  label var bffsl20 "max(0, trgovw2 - 10) * bffsl18"

.
. cap gen bffsl23 = max(0, 70 - carcin) * bffsl19

.  label var bffsl23 "max(0, trgovw2 - 10) * bffsl18"

.
. cap gen bffsl24 = max(0, phlthw3 - 40) * bffsl18

.  label var bffsl24 "max(0, phlthw3 - 40) * bffsl18"

.
. cap gen bffsl25 = max(0, 40 - phlthw3) * bffsl18

. label var bffsl25 " max(0, 40 - phlthw3) * bffsl18"

.
.
. //  wave 1 possible socio-demog covariates full Female model
. regress WHPsleep age educ2-educ7 marrw11-marrw13 marrw15 childw1 ///
>   emplw12-emplw16 occ1w1-occ8w1 inc1w1-inc4w1 if gender==2

      Source |       SS       df       MS              Number of obs =     361
-------------+------------------------------           F( 29,   331) =    3.69
       Model |  84294.4815    29  2906.70626           Prob > F      =  0.0000
    Residual |  260532.467   331  787.107151           R-squared     =  0.2445
-------------+------------------------------           Adj R-squared =  0.1783
       Total |  344826.948   360  957.852634           Root MSE      =  28.055

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    1.248678    .2311643      5.40    0.000      .7939412     1.703414
       educ2 |    21.50202    29.52348      0.73    0.467     -36.57529     79.57934
       educ3 |    22.85261    29.29409      0.78    0.436     -34.77345     80.47868
       educ4 |      20.348    29.79995      0.68    0.495     -38.27318     78.96917
       educ5 |    11.43302    29.53251      0.39    0.699     -46.66205     69.52809
       educ6 |    10.09879    29.26331      0.35    0.730     -47.46674     67.66431
       educ7 |   -6.701838    35.50365     -0.19    0.850     -76.54309     63.13942
     marrw11 |   -10.39762    9.873073     -1.05    0.293     -29.81951     9.024261
     marrw12 |    16.07669    16.91172      0.95    0.342     -17.19132     49.34471
     marrw13 |   -11.39635    9.228235     -1.23    0.218     -29.54974     6.757033
     marrw15 |   -7.329604    15.62591     -0.47    0.639     -38.06823     23.40902
     childw1 |   -.0715685    2.572531     -0.03    0.978     -5.132141     4.989004
     emplw12 |   -4.156287    11.34617     -0.37    0.714     -26.47599     18.16342
     emplw13 |    8.939886    13.00345      0.69    0.492     -16.63993     34.51971
     emplw14 |    16.63853    30.23958      0.55    0.583     -42.84746     76.12451
     emplw15 |   -14.50791     18.7078     -0.78    0.439     -51.30909     22.29328
     emplw16 |    9.030303    11.79844      0.77    0.445     -14.17908     32.23969
      occ1w1 |    2.063761    12.04198      0.17    0.864     -21.62471     25.75223
      occ2w1 |   -5.777501     12.7412     -0.45    0.651     -30.84145     19.28645
      occ3w1 |    3.442475    12.64437      0.27    0.786     -21.43099     28.31594
      occ4w1 |   -.4413774    13.62519     -0.03    0.974     -27.24427     26.36151
      occ5w1 |    5.231769    14.52209      0.36    0.719     -23.33546       33.799
      occ6w1 |    14.33591    15.13781      0.95    0.344     -15.44253     44.11436
      occ7w1 |    -.802484    13.46626     -0.06    0.953     -27.29272     25.68776
      occ8w1 |    5.230194    12.89959      0.41    0.685     -20.14533     30.60571
      inc1w1 |   -6.777508    11.65228     -0.58    0.561     -29.69938     16.14436
      inc2w1 |   -8.164289    11.14255     -0.73    0.464     -30.08344     13.75486
      inc3w1 |    .5779226     11.4058      0.05    0.960     -21.85907     23.01492
      inc4w1 |   -.9748117    13.05518     -0.07    0.941     -26.65639     24.70677
       _cons |   -39.76064    33.69164     -1.18    0.239     -106.0374     26.51611
-------------+----------------------------------------------------------------

.
.
. set more off

. des WHPsleep age educ2-educ7 marrw11-marrw13 marrw15 childw1 ///
>   emplw12-emplw16 occ1w1-occ8w1 inc1w1-inc4w1 radhlw1 radchw1 ///
>   radtlw1 havmil bffsl2-bffsl25

              storage  display     value
variable name   type   format      label      variable label
-------------


WHPsleep          double   %9.0g                        Wtd Health Profile Sleep Pt 1 subscale


age               byte     %8.0g                    *   Respondent's age


educ2             byte     %8.0g                        educ==2. graduated high school


educ3             byte     %8.0g                        educ==3. technical degree


educ4             byte     %8.0g                        educ==4. did not finish college/bachelor's


educ5             byte     %8.0g                        educ==5. graduated college/bachelor's


educ6             byte     %8.0g                        educ==6. finished specialist/master's degree


educ7             byte     %8.0g                        educ==7. doctor of science/phd


marrw11           byte     %8.0g                        marrw1==1. single


marrw12           byte     %8.0g                        marrw1==2. cohabitating


marrw13           byte     %8.0g                        marrw1==3. married


marrw15           byte     %8.0g                        marrw1==5. divorced


childw1           byte     %8.0g                        number of children in 1986


emplw12           byte     %8.0g                        emplw1==1. full time


emplw13           byte     %8.0g                        emplw1==2. part time


emplw14           byte     %8.0g                        emplw1==3. voluntary


emplw15           byte     %8.0g                        emplw1==4. retired


emplw16           byte     %8.0g                        emplw1==5. unemployed


occ1w1            byte     %15.0g        LABJ           profess executive administration in 1986


occ2w1            byte     %15.0g        LABJ           technical sales admin support  in 1986


occ3w1            byte     %15.0g        LABJ           service occup protective services  in 1986


occ4w1            byte     %15.0g        LABJ           precision prod mechan craft construction  in 1986


occ5w1            byte     %15.0g        LABJ           factory laborer machinist transp cleaner  in 1986


occ6w1            byte     %15.0g        LABJ           farming agricul forestry fishing trapping logging  in 1986


occ7w1            byte     %15.0g        LABJ           homemaking or caregiving  in 1986


occ8w1            byte     %15.0g        LABJ           student  in 1986


inc1w1            byte     %15.0g        LABJ           Income is not sufficient for basic neccessities in 1986


inc2w1            byte     %15.0g        LABJ           Income is just sufficient for basic neccessities in 1986


inc3w1            byte     %15.0g        LABJ           Income is sufficient for basics plus extra purchases/savings in 1986


inc4w1            byte     %15.0g        LABJ           Income allows to comfortably afford luxury items in 1986


radhlw1           byte     %8.0g                        Self-perceived Chornobyl health threat in wave 1


radchw1           byte     %8.0g                        believed % of polution related to chornobyl in 1986


radtlw1           byte     %8.0g                        believed % of cumulative radiation exposed to in a lifetime in 1986


havmil            double   %9.0g                        Distance from Chornobyl in miles


bffsl2            float    %9.0g                         max(0, emplw25 + 1.14467e-009)


bffsl3            float    %9.0g                        max(, deaw1 - 1)* bffsl1


bffsl4            float    %9.0g                        max(0,1- deaw1) * bffsl1


bffsl5            float    %9.0g                        (docvisitsw1 = . )* bffsl4


bffsl6            float    %9.0g                        (docvisitsw1 != . )*bffsl4


bffsl7            float    %9.0g                        max(0, airw2-10)*bffsl5


bffsl8            float    %9.0g                        max(0, 10- airw2 )*bffsl5


bffsl9            float    %9.0g                        max(0, BSIhos - 9)*bffsl7


bffsl10           float    %9.0g                        max(0, 9 - BSIhos) * bffsl7


bffsl11           float    %9.0g                         max(0, cloud - 10) * bffsl6


bffsl12           float    %9.0g                        max(0, 10-cloud)*bffsl6


bffsl13           float    %9.0g                        max(0, airw3 - 20) * bffsl8


bffsl15           float    %9.0g                        (suchrw1 != . ) * bffsl8


bffsl17           float    %9.0g                        max(0, suchrw1 + 1.85275e-007) * bffsl15


bffsl18           float    %9.0g                        (trgovw2 != . ) * bffsl1


bffsl19           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


bffsl20           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


bffsl23           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


bffsl24           float    %9.0g                        max(0, phlthw3 - 40) * bffsl18


bffsl25           float    %9.0g                         max(0, 40 - phlthw3) * bffsl18


.
.
. local bfw1 bffsl3 bffsl4 bffsl5 bffsl6 bffsl9 bffsl10 bffsl11 bffsl12 bffsl15 ///
>    bffsl17

.
. local myvarlist age educ2-educ7 marrw11-marrw13 marrw15 childw1 ///
>   emplw12-emplw16 occ1w1-occ8w1 inc1w1-inc4w1 radhlw1 radchw1 ///
>   radtlw1 havmil

.
.
.
.
. set more off

. regress WHPsleep age avgcumdosew1 educ2-educ7 marrw11-marrw13 marrw15 childw1 ///
>   emplw12-emplw16 occ1w1-occ8w1 inc1w1-inc4w1 radhlw1 radchw1 ///
>   radtlw1 havmil bffsl2 bffsl5 bffsl8 bffsl9 bffsl19 ///
>   bffsl23  if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     360
                                                       F( 38,   359) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.4129
                                                       Root MSE      =  25.186

(Std. Err. adjusted for 360 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7122626    .2377661      3.00    0.003      .2446732     1.179852
avgcumdosew1 |    4.402951    3.213039      1.37    0.171     -1.915792     10.72169
       educ2 |    23.96428    9.057635      2.65    0.009      6.151586     41.77696
       educ3 |    25.04783    8.599944      2.91    0.004      8.135235     41.96043
       educ4 |    27.49574     10.0494      2.74    0.007      7.732645     47.25884
       educ5 |    15.24254    9.024191      1.69    0.092     -2.504382     32.98945
       educ6 |    15.84262    8.629518      1.84    0.067     -1.128137     32.81338
       educ7 |   -8.284899    16.47217     -0.50    0.615     -40.67897     24.10917
     marrw11 |   -11.60127    10.42524     -1.11    0.267     -32.10349     8.900952
     marrw12 |    11.09668    25.45493      0.44    0.663     -38.96282     61.15618
     marrw13 |    -14.9945    11.00887     -1.36    0.174     -36.64448     6.655475
     marrw15 |   -19.27155    15.01928     -1.28    0.200     -48.80839     10.26528
     childw1 |    .9042602    2.574491      0.35    0.726     -4.158718     5.967238
     emplw12 |   -2.664569     8.94748     -0.30    0.766     -20.26063     14.93149
     emplw13 |    9.786761    9.975251      0.98    0.327     -9.830508     29.40403
     emplw14 |    16.03531    9.310385      1.72    0.086      -2.27444     34.34505
     emplw15 |   -15.79561    13.43756     -1.18    0.241     -42.22183      10.6306
     emplw16 |    4.267288    9.208999      0.46    0.643     -13.84307     22.37765
      occ1w1 |    9.696312    8.066789      1.20    0.230     -6.167785     25.56041
      occ2w1 |    .6690337    9.174688      0.07    0.942     -17.37385     18.71192
      occ3w1 |    8.561839    9.437804      0.91    0.365      -9.99849     27.12217
      occ4w1 |    5.544649    9.598896      0.58    0.564     -13.33248     24.42178
      occ5w1 |    7.115806    10.34157      0.69    0.492     -13.22186     27.45347
      occ6w1 |    13.35849    13.23559      1.01    0.314     -12.67054     39.38752
      occ7w1 |    5.014871    9.157328      0.55    0.584     -12.99387     23.02362
      occ8w1 |    7.199668    8.551695      0.84    0.400     -9.618044     24.01738
      inc1w1 |   -1.109941    7.938776     -0.14    0.889     -16.72229     14.50241
      inc2w1 |   -3.480271    7.486286     -0.46    0.642     -18.20276     11.24221
      inc3w1 |    4.481171    7.769263      0.58    0.564     -10.79781     19.76016
      inc4w1 |    1.040991    8.996699      0.12    0.908     -16.65186     18.73384
     radhlw1 |    .0663636     .041918      1.58    0.114     -.0160721     .1487993
     radchw1 |   -.0568367    .0478033     -1.19    0.235     -.1508465      .037173
     radtlw1 |    .0173754    .0500737      0.35    0.729     -.0810992     .1158501
      havmil |    .0072049    .0066053      1.09    0.276      -.005785     .0201948
      bffsl2 |    16.55423    7.246191      2.28    0.023      2.303919     30.80455
      bffsl5 |    .5010157    .1183031      4.24    0.000      .2683615     .7336699
      bffsl8 |   -.0028871    .0123049     -0.23    0.815     -.0270859     .0213117
      bffsl9 |    .0001223    .0017946      0.07    0.946     -.0034069     .0036515
     bffsl19 |    .3275911    .1152007      2.84    0.005       .101038     .5541442
     bffsl23 |    .0034286    .0022857      1.50    0.134     -.0010665     .0079236
       _cons |   -36.50518    16.49291     -2.21    0.027     -68.94004     -4.07032
-------------+----------------------------------------------------------------

.
.
.  scalar fw1 = e(r2_a)

.  scalar list fw1
       fw1 =   .3392537

.
. des age educ2-educ6 bffsl1 bffsl4 bffsl5  inc3w1 radhlw1 ///
>      radchw1 emplw14  bffsl5 bffsl19

              storage  display     value
variable name   type   format      label      variable label
-------------


age               byte     %8.0g                    *   Respondent's age


educ2             byte     %8.0g                        educ==2. graduated high school


educ3             byte     %8.0g                        educ==3. technical degree


educ4             byte     %8.0g                        educ==4. did not finish college/bachelor's


educ5             byte     %8.0g                        educ==5. graduated college/bachelor's


educ6             byte     %8.0g                        educ==6. finished specialist/master's degree


bffsl1            float    %9.0g                        max(0, BSIposymp-15)


bffsl4            float    %9.0g                        max(0,1- deaw1) * bffsl1


bffsl5            float    %9.0g                        (docvisitsw1 = . )* bffsl4


inc3w1            byte     %15.0g        LABJ           Income is sufficient for basics plus extra purchases/savings in 1986


radhlw1           byte     %8.0g                        Self-perceived Chornobyl health threat in wave 1


radchw1           byte     %8.0g                        believed % of polution related to chornobyl in 1986


emplw14           byte     %8.0g                        emplw1==3. voluntary


bffsl5            float    %9.0g                        (docvisitsw1 = . )* bffsl4


bffsl19           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


. regress WHPsleep age educ2-educ7 marrw11-marrw13 marrw15 childw1 ///
>   emplw12-emplw16 occ1w1-occ8w1 inc1w1-inc4w1 radhlw1 radchw1 ///
>   radtlw1 havmil bffsl1 bffsl4 bffsl5 bffsl8 bffsl9  ///
>   bffsl23  if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     360
                                                       F( 37,   359) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.5109
                                                       Root MSE      =  22.951

(Std. Err. adjusted for 360 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7121932    .2002559      3.56    0.000      .3183711     1.106015
       educ2 |    14.76953    8.105259      1.82    0.069      -1.17022     30.70929
       educ3 |    15.20301    7.631227      1.99    0.047      .1954881     30.21054
       educ4 |    15.11164    8.140879      1.86    0.064     -.8981598     31.12145
       educ5 |    7.338391    7.848357      0.94    0.350     -8.096141     22.77292
       educ6 |    8.917335    7.277276      1.23    0.221     -5.394112     23.22878
       educ7 |   -4.640547    8.965795     -0.52    0.605     -22.27263     12.99153
     marrw11 |   -2.811583    8.047936     -0.35    0.727     -18.63861     13.01544
     marrw12 |     15.8409    19.65638      0.81    0.421     -22.81523     54.49702
     marrw13 |   -7.675459    8.525026     -0.90    0.369     -24.44072     9.089806
     marrw15 |   -13.11814    11.41054     -1.15    0.251     -35.55803      9.32175
     childw1 |     .746567    2.497417      0.30    0.765     -4.164837     5.657971
     emplw12 |   -5.260032     7.57252     -0.69    0.488      -20.1521      9.63204
     emplw13 |    6.904821    8.648243      0.80    0.425     -10.10276      23.9124
     emplw14 |    12.62199    7.936775      1.59    0.113      -2.98642     28.23041
     emplw15 |   -5.092936     10.5647     -0.48    0.630     -25.86941     15.68353
     emplw16 |    -.711606    7.775104     -0.09    0.927     -16.00208     14.57887
      occ1w1 |     1.45034    8.433231      0.17    0.864      -15.1344     18.03508
      occ2w1 |   -4.867646    8.651217     -0.56    0.574     -21.88108     12.14578
      occ3w1 |    2.770103    9.033901      0.31    0.759     -14.99591     20.53612
      occ4w1 |   -3.687963    9.865869     -0.37    0.709     -23.09012      15.7142
      occ5w1 |   -4.673377    10.49458     -0.45    0.656     -25.31196      15.9652
      occ6w1 |    13.92021    12.13427      1.15    0.252     -9.942976      37.7834
      occ7w1 |    1.195388    8.492183      0.14    0.888     -15.50529     17.89606
      occ8w1 |    4.575658    8.523953      0.54    0.592      -12.1875     21.33881
      inc1w1 |   -5.743878    8.008077     -0.72    0.474     -21.49251     10.00476
      inc2w1 |   -5.348253    7.389777     -0.72    0.470     -19.88094     9.184439
      inc3w1 |    3.335976     7.79088      0.43    0.669     -11.98552     18.65747
      inc4w1 |   -1.491376    8.272616     -0.18    0.857     -17.76025      14.7775
     radhlw1 |    .0382992    .0385424      0.99    0.321      -.037498     .1140964
     radchw1 |   -.0625732    .0430319     -1.45    0.147     -.1471995     .0220531
     radtlw1 |    .0584122    .0468509      1.25    0.213     -.0337245      .150549
      havmil |    .0063842    .0060976      1.05    0.296     -.0056073     .0183757
      bffsl1 |    .6467535    .0838632      7.71    0.000      .4818286     .8116783
      bffsl4 |   -.2500661    .0872125     -2.87    0.004     -.4215777    -.0785546
      bffsl5 |    .4669187    .1163002      4.01    0.000      .2382034     .6956339
      bffsl8 |   -.0160707    .0120767     -1.33    0.184     -.0398206     .0076792
      bffsl9 |   -.0010516    .0017337     -0.61    0.545     -.0044611      .002358
     bffsl23 |    .0065939    .0021675      3.04    0.003      .0023314     .0108564
       _cons |   -36.61183    14.86716     -2.46    0.014     -65.84948    -7.374165
-------------+----------------------------------------------------------------

.
. // selected as significant for females wave one are posymp deaw1 docvisitsw1 ttrgovw2
. des bffsl1 bffsl4 bffsl5 bffsl18 bffsl23   // there is a cross wave interaction from

              storage  display     value
variable name   type   format      label      variable label
-------------


bffsl1            float    %9.0g                        max(0, BSIposymp-15)


bffsl4            float    %9.0g                        max(0,1- deaw1) * bffsl1


bffsl5            float    %9.0g                        (docvisitsw1 = . )* bffsl4


bffsl18           float    %9.0g                        (trgovw2 != . ) * bffsl1


bffsl23           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


. // wave 1 to wave 2 here on trust govert and BSIposymptoms
. // model
. regress WHPsleep age  ///
>   radchw1 radhlw1 havmil avgcumdosew1 ///
>   bffsl1 bffsl4 bffsl5 bffsl8 bffsl9 illw1 bffsl23  if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     362
                                                       F( 12,   361) =   33.22
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.4549
                                                       Root MSE      =  23.231

(Std. Err. adjusted for 362 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .4915241    .1127291      4.36    0.000      .2698358     .7132123
     radchw1 |   -.0388423    .0322386     -1.20    0.229     -.1022413     .0245567
     radhlw1 |     .041149    .0341087      1.21    0.228     -.0259277     .1082256
      havmil |    .0050185    .0063396      0.79    0.429     -.0074487     .0174857
avgcumdosew1 |   -.5204685    2.075005     -0.25    0.802     -4.601083     3.560146
      bffsl1 |      .68168      .08899      7.66    0.000      .5066762     .8566839
      bffsl4 |    -.281887    .0883738     -3.19    0.002     -.4556791    -.1080949
      bffsl5 |    .4842357     .110233      4.39    0.000      .2674562     .7010151
      bffsl8 |    -.018989    .0119334     -1.59    0.112     -.0424566     .0044787
      bffsl9 |    -.001293    .0015101     -0.86    0.392     -.0042626     .0016766
       illw1 |    .3227618      2.9526      0.11    0.913     -5.483694     6.129218
     bffsl23 |    .0075375    .0019824      3.80    0.000       .003639     .0114359
       _cons |   -20.97236     5.60273     -3.74    0.000     -31.99045    -9.954272
-------------+----------------------------------------------------------------

.
. // female dose=> sleep impact washes out
.
. // wave 1 trimmed model of possible socio-demog covariates Female model
. set more off

. sw, pr(.05):  regress WHPsleep age educ2-educ7 marrw11-marrw13 marrw15 childw1 ///
>   emplw12-emplw16 occ1w1-occ8w1 inc1w1-inc4w1 if gender==2


  begin with full model


p = 0.9778 >= 0.0500  removing childw1


p = 0.9745 >= 0.0500  removing occ4w1


p = 0.9674 >= 0.0500  removing inc3w1


p = 0.9688 >= 0.0500  removing occ7w1


p = 0.8502 >= 0.0500  removing educ7


p = 0.8336 >= 0.0500  removing inc4w1


p = 0.7032 >= 0.0500  removing emplw12


p = 0.6815 >= 0.0500  removing occ1w1


p = 0.6598 >= 0.0500  removing occ3w1


p = 0.6932 >= 0.0500  removing occ5w1


p = 0.6010 >= 0.0500  removing occ8w1


p = 0.5856 >= 0.0500  removing marrw15


p = 0.5158 >= 0.0500  removing emplw15


p = 0.4673 >= 0.0500  removing emplw14


p = 0.4385 >= 0.0500  removing marrw11


p = 0.3488 >= 0.0500  removing educ6


p = 0.7818 >= 0.0500  removing educ5


p = 0.3279 >= 0.0500  removing marrw13


p = 0.2663 >= 0.0500  removing inc1w1


p = 0.2261 >= 0.0500  removing occ6w1


p = 0.1440 >= 0.0500  removing occ2w1


p = 0.1240 >= 0.0500  removing educ4


p = 0.0844 >= 0.0500  removing emplw13


p = 0.0545 >= 0.0500  removing inc2w1


      Source |       SS       df       MS              Number of obs =     361
-------------+------------------------------           F(  5,   355) =   18.09
       Model |  70008.8769     5  14001.7754           Prob > F      =  0.0000
    Residual |  274818.071   355  774.135412           R-squared     =  0.2030
-------------+------------------------------           Adj R-squared =  0.1918
       Total |  344826.948   360  957.852634           Root MSE      =  27.823

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    1.043262    .1444159      7.22    0.000      .7592432      1.32728
       educ2 |    12.33292    6.185662      1.99    0.047      .1677718     24.49807
       educ3 |    11.08882    3.145775      3.52    0.000      4.902127     17.27552
     emplw16 |    10.50336     4.06107      2.59    0.010      2.516582     18.49014
     marrw12 |    32.15646    14.04846      2.29    0.023      4.527801     59.78513
       _cons |   -33.40629    7.664965     -4.36    0.000     -48.48073    -18.33184
-------------+----------------------------------------------------------------

.
. // only by a stepwise removal is the dose-sleep impact retained as significant
. set more off

. sw, pr(.05): regress WHPsleep age educ2-educ7 marrw11-marrw13 marrw15 childw1 ///
>   emplw12-emplw16 occ1w1-occ8w1 inc1w1-inc4w1 radhlw1 radchw1 ///
>   radtlw1 illw1 havmil avgcumdosew1 if gender==2


  begin with full model


p = 0.9937 >= 0.0500  removing inc4w1


p = 0.9766 >= 0.0500  removing childw1


p = 0.9655 >= 0.0500  removing inc3w1


p = 0.9760 >= 0.0500  removing occ4w1


p = 0.9353 >= 0.0500  removing emplw12


p = 0.9193 >= 0.0500  removing radtlw1


p = 0.9059 >= 0.0500  removing occ7w1


p = 0.7737 >= 0.0500  removing educ7


p = 0.7855 >= 0.0500  removing occ1w1


p = 0.7270 >= 0.0500  removing occ5w1


p = 0.6157 >= 0.0500  removing occ3w1


p = 0.5610 >= 0.0500  removing marrw15


p = 0.5191 >= 0.0500  removing occ8w1


p = 0.5229 >= 0.0500  removing emplw15


p = 0.4577 >= 0.0500  removing illw1


p = 0.4344 >= 0.0500  removing marrw11


p = 0.3248 >= 0.0500  removing emplw14


p = 0.2889 >= 0.0500  removing marrw13


p = 0.3256 >= 0.0500  removing educ5


p = 0.7616 >= 0.0500  removing educ6


p = 0.3003 >= 0.0500  removing havmil


p = 0.2657 >= 0.0500  removing occ6w1


p = 0.2773 >= 0.0500  removing inc1w1


p = 0.1466 >= 0.0500  removing radchw1


p = 0.1929 >= 0.0500  removing radhlw1


p = 0.1255 >= 0.0500  removing occ2w1


p = 0.1191 >= 0.0500  removing educ4


p = 0.1060 >= 0.0500  removing emplw13


p = 0.0686 >= 0.0500  removing educ2


      Source |       SS       df       MS              Number of obs =     360
-------------+------------------------------           F(  6,   353) =   16.23
       Model |  74510.5445     6  12418.4241           Prob > F      =  0.0000
    Residual |  270125.843   353  765.229018           R-squared     =  0.2162
-------------+------------------------------           Adj R-squared =  0.2029
       Total |  344636.388   359  959.989938           Root MSE      =  27.663

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    1.072342    .1400676      7.66    0.000      .7968704     1.347814
avgcumdosew1 |    6.779359    2.699578      2.51    0.012      1.470079     12.08864
       educ3 |    9.831739    3.058298      3.21    0.001      3.816964     15.84651
     marrw12 |    30.58917    13.95631      2.19    0.029      3.141208     58.03714
     emplw16 |    11.12886    4.056174      2.74    0.006      3.151559     19.10617
      inc2w1 |   -6.038637    2.982243     -2.02    0.044     -11.90384     -.173439
       _cons |   -33.56019    7.664829     -4.38    0.000     -48.63466    -18.48572
-------------+----------------------------------------------------------------

.
. set more off //  Full model has dose=>sleep female impact

. regress WHPsleep age educ2-educ7 marrw11-marrw13 marrw15 childw1 ///
>   emplw12-emplw16 occ1w1-occ8w1 inc1w1-inc4w1 radhlw1 radchw1 ///
>   radtlw1 illw1 havmil avgcumdosew1 if gender==2

      Source |       SS       df       MS              Number of obs =     360
-------------+------------------------------           F( 35,   324) =    3.40
       Model |  92655.0478    35  2647.28708           Prob > F      =  0.0000
    Residual |   251981.34   324  777.720185           R-squared     =  0.2688
-------------+------------------------------           Adj R-squared =  0.1899
       Total |  344636.388   359  959.989938           Root MSE      =  27.888

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    1.137842    .2331707      4.88    0.000      .6791225     1.596562
       educ2 |    18.96378    29.47888      0.64    0.520      -39.0304     76.95797
       educ3 |    22.36607    29.23033      0.77    0.445     -35.13913     79.87127
       educ4 |    20.88552    29.68071      0.70    0.482     -37.50572     79.27676
       educ5 |    10.01882    29.45126      0.34    0.734     -47.92102     67.95865
       educ6 |    9.774852    29.18201      0.33    0.738     -47.63528     67.18499
       educ7 |   -10.18774    35.46344     -0.29    0.774     -79.95543     59.57994
     marrw11 |   -11.99451    10.10951     -1.19    0.236     -31.88309     7.894062
     marrw12 |    14.06115    16.96986      0.83    0.408     -19.32387     47.44617
     marrw13 |   -12.86976    9.396172     -1.37    0.172     -31.35497      5.61545
     marrw15 |   -8.447576    15.63467     -0.54    0.589     -39.20586      22.3107
     childw1 |   -.0753986    2.561098     -0.03    0.977     -5.113879     4.963082
     emplw12 |   -.8787865    11.79831     -0.07    0.941     -24.08975     22.33218
     emplw13 |    10.77238    13.37076      0.81    0.421     -15.53208     37.07685
     emplw14 |    27.25469    30.61401      0.89    0.374     -32.97265     87.48203
     emplw15 |   -11.73205    18.95279     -0.62    0.536     -49.01811     25.55401
     emplw16 |    11.30623    12.13703      0.93    0.352      -12.5711     35.18356
      occ1w1 |    1.021791    12.23756      0.08    0.934     -23.05332      25.0969
      occ2w1 |    -6.48175    12.93888     -0.50    0.617     -31.93658     18.97308
      occ3w1 |    3.366432    12.92252      0.26    0.795     -22.05621     28.78907
      occ4w1 |   -.5743876    13.80492     -0.04    0.967     -27.73297      26.5842
      occ5w1 |    3.330031    14.86892      0.22    0.823     -25.92179     32.58185
      occ6w1 |    11.84712    15.35904      0.77    0.441      -18.3689     42.06315
      occ7w1 |   -1.267355    13.55551     -0.09    0.926     -27.93529     25.40058
      occ8w1 |    4.848552    13.16943      0.37    0.713     -21.05984     30.75694
      inc1w1 |    -6.51091    11.78358     -0.55    0.581      -29.6929     16.67108
      inc2w1 |   -7.410934     11.3958     -0.65    0.516     -29.83003     15.00816
      inc3w1 |    .3518362    11.52588      0.03    0.976     -22.32317     23.02684
      inc4w1 |    .1031837    13.11121      0.01    0.994     -25.69067     25.89703
     radhlw1 |    .1013083    .0513299      1.97    0.049      .0003264     .2022903
     radchw1 |   -.0717827    .0571007     -1.26    0.210     -.1841176     .0405522
     radtlw1 |   -.0061093    .0593689     -0.10    0.918     -.1229065     .1106879
       illw1 |    2.204037    3.183263      0.69    0.489     -4.058437     8.466511
      havmil |    .0063651    .0069043      0.92    0.357     -.0072177      .019948
avgcumdosew1 |    6.039491    2.830851      2.13    0.034       .470321     11.60866
       _cons |   -38.76462    34.43461     -1.13    0.261     -106.5083     28.97902
-------------+----------------------------------------------------------------

.
. // manual simultaneous trimming retains dose=> sleep impact but adding
. // bf reduces it
.
. nestreg : regress WHPsleep (age )(avgcumdosew1)(bffsl3) (bffsl4) (bffsl10) ///
>    (bffsl11) (radchw1 ) if gender==2

Block  1: age

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  1,   361) =   62.16
       Model |  50783.6176     1  50783.6176           Prob > F      =  0.0000
    Residual |  294923.523   361  816.962668           R-squared     =  0.1469
-------------+------------------------------           Adj R-squared =  0.1445
       Total |  345707.141   362  954.992102           Root MSE      =  28.583

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9980208     .126584      7.88    0.000      .7490861     1.246955
       _cons |   -23.87083    6.531035     -3.65    0.000     -36.71449    -11.02718
-------------+----------------------------------------------------------------

Block  2: avgcumdosew1

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  2,   360) =   33.79
       Model |   54642.856     2   27321.428           Prob > F      =  0.0000
    Residual |  291064.285   360  808.511902           R-squared     =  0.1581
-------------+------------------------------           Adj R-squared =  0.1534
       Total |  345707.141   362  954.992102           Root MSE      =  28.434

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9465368    .1281135      7.39    0.000      .6945919     1.198482
avgcumdosew1 |    6.047101    2.767831      2.18    0.030      .6039517     11.49025
       _cons |   -23.31448    6.502157     -3.59    0.000     -36.10146     -10.5275
-------------+----------------------------------------------------------------

Block  3: bffsl3

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  3,   359) =   22.62
       Model |   54965.108     3  18321.7027           Prob > F      =  0.0000
    Residual |  290742.033   359  809.866387           R-squared     =  0.1590
-------------+------------------------------           Adj R-squared =  0.1520
       Total |  345707.141   362  954.992102           Root MSE      =  28.458

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9320784    .1302533      7.16    0.000       .675923     1.188234
avgcumdosew1 |    5.926304     2.77676      2.13    0.033      .4655452     11.38706
      bffsl3 |    .0545461    .0864713      0.63    0.529      -.115508     .2246001
       _cons |   -22.76761    6.565094     -3.47    0.001     -35.67849    -9.856737
-------------+----------------------------------------------------------------

Block  4: bffsl4

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  4,   358) =   33.87
       Model |  94902.9814     4  23725.7454           Prob > F      =  0.0000
    Residual |  250804.159   358  700.570278           R-squared     =  0.2745
-------------+------------------------------           Adj R-squared =  0.2664
       Total |  345707.141   362  954.992102           Root MSE      =  26.468

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |     .779318    .1228235      6.35    0.000      .5377717     1.020864
avgcumdosew1 |    4.961193    2.585763      1.92    0.056      -.124001     10.04639
      bffsl3 |    .2178584    .0832829      2.62    0.009      .0540733     .3816436
      bffsl4 |    .3785167    .0501324      7.55    0.000      .2799257     .4771077
       _cons |    -26.2982    6.123926     -4.29    0.000     -38.34159    -14.25481
-------------+----------------------------------------------------------------

Block  5: bffsl10

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  5,   357) =   30.85
       Model |   104306.42     5   20861.284           Prob > F      =  0.0000
    Residual |  241400.721   357  676.192495           R-squared     =  0.3017
-------------+------------------------------           Adj R-squared =  0.2919
       Total |  345707.141   362  954.992102           Root MSE      =  26.004

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7467712    .1209829      6.17    0.000      .5088425     .9846999
avgcumdosew1 |    5.228531    2.541388      2.06    0.040      .2305581      10.2265
      bffsl3 |    .2409732    .0820555      2.94    0.004      .0796003     .4023461
      bffsl4 |    .3830333    .0492673      7.77    0.000      .2861426     .4799239
     bffsl10 |    .0042843    .0011489      3.73    0.000      .0020249     .0065437
       _cons |   -26.37526     6.01647     -4.38    0.000     -38.20744    -14.54308
-------------+----------------------------------------------------------------

Block  6: bffsl11

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  6,   356) =   26.69
       Model |  107270.261     6  17878.3768           Prob > F      =  0.0000
    Residual |   238436.88   356  669.766516           R-squared     =  0.3103
-------------+------------------------------           Adj R-squared =  0.2987
       Total |  345707.141   362  954.992102           Root MSE      =   25.88

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7480538    .1204082      6.21    0.000       .511253     .9848546
avgcumdosew1 |    5.184635    2.529369      2.05    0.041      .2102504     10.15902
      bffsl3 |    .2441872     .081679      2.99    0.003      .0835533     .4048211
      bffsl4 |    .4322411    .0543267      7.96    0.000      .3253995     .5390827
     bffsl10 |    .0040377    .0011494      3.51    0.001      .0017773     .0062982
     bffsl11 |   -.0022644    .0010764     -2.10    0.036     -.0043814    -.0001474
       _cons |   -26.68815    5.989661     -4.46    0.000     -38.46771    -14.90858
-------------+----------------------------------------------------------------

Block  7: radchw1

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  7,   355) =   23.22
       Model |  108558.696     7  15508.3852           Prob > F      =  0.0000
    Residual |  237148.444   355  668.023787           R-squared     =  0.3140
-------------+------------------------------           Adj R-squared =  0.3005
       Total |  345707.141   362  954.992102           Root MSE      =  25.846

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7695076    .1212396      6.35    0.000      .5310694     1.007946
avgcumdosew1 |    5.300215    2.527447      2.10    0.037      .3295634     10.27087
      bffsl3 |    .2508955    .0817155      3.07    0.002      .0901882     .4116029
      bffsl4 |    .4247964    .0545202      7.79    0.000      .3175733     .5320195
     bffsl10 |    .0041473    .0011506      3.60    0.000      .0018844     .0064101
     bffsl11 |   -.0023974    .0010793     -2.22    0.027       -.00452    -.0002748
     radchw1 |   -.0523155    .0376699     -1.39    0.166     -.1263998     .0217688
       _cons |   -24.37773    6.208891     -3.93    0.000     -36.58857     -12.1669
-------------+----------------------------------------------------------------


  +-------+-----------------------------------------------------+
  |       |          Block  Residual                     Change |
  | Block |       F     df        df   Pr > F       R2    in R2 |
  +-------+-----------------------------------------------------+
  |     1 |   62.16      1       361   0.0000   0.1469          |
  |     2 |    4.77      1       360   0.0295   0.1581   0.0112 |
  |     3 |    0.40      1       359   0.5286   0.1590   0.0009 |
  |     4 |   57.01      1       358   0.0000   0.2745   0.1155 |
  |     5 |   13.91      1       357   0.0002   0.3017   0.0272 |
  |     6 |    4.43      1       356   0.0361   0.3103   0.0086 |
  |     7 |    1.93      1       355   0.1658   0.3140   0.0037 |
  +-------+-----------------------------------------------------+

.  regress WHPsleep age  radhlw1 radchw1 avgcumdosew1 bffsl3 bffsl4 ///
>     bffsl10 bffsl11   if gender==2

      Source |       SS       df       MS              Number of obs =     362
-------------+------------------------------           F(  8,   353) =   20.51
       Model |  109637.073     8  13704.6341           Prob > F      =  0.0000
    Residual |  235882.555   353  668.222537           R-squared     =  0.3173
-------------+------------------------------           Adj R-squared =  0.3018
       Total |  345519.628   361  957.118083           Root MSE      =   25.85

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7496996    .1238431      6.05    0.000      .5061365     .9932627
     radhlw1 |    .0504505    .0425341      1.19    0.236     -.0332015     .1341025
     radchw1 |   -.0743043    .0414448     -1.79    0.074      -.155814     .0072054
avgcumdosew1 |    5.327278    2.527946      2.11    0.036      .3555491     10.29901
      bffsl3 |    .2442025    .0819543      2.98    0.003      .0830225     .4053825
      bffsl4 |    .4214149    .0546883      7.71    0.000       .313859     .5289707
     bffsl10 |    .0040643    .0011536      3.52    0.000      .0017955     .0063331
     bffsl11 |    -.002211    .0010926     -2.02    0.044     -.0043598    -.0000622
       _cons |   -24.89223    6.231688     -3.99    0.000     -37.14813    -12.63632
-------------+----------------------------------------------------------------

.
.
.
. * some basis functions are removed owing to collinearity: bffsl6
. des bffsl3 bffsl4 bffsl10 bffsl11 bffsl12 bffsl15 ///
>    bffsl17

              storage  display     value
variable name   type   format      label      variable label
-------------


bffsl3            float    %9.0g                        max(, deaw1 - 1)* bffsl1


bffsl4            float    %9.0g                        max(0,1- deaw1) * bffsl1


bffsl10           float    %9.0g                        max(0, 9 - BSIhos) * bffsl7


bffsl11           float    %9.0g                         max(0, cloud - 10) * bffsl6


bffsl12           float    %9.0g                        max(0, 10-cloud)*bffsl6


bffsl15           float    %9.0g                        (suchrw1 != . ) * bffsl8


bffsl17           float    %9.0g                        max(0, suchrw1 + 1.85275e-007) * bffsl15


.  * death BSIhos cloud suchrw1 are the base variables
. nestreg : regress WHPsleep (age )(avgcumdosew1) (bffsl4) (bffsl10) ///
>    (bffsl11) (BSIhos)  if gender==2

Block  1: age

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  1,   361) =   62.16
       Model |  50783.6176     1  50783.6176           Prob > F      =  0.0000
    Residual |  294923.523   361  816.962668           R-squared     =  0.1469
-------------+------------------------------           Adj R-squared =  0.1445
       Total |  345707.141   362  954.992102           Root MSE      =  28.583

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9980208     .126584      7.88    0.000      .7490861     1.246955
       _cons |   -23.87083    6.531035     -3.65    0.000     -36.71449    -11.02718
-------------+----------------------------------------------------------------

Block  2: avgcumdosew1

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  2,   360) =   33.79
       Model |   54642.856     2   27321.428           Prob > F      =  0.0000
    Residual |  291064.285   360  808.511902           R-squared     =  0.1581
-------------+------------------------------           Adj R-squared =  0.1534
       Total |  345707.141   362  954.992102           Root MSE      =  28.434

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9465368    .1281135      7.39    0.000      .6945919     1.198482
avgcumdosew1 |    6.047101    2.767831      2.18    0.030      .6039517     11.49025
       _cons |   -23.31448    6.502157     -3.59    0.000     -36.10146     -10.5275
-------------+----------------------------------------------------------------

Block  3: bffsl4

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  3,   359) =   42.19
       Model |  90109.0808     3  30036.3603           Prob > F      =  0.0000
    Residual |   255598.06   359  711.972312           R-squared     =  0.2607
-------------+------------------------------           Adj R-squared =  0.2545
       Total |  345707.141   362  954.992102           Root MSE      =  26.683

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .8469156    .1210475      7.00    0.000      .6088642     1.084967
avgcumdosew1 |    5.497956      2.5985      2.12    0.035      .3877616     10.60815
      bffsl4 |    .3444575    .0488045      7.06    0.000      .2484789     .4404362
       _cons |    -28.0174    6.137904     -4.56    0.000     -40.08816    -15.94663
-------------+----------------------------------------------------------------

Block  4: bffsl10

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  4,   358) =   35.65
       Model |  98474.7572     4  24618.6893           Prob > F      =  0.0000
    Residual |  247232.384   358   690.59325           R-squared     =  0.2849
-------------+------------------------------           Adj R-squared =  0.2769
       Total |  345707.141   362  954.992102           Root MSE      =  26.279

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .8230504    .1194133      6.89    0.000      .5882107      1.05789
avgcumdosew1 |    5.802952    2.560689      2.27    0.024      .7670701     10.83883
      bffsl4 |    .3453067    .0480668      7.18    0.000       .250778     .4398354
     bffsl10 |    .0040294    .0011577      3.48    0.001      .0017526     .0063062
       _cons |   -28.26143    6.045454     -4.67    0.000      -40.1505    -16.37237
-------------+----------------------------------------------------------------

Block  5: bffsl11

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  5,   357) =   29.59
       Model |  101284.094     5  20256.8188           Prob > F      =  0.0000
    Residual |  244423.047   357  684.658395           R-squared     =  0.2930
-------------+------------------------------           Adj R-squared =  0.2831
       Total |  345707.141   362  954.992102           Root MSE      =  26.166

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .8252892    .1189042      6.94    0.000      .5914484      1.05913
avgcumdosew1 |    5.767681    2.549721      2.26    0.024      .7533198     10.78204
      bffsl4 |    .3927166    .0532761      7.37    0.000      .2879422      .497491
     bffsl10 |    .0037861     .001159      3.27    0.001      .0015068     .0060654
     bffsl11 |   -.0022042    .0010881     -2.03    0.044     -.0043442    -.0000642
       _cons |   -28.59049    6.021613     -4.75    0.000     -40.43278    -16.74819
-------------+----------------------------------------------------------------

Block  6: BSIhos

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  6,   356) =   26.65
       Model |  107147.756     6  17857.9594           Prob > F      =  0.0000
    Residual |  238559.385   356  670.110631           R-squared     =  0.3099
-------------+------------------------------           Adj R-squared =  0.2983
       Total |  345707.141   362  954.992102           Root MSE      =  25.886

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .8468331    .1178594      7.19    0.000      .6150448     1.078621
avgcumdosew1 |    5.704157    2.522579      2.26    0.024       .743127     10.66519
      bffsl4 |    .2874095    .0636032      4.52    0.000      .1623242     .4124948
     bffsl10 |    .0044169    .0011663      3.79    0.000      .0021233     .0067106
     bffsl11 |   -.0021898    .0010765     -2.03    0.043      -.004307    -.0000727
      BSIhos |    1.800989    .6088354      2.96    0.003      .6036231     2.998355
       _cons |   -40.32052    7.156385     -5.63    0.000     -54.39463    -26.24642
-------------+----------------------------------------------------------------


  +-------+-----------------------------------------------------+
  |       |          Block  Residual                     Change |
  | Block |       F     df        df   Pr > F       R2    in R2 |
  +-------+-----------------------------------------------------+
  |     1 |   62.16      1       361   0.0000   0.1469          |
  |     2 |    4.77      1       360   0.0295   0.1581   0.0112 |
  |     3 |   49.81      1       359   0.0000   0.2607   0.1026 |
  |     4 |   12.11      1       358   0.0006   0.2849   0.0242 |
  |     5 |    4.10      1       357   0.0435   0.2930   0.0081 |
  |     6 |    8.75      1       356   0.0033   0.3099   0.0170 |
  +-------+-----------------------------------------------------+

.
.
. local bfw1a bffsl3 bffsl4 bffsl5 bffsl9 bffsl10 bffsl11 bffsl12 bffsl15 ///
>    bffsl17

.
.
. set more off

. sw, pr(.1): regress WHPsleep age educ2-educ7 marrw11-marrw13 marrw15 childw1 ///
>   emplw12-emplw16 occ1w1-occ8w1 inc1w1-inc4w1 radhlw1 radchw1 ///
>   radtlw1 havmil BSIhos bffsl3 bffsl4 bffsl5 bffsl9 bffsl10 bffsl11 bffsl12 bffsl15 ///
>    bffsl17  if gender==2, vce(cluster id)


  begin with full model


p = 0.9918 >= 0.1000  removing bffsl9


p = 0.9572 >= 0.1000  removing bffsl15


p = 0.9281 >= 0.1000  removing occ7w1


p = 0.8908 >= 0.1000  removing inc3w1


p = 0.9232 >= 0.1000  removing occ8w1


p = 0.9381 >= 0.1000  removing occ3w1


p = 0.9068 >= 0.1000  removing occ1w1


p = 0.8324 >= 0.1000  removing childw1


p = 0.8470 >= 0.1000  removing educ7


p = 0.8293 >= 0.1000  removing emplw12


p = 0.6234 >= 0.1000  removing occ6w1


p = 0.6121 >= 0.1000  removing marrw12


p = 0.5350 >= 0.1000  removing bffsl17


p = 0.4335 >= 0.1000  removing radtlw1


p = 0.4647 >= 0.1000  removing radchw1


p = 0.4378 >= 0.1000  removing bffsl5


p = 0.3997 >= 0.1000  removing marrw11


p = 0.3127 >= 0.1000  removing emplw16


p = 0.2819 >= 0.1000  removing emplw15


p = 0.3074 >= 0.1000  removing marrw15


p = 0.2909 >= 0.1000  removing educ5


p = 0.3994 >= 0.1000  removing educ6


p = 0.2954 >= 0.1000  removing inc4w1


p = 0.2190 >= 0.1000  removing educ4


p = 0.1415 >= 0.1000  removing havmil


p = 0.1333 >= 0.1000  removing emplw13


p = 0.1124 >= 0.1000  removing occ4w1


p = 0.1353 >= 0.1000  removing occ5w1


p = 0.1179 >= 0.1000  removing occ2w1


p = 0.1215 >= 0.1000  removing radhlw1


Linear regression                                      Number of obs =     360
                                                       F( 12,   359) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.3877
                                                       Root MSE      =  24.696

(Std. Err. adjusted for 360 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9645961    .1677099      5.75    0.000      .6347789     1.294413
       educ2 |    12.16019    5.929755      2.05    0.041      .4987658      23.8216
       educ3 |    6.502654    2.915673      2.23    0.026      .7687085      12.2366
      inc1w1 |   -6.853432    3.913822     -1.75    0.081     -14.55033     .8434666
     emplw14 |    12.57209    2.618662      4.80    0.000      7.422241     17.72193
     bffsl10 |    .0037706    .0016238      2.32    0.021      .0005773     .0069639
      bffsl3 |    .2042475    .0987591      2.07    0.039      .0100284     .3984667
      inc2w1 |   -9.795586    2.811102     -3.48    0.001     -15.32388     -4.26729
     bffsl11 |    -.004553    .0010124     -4.50    0.000      -.006544     -.002562
     marrw13 |   -8.961304    3.878728     -2.31    0.021     -16.58919    -1.333421
     bffsl12 |   -.0365311     .008225     -4.44    0.000     -.0527063    -.0203558
      bffsl4 |     .458643     .077333      5.93    0.000      .3065603     .6107256
      BSIhos |    1.384154    .6612619      2.09    0.037      .0837201     2.684587
       _cons |   -36.04149    7.706191     -4.68    0.000     -51.19644    -20.88654
-------------+----------------------------------------------------------------

.
. des inc1w1-inc4w1  marrw11-marrw16 bffsl3-bffsl12

              storage  display     value
variable name   type   format      label      variable label
-------------


inc1w1            byte     %15.0g        LABJ           Income is not sufficient for basic neccessities in 1986


inc2w1            byte     %15.0g        LABJ           Income is just sufficient for basic neccessities in 1986


inc3w1            byte     %15.0g        LABJ           Income is sufficient for basics plus extra purchases/savings in 1986


inc4w1            byte     %15.0g        LABJ           Income allows to comfortably afford luxury items in 1986


marrw11           byte     %8.0g                        marrw1==1. single


marrw12           byte     %8.0g                        marrw1==2. cohabitating


marrw13           byte     %8.0g                        marrw1==3. married


marrw14           byte     %8.0g                        marrw1==4. separated


marrw15           byte     %8.0g                        marrw1==5. divorced


marrw16           byte     %8.0g                        marrw1==6. widowed


bffsl3            float    %9.0g                        max(, deaw1 - 1)* bffsl1


bffsl4            float    %9.0g                        max(0,1- deaw1) * bffsl1


bffsl5            float    %9.0g                        (docvisitsw1 = . )* bffsl4


bffsl6            float    %9.0g                        (docvisitsw1 != . )*bffsl4


bffsl7            float    %9.0g                        max(0, airw2-10)*bffsl5


bffsl8            float    %9.0g                        max(0, 10- airw2 )*bffsl5


bffsl9            float    %9.0g                        max(0, BSIhos - 9)*bffsl7


bffsl10           float    %9.0g                        max(0, 9 - BSIhos) * bffsl7


bffsl11           float    %9.0g                         max(0, cloud - 10) * bffsl6


bffsl12           float    %9.0g                        max(0, 10-cloud)*bffsl6


. regress WHPsleep age avgcumdosew1 inc1w1-inc4w1 ///
>     marrw11-marrw16 bffsl4 bffsl10 bffsl11  BSIhos ///
>      if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 16,   362) =   15.56
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.3662
                                                       Root MSE      =  25.165

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .8578777     .166374      5.16    0.000      .5306968     1.185059
avgcumdosew1 |    6.398469    2.576871      2.48    0.013      1.330951     11.46599
      inc1w1 |   -4.983327    4.876462     -1.02    0.308     -14.57308     4.606425
      inc2w1 |   -9.770151    3.958723     -2.47    0.014     -17.55513    -1.985168
      inc3w1 |   -4.092424    4.778323     -0.86    0.392     -13.48918     5.304334
      inc4w1 |   -9.313265    6.127037     -1.52    0.129     -21.36232     2.735791
     marrw11 |      6.7235    6.517339      1.03    0.303     -6.093101      19.5401
     marrw12 |    33.90256    21.39437      1.58    0.114     -8.170298     75.97541
     marrw13 |    2.362157    7.541906      0.31    0.754     -12.46929     17.19361
     marrw14 |    57.13913    13.75949      4.15    0.000      30.08057      84.1977
     marrw15 |    2.248905    15.63139      0.14    0.886     -28.49083     32.98864
     marrw16 |     13.8564    19.02823      0.73    0.467     -23.56335     51.27614
      bffsl4 |    .3391336     .078603      4.31    0.000      .1845578     .4937094
     bffsl10 |    .0038976      .00166      2.35    0.019      .0006331     .0071621
     bffsl11 |   -.0026421    .0010676     -2.47    0.014     -.0047415    -.0005427
      BSIhos |    1.514132    .6783504      2.23    0.026      .1801291     2.848134
       _cons |   -38.92315    8.959197     -4.34    0.000     -56.54175    -21.30454
-------------+----------------------------------------------------------------

. scalar tw1bf = e(r2_a)

. scalar list tw1bf
     tw1bf =  .33687491

.
. scalar w1numbf = 3

. set more off

. regress WHPsleep age avgcumdosew1 inc1w1-inc4w1 marrw11-marrw16 ///
>    BSIhos if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 13,   362) =   18.20
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.2842
                                                       Root MSE      =  26.629

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9125816    .1852578      4.93    0.000      .5482649     1.276898
avgcumdosew1 |    6.142491    2.246632      2.73    0.007      1.724401     10.56058
      inc1w1 |    1.105283    5.383593      0.21    0.837     -9.481761     11.69233
      inc2w1 |    -6.49865    3.818681     -1.70    0.090     -14.00823     1.010934
      inc3w1 |   -1.764873    4.782481     -0.37    0.712     -11.16981     7.640062
      inc4w1 |   -8.302836    6.916288     -1.20    0.231     -21.90398     5.298313
     marrw11 |    10.39463     7.49797      1.39    0.167     -4.350421     25.13967
     marrw12 |    34.15338    18.83721      1.81    0.071     -2.890728     71.19748
     marrw13 |    8.397056    8.456978      0.99    0.321     -8.233919     25.02803
     marrw14 |    67.57899    12.29544      5.50    0.000      43.39952     91.75846
     marrw15 |     17.4306    17.08699      1.02    0.308     -16.17163     51.03283
     marrw16 |    11.31017    18.03912      0.63    0.531     -24.16445     46.78479
      BSIhos |    3.025676    .5130592      5.90    0.000      2.016725     4.034627
       _cons |   -50.88147    9.735873     -5.23    0.000     -70.02744     -31.7355
-------------+----------------------------------------------------------------

. scalar tw1nobf = e(r2_a)

.
. scalar r2chabfw1 = tw1bf - tw1nobf

. scalar list r2chabfw1
 r2chabfw1 =  .07938894

.
. sw, pr(.1):regress WHPsleep age inc1w1-inc4w1 marrw11-marrw16 ///
>     bffsl3 bffsl4 bffsl10 bffsl11 bffsl12 radchw1 radhlw2 if gender==2, vce(cluster id)


  begin with full model


p = 0.9375 >= 0.1000  removing marrw16


p = 0.8485 >= 0.1000  removing marrw13


p = 0.7295 >= 0.1000  removing marrw15


p = 0.5223 >= 0.1000  removing inc3w1


p = 0.2930 >= 0.1000  removing radchw1


p = 0.2461 >= 0.1000  removing inc4w1


p = 0.2321 >= 0.1000  removing inc1w1


p = 0.1483 >= 0.1000  removing bffsl10


p = 0.1127 >= 0.1000  removing marrw12


p = 0.1245 >= 0.1000  removing marrw11


Linear regression                                      Number of obs =     363
                                                       F(  8,   362) =   51.18
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.3745
                                                       Root MSE      =  24.715

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7501282    .1235755      6.07    0.000      .5071122     .9931443
      bffsl3 |    .2209694    .0974793      2.27    0.024      .0292726     .4126661
      inc2w1 |   -6.963654    2.571886     -2.71    0.007     -12.02137    -1.905941
     bffsl11 |   -.0048057    .0010152     -4.73    0.000     -.0068022    -.0028093
      bffsl4 |    .5646001    .0529057     10.67    0.000       .460559     .6686412
     marrw14 |    58.98748    7.077483      8.33    0.000      45.06933     72.90562
     bffsl12 |   -.0403642    .0081502     -4.95    0.000     -.0563919    -.0243365
     radhlw2 |     .074428    .0409549      1.82    0.070     -.0061114     .1549674
       _cons |   -25.21833     5.45902     -4.62    0.000      -35.9537    -14.48295
-------------+----------------------------------------------------------------

.
.
. matrix define FemaleWHPsleepr2 = J(4,5,0)

. matrix colnames FemaleWHPsleepr2 = FullBFR2a TR2aBF  TR2aNoBF  NumBF  BFR2cha

. matrix rownames FemaleWHPsleepr2 = wave1  wave2  wave3

. matlist FemaleWHPsleepr2

   | FullBFR2a    TR2aBF    TR2aNoBF    NumBF    BFR2cha
-------------+-------------------------------------------------------
wave1   |         0            0            0            0            0
wave2   |         0            0            0            0            0
wave3   |         0            0            0            0            0
wave3   |         0            0            0            0            0

.
.
. matrix define FemaleWHPsleepr2w1 = ( fw1 , tw1bf, tw1nobf, w1numbf, r2chabfw1)

. matrix colnames FemaleWHPsleepr2w1 = FullBFR2w1 TR2BFw1  TR2NoBFw1  w1NumBF  BFR2chaw1

. matrix rownames FemaleWHPsleepr2w1 = wave1

.
.
. matlist FemaleWHPsleepr2w1

   | FullBFR~1    TR2BFw1    TR2NoBFw1    w1NumBF    BFR2chaw1
-------------+-------------------------------------------------------
wave1   |  .3392537     .3368749      .257486            3     .0793889

.
. title "Possible wave one female moderators of sleep"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****            Possible wave one female moderators of sleep             *****
*****                                                                     *****
*****                                                                     *****
*****                                            29 Jun 2012    00:25:06  *****
*******************************************************************************
*******************************************************************************


.
.
.     foreach var in age inc1w1-inc4w1 ///
>     marrw11-marrw16 bffsl4 bffsl10 bffsl11 BSIhos {
  2.     cap gen `var'Xd1 = `var'*avgcumdosew1
  3.     }

.
. regress WHPsleep age avgcumdosew1  ///
>     marrw11-marrw16 bffsl4 bffsl10 bffsl11  BSIhos ///
>     ageXd1 ///
>     bffsl4Xd1 ///
>      if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 14,   362) =   19.47
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.3605
                                                       Root MSE      =  25.204

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7322607    .1877279      3.90    0.000      .3630865     1.101435
avgcumdosew1 |   -10.76014    15.85411     -0.68    0.498     -41.93786     20.41758
     marrw11 |    2.027149    5.734546      0.35    0.724     -9.250059     13.30436
     marrw12 |    34.82898    22.95706      1.52    0.130     -10.31697     79.97493
     marrw13 |   -.8725605    6.765701     -0.13    0.897     -14.17757     12.43245
     marrw14 |    56.74991    13.31174      4.26    0.000      30.57185     82.92796
     marrw15 |   -.5240387    16.00412     -0.03    0.974     -31.99676     30.94869
     marrw16 |    13.42993    19.19402      0.70    0.485     -24.31586     51.17572
      bffsl4 |      .40573    .0898826      4.51    0.000      .2289723     .5824876
     bffsl10 |    .0037234    .0016804      2.22    0.027      .0004188     .0070281
     bffsl11 |   -.0021772    .0010306     -2.11    0.035      -.004204    -.0001504
      BSIhos |    1.251892    .7094405      1.76    0.078     -.1432502     2.647034
      ageXd1 |    .3968324    .2958938      1.34    0.181     -.1850542     .9787191
   bffsl4Xd1 |   -.1602782    .0689758     -2.32    0.021     -.2959218    -.0246346
       _cons |   -34.86181    9.696088     -3.60    0.000     -53.92954    -15.79407
-------------+----------------------------------------------------------------

.
. des bffsl1 bffsl4

              storage  display     value
variable name   type   format      label      variable label
-------------


bffsl1            float    %9.0g                        max(0, BSIposymp-15)


bffsl4            float    %9.0g                        max(0,1- deaw1) * bffsl1


. summ bffsl4 if gender==2

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
      bffsl4 |       363    28.71074    28.97201          0        141

. centile bffsl4 if gender==2, centile(33 67)

                                                       -- Binom. Interp. --
    Variable |     Obs  Percentile      Centile        [95% Conf. Interval]
-------------+-------------------------------------------------------------
      bffsl4 |      363        33            10         6.89607          14
             |                 67            35              32    39.10393

. cap gen lobffsl4Xd1 = avgcumdosew1 if bffsl4 <= 10 & gender==2

. cap gen midbffsl4Xd1 = avgcumdosew1 if bffsl4 > 10 & bffsl4 < 35 & gender==2

. cap gen hibffsl4Xd1 = avgcumdosew1 if bffsl4 >= 35 & gender==2 & bffsl4 < .

. label var lobffsl4Xd1 "cumdosew3 for male lower third of bffsl4 "

. label var midbffsl4Xd1  "cumdosew3 for male mid-third of bffsl4"

. label var hibffsl4Xd1 "cumdosew3 for male upper third of bffsl4"

.
.
.
.
. twoway lfit WHPsleep lobffsl4Xd1 || lfit WHPsleep midbffsl4Xd1 || lfit WHPsleep hibffsl4Xd1,  ///
>      yti(WHPsleep) color(blue red green) ///
>      title(Effect of bffsl4 by Dose Interaction on Sleep) ///
>      subtitle(for females in wave one) ///
>    legend(label( 1 "for lower bffsl4 " ) caption(bffsl4=max(0,1-deaw1)*max(0,BSIposymp-15)) ///
>    label( 2 "for mid-bffsl4") label(3 "for high bffsl4") rows(3))  ///
>    xti(avgcumdosew3 in milliGrays scale)

. graph save fbffsl4Xd1onSlp1.gph, replace
(file fbffsl4Xd1onSlp1.gph saved)

. graph export fbffsl4Xd1onSlp1.eps, replace
(file fbffsl4Xd1onSlp1.eps written in EPS format)

.
.
. cap gen whpsleep = WHPsleep

.
. title "Possible mediators for Female sleep"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****                Possible mediators for Female sleep                  *****
*****                                                                     *****
*****                                                                     *****
*****                                            29 Jun 2012    00:25:08  *****
*******************************************************************************
*******************************************************************************


. * age, BSIanxiety, fear of eating contaminated food, fear of going outdoors,
. * PTSD in wave 1, depression in wave one
. sem(avgcumdosew1->radhlw3)(radhlw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radhlw3 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3840.3075
Iteration 1:   log likelihood = -3840.3075

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3840.3075

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radhlw3 <-     |
    avgcumdosew1 |    10.14689    3.274489      3.10    0.002      3.729013     16.56477
           _cons |    57.60931    2.105602     27.36    0.000      53.48241     61.73621
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radhlw3 |    .2449287    .0449114      5.45    0.000      .1569039     .3329534
           _cons |    11.30063    3.151775      3.59    0.000      5.123269       17.478
-----------------+----------------------------------------------------------------
 Variance         |
       e.radhlw3 |     1171.23    86.93681                    1012.651     1354.641
      e.whpsleep |    880.2405    65.33758                    761.0606     1018.084
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      6.90, Prob > chi2 = 0.0086

. sem(avgcumdosew1->age)(age-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  age whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3434.5704
Iteration 1:   log likelihood = -3434.5704

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3434.5704

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   age <-         |
    avgcumdosew1 |    3.973879    1.114596      3.57    0.000       1.78931     6.158447
           _cons |    48.88157    .7167212     68.20    0.000      47.47682     50.28632
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
             age |    .9980208    .1262348      7.91    0.000      .7506051     1.245436
           _cons |   -23.87083    6.513018     -3.67    0.000     -36.63611    -11.10555
-----------------+----------------------------------------------------------------
 Variance         |
           e.age |    135.7032    10.07284                    117.3297     156.9539
      e.whpsleep |    812.4615    60.30655                    702.4585     939.6906
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      4.78, Prob > chi2 = 0.0288

. sem(avgcumdosew1->radhlw1)(radhlw1-> whpsleep) if gender==2, nocapslatent
(1 observations with missing values excluded;
 specify option 'method(mlmv)' to use all observations)

Endogenous variables


Observed:  radhlw1 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3871.0527
Iteration 1:   log likelihood = -3871.0527

Structural equation model                       Number of obs      =       362
 Estimation method = ml
 Log likelihood    = -3871.0527

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radhlw1 <-     |
    avgcumdosew1 |    3.789972      3.5524      1.07    0.286     -3.172604     10.75255
           _cons |    55.44948    2.287412     24.24    0.000      50.96623     59.93272
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radhlw1 |    .1083192    .0433068      2.50    0.012      .0234394     .1931991
           _cons |    20.13816    2.937073      6.86    0.000       14.3816     25.89471
-----------------+----------------------------------------------------------------
 Variance         |
       e.radhlw1 |    1377.647    102.3997                    1190.882     1593.703
      e.whpsleep |    938.2593    69.74026                    811.0611     1085.406
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =     10.40, Prob > chi2 = 0.0013

. sem(avgcumdosew1-> illw1)(illw1-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  illw1 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -2314.2199
Iteration 1:   log likelihood = -2314.2199

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2314.2199

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   illw1 <-       |
    avgcumdosew1 |    .0655142    .0473479      1.38    0.166     -.0272859     .1583144
           _cons |    .1570822    .0304462      5.16    0.000      .0974087     .2167556
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
           illw1 |    7.374115    3.241554      2.27    0.023      1.020785     13.72744
           _cons |    24.92422     1.70986     14.58    0.000      21.57296     28.27549
-----------------+----------------------------------------------------------------
 Variance         |
         e.illw1 |    .2448817    .0181768                     .211726     .2832294
      e.whpsleep |     938.975    69.69726                    811.8427     1086.016
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =     10.32, Prob > chi2 = 0.0013

. sem(avgcumdosew1->radchw1)(radchw1-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radchw1 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3884.0569
Iteration 1:   log likelihood = -3884.0569

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3884.0569

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radchw1 <-     |
    avgcumdosew1 |    3.622756     3.55317      1.02    0.308      -3.34133     10.58684
           _cons |    60.18119    2.284803     26.34    0.000      55.70306     64.65933
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radchw1 |   -.0270378    .0435314     -0.62    0.535     -.1123577     .0582821
           _cons |    27.90469    3.124745      8.93    0.000       21.7803     34.02908
-----------------+----------------------------------------------------------------
 Variance         |
       e.radchw1 |    1379.072    102.3643                    1192.353     1595.031
      e.whpsleep |    951.3502    70.61584                    822.5424     1100.329
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =     11.48, Prob > chi2 = 0.0007

. sem(avgcumdosew1->phlthw1)(phlthw1-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  phlthw1 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3614.5213
Iteration 1:   log likelihood = -3614.5213

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3614.5213

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   phlthw1 <-     |
    avgcumdosew1 |    1.262912     1.69126      0.75    0.455     -2.051897     4.577721
           _cons |    88.49087    1.087535     81.37    0.000      86.35934      90.6224
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         phlthw1 |   -.0646515    .0915014     -0.71    0.480      -.243991      .114688
           _cons |    31.99312    8.295266      3.86    0.000       15.7347     48.25154
-----------------+----------------------------------------------------------------
 Variance         |
       e.phlthw1 |    312.4466    23.19197                     270.143     361.3749
      e.whpsleep |    951.0533     70.5938                    822.2857     1099.985
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =     11.43, Prob > chi2 = 0.0007

. sem(avgcumdosew1->BSIanx)(BSIanx-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  BSIanx whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -2984.6352
Iteration 1:   log likelihood = -2984.6352

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2984.6352

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   BSIanx <-      |
    avgcumdosew1 |    1.528414    .3399598      4.50    0.000      .8621048     2.194723
           _cons |    8.465151    .2186051     38.72    0.000      8.036693     8.893609
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          BSIanx |    4.065276    .3890115     10.45    0.000      3.302827     4.827724
           _cons |   -10.25323    3.770224     -2.72    0.007     -17.64274     -2.86373
-----------------+----------------------------------------------------------------
 Variance         |
        e.BSIanx |    12.62438    .9370693                     10.9151     14.60132
      e.whpsleep |    732.1075    54.34211                     632.984     846.7534
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      2.04, Prob > chi2 = 0.1531

. sem(avgcumdosew1->injothr)(injothr-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  injothr whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -2202.3259
Iteration 1:   log likelihood = -2202.3259

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2202.3259

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   injothr <-     |
    avgcumdosew1 |    .0392118    .0349969      1.12    0.263     -.0293809     .1078046
           _cons |    .8270641    .0225042     36.75    0.000      .7829568     .8711715
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         injothr |    13.52601    4.363315      3.10    0.002      4.974069     22.07795
           _cons |    14.87983    3.999569      3.72    0.000      7.040818     22.71884
-----------------+----------------------------------------------------------------
 Variance         |
       e.injothr |    .1337874    .0099306                    .1156733     .1547381
      e.whpsleep |    927.7998    68.86776                    802.1806     1073.091
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =     10.32, Prob > chi2 = 0.0013

. sem(avgcumdosew1->BSIhos)(BSIhos-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  BSIhos whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -2933.8323
Iteration 1:   log likelihood = -2933.8323

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2933.8323

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   BSIhos <-      |
    avgcumdosew1 |    .1662025    .2699552      0.62    0.538        -.3629     .6953051
           _cons |    7.429085    .1735899     42.80    0.000      7.088855     7.769315
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          BSIhos |    3.063457    .5507991      5.56    0.000      1.983911     4.143004
           _cons |    3.315143    4.406108      0.75    0.452     -5.320671     11.95096
-----------------+----------------------------------------------------------------
 Variance         |
        e.BSIhos |    7.960466    .5908812                    6.882661     9.207052
      e.whpsleep |    877.5761    65.13981                     758.757     1015.002
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =     10.94, Prob > chi2 = 0.0009

. sem(avgcumdosew1->radchw1)(radchw1-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radchw1 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3884.0569
Iteration 1:   log likelihood = -3884.0569

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3884.0569

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radchw1 <-     |
    avgcumdosew1 |    3.622756     3.55317      1.02    0.308      -3.34133     10.58684
           _cons |    60.18119    2.284803     26.34    0.000      55.70306     64.65933
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radchw1 |   -.0270378    .0435314     -0.62    0.535     -.1123577     .0582821
           _cons |    27.90469    3.124745      8.93    0.000       21.7803     34.02908
-----------------+----------------------------------------------------------------
 Variance         |
       e.radchw1 |    1379.072    102.3643                    1192.353     1595.031
      e.whpsleep |    951.3502    70.61584                    822.5424     1100.329
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =     11.48, Prob > chi2 = 0.0007

. sem(avgcumdosew1->fdferw1)(fdferw1-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  fdferw1 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3882.9571
Iteration 1:   log likelihood = -3882.9571

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3882.9571

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   fdferw1 <-     |
    avgcumdosew1 |    9.144086    3.586316      2.55    0.011      2.115036     16.17314
           _cons |    34.25982    2.306117     14.86    0.000      29.73991     38.77972
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         fdferw1 |     .129969    .0422852      3.07    0.002      .0470916     .2128464
           _cons |     21.3932     2.24687      9.52    0.000      16.98941     25.79698
-----------------+----------------------------------------------------------------
 Variance         |
       e.fdferw1 |    1404.922    104.2831                    1214.703     1624.928
      e.whpsleep |    928.2044    68.89779                    802.5305     1073.559
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      9.01, Prob > chi2 = 0.0027

. sem(avgcumdosew1->sepaw1)(sepaw1-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  sepaw1 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -1824.0742
Iteration 1:   log likelihood = -1824.0742

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -1824.0742

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   sepaw1 <-      |
    avgcumdosew1 |    -.000846     .012199     -0.07    0.945     -.0247555     .0230636
           _cons |    .0168128    .0078443      2.14    0.032      .0014382     .0321874
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          sepaw1 |   -11.69971    12.68927     -0.92    0.357     -36.57021      13.1708
           _cons |    26.43804    1.631394     16.21    0.000      23.24057     29.63551
-----------------+----------------------------------------------------------------
 Variance         |
        e.sepaw1 |    .0162555    .0012066                    .0140546     .0188011
      e.whpsleep |    950.1361    70.52572                    821.4927     1098.925
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =     11.21, Prob > chi2 = 0.0008

. sem(avgcumdosew1->PTSDw1)(PTSDw1-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  PTSDw1 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3828.7676
Iteration 1:   log likelihood = -3828.7676

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3828.7676

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   PTSDw1 <-      |
    avgcumdosew1 |    13.50282    3.095226      4.36    0.000      7.436293     19.56936
           _cons |    13.41722     1.99033      6.74    0.000      9.516244     17.31819
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          PTSDw1 |    .1591462    .0480869      3.31    0.001      .0648977     .2533947
           _cons |    23.38835    1.814274     12.89    0.000      19.83244     26.94427
-----------------+----------------------------------------------------------------
 Variance         |
        e.PTSDw1 |    1046.501     77.6786                    904.8104      1210.38
      e.whpsleep |    924.4664    68.62033                    799.2985     1069.235
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      7.37, Prob > chi2 = 0.0066

. sem(avgcumdosew1->depagw1)(depagw1-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  depagw1 whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3760.6572
Iteration 1:   log likelihood = -3760.6572

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3760.6572

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   depagw1 <-     |
    avgcumdosew1 |    13.80922    2.592668      5.33    0.000      8.727682     18.89075
           _cons |    7.956292    1.667169      4.77    0.000      4.688701     11.22388
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         depagw1 |    .2437406    .0561288      4.34    0.000      .1337301     .3537511
           _cons |    23.17608    1.730132     13.40    0.000      19.78508     26.56707
-----------------+----------------------------------------------------------------
 Variance         |
       e.depagw1 |     734.258    54.50174                    634.8434     849.2407
      e.whpsleep |    905.3303    67.19991                    782.7533     1047.102
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      5.44, Prob > chi2 = 0.0197

. sem(avgcumdosew1->BSIdep)(BSIdep-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  BSIdep whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -3010.1111
Iteration 1:   log likelihood = -3010.1111

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3010.1111

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   BSIdep <-      |
    avgcumdosew1 |    1.380465    .3522431      3.92    0.000      .6900812     2.070849
           _cons |    9.192477    .2265037     40.58    0.000      8.748538     9.636416
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          BSIdep |    3.445067    .3911827      8.81    0.000      2.678363     4.211771
           _cons |   -7.019698    4.053191     -1.73    0.083     -14.96381     .9244117
-----------------+----------------------------------------------------------------
 Variance         |
        e.BSIdep |    13.55315    1.006009                    11.71812     15.67553
      e.whpsleep |    784.6999    58.24589                    678.4557     907.5816
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      3.72, Prob > chi2 = 0.0537

. sem(avgcumdosew1->injselfr)(injselfr->whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  injselfr whpsleep


Exogenous variables


Observed:  avgcumdosew1


Fitting target model:

Iteration 0:   log likelihood = -2279.3942
Iteration 1:   log likelihood = -2279.3942

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2279.3942

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   injselfr <-    |
    avgcumdosew1 |    .0943398    .0441318      2.14    0.033      .0078431     .1808365
           _cons |    .6542977    .0283782     23.06    0.000      .5986776     .7099179
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
        injselfr |    16.72313    3.377628      4.95    0.000       10.1031     23.34315
           _cons |    14.77342    2.797423      5.28    0.000      9.290573     20.25627
-----------------+----------------------------------------------------------------
 Variance         |
      e.injselfr |    .2127443    .0157914                    .1839398     .2460594
      e.whpsleep |    892.1156    66.21903                    771.3279     1031.818
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      8.50, Prob > chi2 = 0.0036

. ********************  Wave 2 begins here
.
.
.
.
. //  wave 2 possible socio-demog covariates full Female model
. regress WHPsleep age avgcumdosew2 educ2-educ7 marrw21-marrw23 marrw25 childw2 ///
>   emplw22-emplw26 occ1w2-occ8w2 inc1w2-inc4w2 BSIhos if gender==2

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F( 31,   331) =    5.47
       Model |  117162.221    31  3779.42648           Prob > F      =  0.0000
    Residual |   228544.92   331  690.468036           R-squared     =  0.3389
-------------+------------------------------           Adj R-squared =  0.2770
       Total |  345707.141   362  954.992102           Root MSE      =  26.277

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7286643    .1705384      4.27    0.000      .3931885      1.06414
avgcumdosew2 |    1.532528    1.081265      1.42    0.157     -.5944892     3.659545
       educ2 |    21.71682     27.8679      0.78    0.436     -33.10371     76.53735
       educ3 |    25.26321    27.75576      0.91    0.363     -29.33671     79.86314
       educ4 |    23.59172    28.23839      0.84    0.404     -31.95762     79.14105
       educ5 |    17.52864    27.95234      0.63    0.531     -37.45799     72.51527
       educ6 |    17.13621    27.80216      0.62    0.538       -37.555     71.82741
       educ7 |   -8.069995    33.62357     -0.24    0.810     -74.21283     58.07284
     marrw21 |   -10.55008     8.86094     -1.19    0.235     -27.98093     6.880781
     marrw22 |    8.181714    12.30111      0.67    0.506     -16.01649     32.37992
     marrw23 |   -1.252346     7.35053     -0.17    0.865     -15.71199      13.2073
     marrw25 |   -3.813645    10.61513     -0.36    0.720     -24.69526     17.06797
     childw2 |   -1.057011    2.202239     -0.48    0.632     -5.389161     3.275138
     emplw22 |   -1.353672    11.69387     -0.12    0.908     -24.35736     21.65001
     emplw23 |    14.88901    12.45196      1.20    0.233     -9.605955     39.38397
     emplw24 |    2.622077    29.01238      0.09    0.928     -54.44982     59.69397
     emplw25 |    21.36149    13.74425      1.55    0.121     -5.675602     48.39858
     emplw26 |   -6.128173    12.74521     -0.48    0.631         -31.2     18.94365
      occ1w2 |    16.54683    13.84804      1.19    0.233     -10.69445      43.7881
      occ2w2 |    8.643981    14.21165      0.61    0.543     -19.31256     36.60052
      occ3w2 |    20.90548    14.23168      1.47    0.143     -7.090474     48.90142
      occ4w2 |     18.0996    15.22076      1.19    0.235     -11.84202     48.04122
      occ5w2 |    12.86955     16.5123      0.78    0.436     -19.61274     45.35184
      occ6w2 |    22.30044    16.48663      1.35    0.177     -10.13134     54.73222
      occ7w2 |    15.16114    15.26972      0.99    0.321     -14.87679     45.19908
      occ8w2 |     29.1472    15.25401      1.91    0.057     -.8598319     59.15424
      inc1w2 |   -19.49344    14.07882     -1.38    0.167     -47.18869     8.201811
      inc2w2 |   -19.09826    13.83681     -1.38    0.168     -46.31744     8.120916
      inc3w2 |   -21.45307    13.92428     -1.54    0.124     -48.84431      5.93817
      inc4w2 |   -18.74804    16.58025     -1.13    0.259       -51.364     13.86792
      BSIhos |    2.749011    .5096237      5.39    0.000      1.746502     3.751521
       _cons |   -48.00085    32.53307     -1.48    0.141     -111.9985     15.99681
-------------+----------------------------------------------------------------

.
.
. des bffsl2- bffsl12 bffsl17-bffsl23

              storage  display     value
variable name   type   format      label      variable label
-------------


bffsl2            float    %9.0g                         max(0, emplw25 + 1.14467e-009)


bffsl3            float    %9.0g                        max(, deaw1 - 1)* bffsl1


bffsl4            float    %9.0g                        max(0,1- deaw1) * bffsl1


bffsl5            float    %9.0g                        (docvisitsw1 = . )* bffsl4


bffsl6            float    %9.0g                        (docvisitsw1 != . )*bffsl4


bffsl7            float    %9.0g                        max(0, airw2-10)*bffsl5


bffsl8            float    %9.0g                        max(0, 10- airw2 )*bffsl5


bffsl9            float    %9.0g                        max(0, BSIhos - 9)*bffsl7


bffsl10           float    %9.0g                        max(0, 9 - BSIhos) * bffsl7


bffsl11           float    %9.0g                         max(0, cloud - 10) * bffsl6


bffsl12           float    %9.0g                        max(0, 10-cloud)*bffsl6


bffsl17           float    %9.0g                        max(0, suchrw1 + 1.85275e-007) * bffsl15


bffsl18           float    %9.0g                        (trgovw2 != . ) * bffsl1


bffsl19           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


bffsl20           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


bffsl23           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


.
. local `bfw2' bffsl2- bffsl12 bffsl17-bffsl23

.
. // trimmed female sleep for wave2 without basis functions
. regress WHPsleep age avgcumdosew2 educ2-educ7  ///
>    occ1w2-occ8w2 inc1w2-inc4w2 radhlw2 radchw2 ///
>     BSIhos  if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 22,   362) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.3113
                                                       Root MSE      =  26.501

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9011044    .1392482      6.47    0.000      .6272675     1.174941
avgcumdosew2 |    1.844007    .8744053      2.11    0.036      .1244552     3.563559
       educ2 |    13.97419    6.461045      2.16    0.031      1.268295     26.68009
       educ3 |    16.81994    5.388722      3.12    0.002      6.222806     27.41707
       educ4 |      16.914     8.61831      1.96    0.050     -.0342398     33.86224
       educ5 |     7.53451    6.437048      1.17    0.243     -5.124195     20.19321
       educ6 |    8.911264     5.29785      1.68    0.093     -1.507164     19.32969
       educ7 |   -19.62281    14.72413     -1.33    0.183     -48.57839     9.332765
      occ1w2 |    8.907211    7.172295      1.24    0.215     -5.197386     23.01181
      occ2w2 |    1.529251    7.540624      0.20    0.839     -13.29968     16.35818
      occ3w2 |    14.38519    7.593997      1.89    0.059     -.5487002     29.31908
      occ4w2 |    7.112592    8.774468      0.81    0.418     -10.14274     24.36792
      occ5w2 |    2.565107    11.43965      0.22    0.823     -19.93141     25.06162
      occ6w2 |     18.6715     10.3563      1.80    0.072     -1.694575     39.03758
      occ7w2 |    14.60983    7.647487      1.91    0.057     -.4292468     29.64891
      occ8w2 |     19.1525    8.157753      2.35    0.019      3.109958     35.19503
      inc1w2 |   -10.85566      7.2631     -1.49    0.136     -25.13883     3.427505
      inc2w2 |   -10.68552    7.115921     -1.50    0.134     -24.67925     3.308219
      inc3w2 |   -14.92295    7.394224     -2.02    0.044     -29.46398    -.3819185
      inc4w2 |   -14.55959    11.81457     -1.23    0.219     -37.79339     8.674211
     radhlw2 |    .1417907    .0482584      2.94    0.004      .0468887     .2366927
     radchw2 |   -.1275432    .0438479     -2.91    0.004     -.2137718    -.0413145
      BSIhos |    2.576862    .5333983      4.83    0.000      1.527913      3.62581
       _cons |   -51.02203    10.45707     -4.88    0.000     -71.58627    -30.45779
-------------+----------------------------------------------------------------

.
. // Trimmed female sleep for wave2 without basis functions
. regress WHPsleep age educ2-educ7 radhlw2 radchw2 occ1w2-occ8w2 inc1w2-inc4w2 ///
>    avgcumdosew2 BSIhos if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 22,   362) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.3113
                                                       Root MSE      =  26.501

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9011044    .1392482      6.47    0.000      .6272675     1.174941
       educ2 |    13.97419    6.461045      2.16    0.031      1.268295     26.68009
       educ3 |    16.81994    5.388722      3.12    0.002      6.222806     27.41707
       educ4 |      16.914     8.61831      1.96    0.050     -.0342398     33.86224
       educ5 |     7.53451    6.437048      1.17    0.243     -5.124195     20.19321
       educ6 |    8.911264     5.29785      1.68    0.093     -1.507164     19.32969
       educ7 |   -19.62281    14.72413     -1.33    0.183     -48.57839     9.332765
     radhlw2 |    .1417907    .0482584      2.94    0.004      .0468887     .2366927
     radchw2 |   -.1275432    .0438479     -2.91    0.004     -.2137718    -.0413145
      occ1w2 |    8.907211    7.172295      1.24    0.215     -5.197386     23.01181
      occ2w2 |    1.529251    7.540624      0.20    0.839     -13.29968     16.35818
      occ3w2 |    14.38519    7.593997      1.89    0.059     -.5487002     29.31908
      occ4w2 |    7.112592    8.774468      0.81    0.418     -10.14274     24.36792
      occ5w2 |    2.565107    11.43965      0.22    0.823     -19.93141     25.06162
      occ6w2 |     18.6715     10.3563      1.80    0.072     -1.694575     39.03758
      occ7w2 |    14.60983    7.647487      1.91    0.057     -.4292468     29.64891
      occ8w2 |     19.1525    8.157753      2.35    0.019      3.109958     35.19503
      inc1w2 |   -10.85566      7.2631     -1.49    0.136     -25.13883     3.427505
      inc2w2 |   -10.68552    7.115921     -1.50    0.134     -24.67925     3.308219
      inc3w2 |   -14.92295    7.394224     -2.02    0.044     -29.46398    -.3819185
      inc4w2 |   -14.55959    11.81457     -1.23    0.219     -37.79339     8.674211
avgcumdosew2 |    1.844007    .8744053      2.11    0.036      .1244552     3.563559
      BSIhos |    2.576862    .5333983      4.83    0.000      1.527913      3.62581
       _cons |   -51.02203    10.45707     -4.88    0.000     -71.58627    -30.45779
-------------+----------------------------------------------------------------

.
. scalar tw2nobf = e(r2_a)

. scalar list tw2nobf
   tw2nobf =  .26459544

. // Trimmed female sleep mode for wave 2 with basis functions
.
. local `bfw2' bffsl2- bffsl12 bffsl17-bffsl23

.
.
. title "Wave 2  female sleep model with basis functions"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****          Wave 2  female sleep model with basis functions            *****
*****                                                                     *****
*****                                                                     *****
*****                                            29 Jun 2012    00:25:11  *****
*******************************************************************************
*******************************************************************************


.
.
. des WHPsleep age radhlw2 radchw2 ///
>     bffsl2- bffsl3  bffsl10-bffsl12 bffsl18-bffsl19 bffsl23

              storage  display     value
variable name   type   format      label      variable label
-------------


WHPsleep          double   %9.0g                        Wtd Health Profile Sleep Pt 1 subscale


age               byte     %8.0g                    *   Respondent's age


radhlw2           byte     %8.0g                        how much believed personal health is affected by radiation in 1996


radchw2           byte     %8.0g                        believed % of polution related to chornobyl in 1996


bffsl2            float    %9.0g                         max(0, emplw25 + 1.14467e-009)


bffsl3            float    %9.0g                        max(, deaw1 - 1)* bffsl1


bffsl10           float    %9.0g                        max(0, 9 - BSIhos) * bffsl7


bffsl11           float    %9.0g                         max(0, cloud - 10) * bffsl6


bffsl12           float    %9.0g                        max(0, 10-cloud)*bffsl6


bffsl18           float    %9.0g                        (trgovw2 != . ) * bffsl1


bffsl19           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


bffsl23           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


. set more off

. regress WHPsleep age  avgcumdosew2 ///
>     radhlw2 radchw2 educ2-educ7 occ1w2-occ8w2 inc1w2-inc4w2 BSIhos ///
>     bffsl10 if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 23,   362) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.3409
                                                       Root MSE      =  25.964

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .8914268    .1347691      6.61    0.000      .6263981     1.156456
avgcumdosew2 |    2.154052    .8922784      2.41    0.016      .3993514     3.908752
     radhlw2 |    .1208159    .0476982      2.53    0.012      .0270155     .2146163
     radchw2 |   -.1292914    .0426107     -3.03    0.003      -.213087    -.0454957
       educ2 |    14.10377    6.455016      2.18    0.030       1.40973     26.79781
       educ3 |    15.52526    5.360612      2.90    0.004      4.983411     26.06711
       educ4 |      11.949    8.465017      1.41    0.159     -4.697786     28.59578
       educ5 |    6.170848    6.371697      0.97    0.333     -6.359342     18.70104
       educ6 |    8.879311    5.239619      1.69    0.091     -1.424604     19.18323
       educ7 |    -17.6245    14.13895     -1.25    0.213      -45.4293     10.18031
      occ1w2 |    9.025681    7.230648      1.25    0.213     -5.193669     23.24503
      occ2w2 |    1.533179    7.756436      0.20    0.843     -13.72015     16.78651
      occ3w2 |    14.65375    7.613966      1.92    0.055     -.3194108     29.62691
      occ4w2 |    6.126081    9.117084      0.67    0.502     -11.80302     24.05518
      occ5w2 |    3.567388    11.33283      0.31    0.753     -18.71906     25.85384
      occ6w2 |    19.59527    10.31017      1.90    0.058     -.6800677     39.87062
      occ7w2 |    11.74104     7.67155      1.53    0.127     -3.345358     26.82744
      occ8w2 |     18.1066    8.189295      2.21    0.028      2.002032     34.21116
      inc1w2 |   -11.36438    7.459033     -1.52    0.128     -26.03285     3.304102
      inc2w2 |   -13.44399     7.17852     -1.87    0.062     -27.56083     .6728512
      inc3w2 |   -15.91007    7.475102     -2.13    0.034     -30.61015    -1.209987
      inc4w2 |   -20.64762    12.80378     -1.61    0.108     -45.82674     4.531506
      BSIhos |    2.888212    .5357928      5.39    0.000      1.834555      3.94187
     bffsl10 |    .0048435    .0017371      2.79    0.006      .0014273     .0082596
       _cons |   -50.55326    10.28341     -4.92    0.000     -70.77598    -30.33054
-------------+----------------------------------------------------------------

. scalar tw2bf = e(r2_a)

. scalar list tw2bf
     tw2bf =  .29409811

.
.
.
. scalar w2numbf = 7

.
. scalar r2chabfw2 = tw2bf - tw2nobf

.
. matrix define FemaleWHPsleepr2w2 = ( fw2 , tw2bf, tw2nobf, w2numbf, r2chabfw2)

. matrix colnames FemaleWHPsleepr2w2 = FullBFR2a TR2aBF  TR2aNoBF  NumBF  BFR2cha

. matrix rownames FemaleWHPsleepr2w2 = wave2

. matlist FemaleWHPsleepr2w2

   | FullBFR2a    TR2aBF    TR2aNoBF    NumBF    BFR2cha
-------------+-------------------------------------------------------
wave2   |  .5774594     .2940981     .2645954            7     .0295027

.
. matrix define  FemaleWHPsleepr2 = (FemaleWHPsleepr2w1 \ FemaleWHPsleepr2w2)

. matlist FemaleWHPsleepr2

   | FullBFR~1    TR2BFw1    TR2NoBFw1    w1NumBF    BFR2chaw1
-------------+-------------------------------------------------------
wave1   |  .3392537     .3368749      .257486            3     .0793889
wave2   |  .5774594     .2940981     .2645954            7     .0295027

.
.
. title "Possible moderators for female sleep in wave two"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****          Possible moderators for female sleep in wave two           *****
*****                                                                     *****
*****                                                                     *****
*****                                            29 Jun 2012    00:25:11  *****
*******************************************************************************
*******************************************************************************


.
.  foreach var in age radhlw2 radchw2 educ2-educ7 occ1w2-occ8w2 ///
>    inc1w2-inc4w2 BSIhos bffsl10 {
  2.   cap gen `var'Xd2 = `var'*avgcumdosew2
  3.    }

.
. regress WHPsleep age  avgcumdosew2 ///
>     radhlw2 radchw2 educ2-educ7 occ1w2-occ8w2 inc1w2-inc4w2 BSIhos ///
>     bffsl10 ageXd2-bffsl10Xd2 if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 28,   362) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.3458
                                                       Root MSE      =   26.06

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .9122214    .1524348      5.98    0.000      .6124524      1.21199
avgcumdosew2 |   -5.792874      8.5095     -0.68    0.496     -22.52714     10.94139
     radhlw2 |    .1211012    .0527555      2.30    0.022      .0173554     .2248469
     radchw2 |   -.1923496    .0562736     -3.42    0.001     -.3030137    -.0816854
       educ2 |    13.42553    6.543081      2.05    0.041      .5583082     26.29275
       educ3 |    16.32046    5.429445      3.01    0.003      5.643246     26.99767
       educ4 |    13.02205    8.520468      1.53    0.127     -3.733781     29.77788
       educ5 |    6.727503    6.354296      1.06    0.290     -5.768467     19.22347
       educ6 |    9.831114    5.331736      1.84    0.066     -.6539513     20.31618
       educ7 |   -23.15517    20.14444     -1.15    0.251     -62.76999     16.45965
      occ1w2 |    8.762213    7.249121      1.21    0.228     -5.493465     23.01789
      occ2w2 |    .8362779    7.758422      0.11    0.914     -14.42096     16.09352
      occ3w2 |    14.47603    7.591366      1.91    0.057     -.4526834     29.40475
      occ4w2 |    5.693903    9.086708      0.63    0.531     -12.17546     23.56327
      occ5w2 |    3.840552    11.36545      0.34    0.736     -18.51005     26.19115
      occ6w2 |    18.50852    10.38486      1.78    0.076     -1.913714     38.93076
      occ7w2 |    10.72636    7.762381      1.38    0.168     -4.538659     25.99139
      occ8w2 |    17.58783     8.22275      2.14    0.033      1.417474     33.75819
      inc1w2 |   -11.04044    7.457408     -1.48    0.140     -25.70572     3.624843
      inc2w2 |   -13.24679     7.17003     -1.85    0.065     -27.34694     .8533474
      inc3w2 |   -15.13455    7.507838     -2.02    0.045     -29.89901    -.3700959
      inc4w2 |   -20.40045    12.82866     -1.59    0.113     -45.62851     4.827614
      BSIhos |     2.82735    .7002411      4.04    0.000      1.450299     4.204401
     bffsl10 |    .0046404    .0053909      0.86    0.390     -.0059609     .0152418
      ageXd2 |      .00884    .0860903      0.10    0.918       -.16046     .1781399
  radhlw2Xd2 |    .0088639    .0160287      0.55    0.581     -.0226572      .040385
  radchw2Xd2 |    .0732694    .0353776      2.07    0.039      .0036981     .1428407
   BSIhosXd2 |    .0875982    .5890687      0.15    0.882     -1.070828     1.246025
  bffsl10Xd2 |    .0003621    .0064965      0.06    0.956     -.0124136     .0131377
       _cons |   -46.83915    11.82517     -3.96    0.000      -70.0938     -23.5845
-------------+----------------------------------------------------------------

.
. **** Possible moderator is radchw2xd2
. summ radchw2 if gender==2

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     radchw2 |       363    62.52893    33.88298          0        100

. * cutpoints are 28.7 62.5 96
. cap drop lowradchw2Xd2

. cap gen loradchw2Xd2 = avgcumdosew2 if radchw2 < 28.7 & gender==2

. cap gen midradchw2Xd2 = avgcumdosew2 if radchw2 >= 28.7 & radchw2 < 62.5 & gender==2

. cap gen hiradchw2Xd2 = avgcumdosew2 if radchw2 >= 62.5 & radchw2  < . & gender==2

.  label var loradchw2Xd2 "cumdosew3 for male lower sd of radchw2 "

. label var midradchw2Xd2  "cumdosew3 for male mean plusminus 1 sd of radchw2"

. label var hiradchw2Xd2 "cumdosew3 for male upper sd of radchw2"

.
.
.
.
. twoway lfit WHPsleep loradchw2Xd2 || lfit WHPsleep midradchw2Xd2 || lfit WHPsleep hiradchw2Xd2,  ///
>      yti(WHPsleep) color(blue red green) ///
>      title("Effect of belief in proportion of pollution due " "to Chornobyl by Dose  on Sleep") ///
>      subtitle(for females in wave two) ///
>    legend(label( 1 "for lower radchw2 ") label( 2 "for mid-radchw2") label(3 "for high radchw2") rows(3))  ///
>    xti(avgcumdosew3 in milliGrays scale)

. graph save fradchw2Xd1onSlp2.gph, replace
(file fradchw2Xd1onSlp2.gph saved)

. graph export fradchw2Xd1onSlp2.eps, replace
(file fradchw2Xd1onSlp2.eps written in EPS format)

.
. title "Possible mediators for Female sleep in wave 2"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****           Possible mediators for Female sleep in wave 2             *****
*****                                                                     *****
*****                                                                     *****
*****                                            29 Jun 2012    00:25:13  *****
*******************************************************************************
*******************************************************************************


. * age, BSIanxiety, fear of eating contaminated food, fear of going outdoors,
. * PTSD in wave 1, depression in wave 2
. sem(avgcumdosew2->radhlw3)(radhlw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radhlw3 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -4175.5824
Iteration 1:   log likelihood = -4175.5824

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4175.5824

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radhlw3 <-     |
    avgcumdosew2 |    3.736223    1.305158      2.86    0.004       1.17816     6.294285
           _cons |    57.66773    2.145924     26.87    0.000       53.4618     61.87367
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radhlw3 |    .2449287    .0449114      5.45    0.000      .1569039     .3329534
           _cons |    11.30063    3.151775      3.59    0.000      5.123269       17.478
-----------------+----------------------------------------------------------------
 Variance         |
       e.radhlw3 |    1175.671    87.26648                    1016.491     1359.778
      e.whpsleep |    880.2405    65.33758                    761.0606     1018.084
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      4.38, Prob > chi2 = 0.0363

. sem(avgcumdosew2->age)(age-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  age whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -3769.7737
Iteration 1:   log likelihood = -3769.7737

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3769.7737

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   age <-         |
    avgcumdosew2 |    1.502324    .4441722      3.38    0.001      .6317629     2.372886
           _cons |    48.86944    .7303023     66.92    0.000      47.43808     50.30081
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
             age |    .9980208    .1262348      7.91    0.000      .7506051     1.245436
           _cons |   -23.87083    6.513018     -3.67    0.000     -36.63611    -11.10555
-----------------+----------------------------------------------------------------
 Variance         |
           e.age |     136.164    10.10704                    117.7281     157.4869
      e.whpsleep |    812.4615    60.30655                    702.4585     939.6906
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      2.68, Prob > chi2 = 0.1013

. sem(avgcumdosew2->radhlw2)(radhlw2-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radhlw2 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -4173.3473
Iteration 1:   log likelihood = -4173.3473

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4173.3473

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radhlw2 <-     |
    avgcumdosew2 |    3.302288    1.280292      2.58    0.010      .7929628     5.811614
           _cons |    56.95167    2.105039     27.05    0.000      52.82587     61.07747
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radhlw2 |    .2057984    .0464831      4.43    0.000      .1146932     .2969036
           _cons |    13.91546    3.200634      4.35    0.000      7.642335     20.18859
-----------------+----------------------------------------------------------------
 Variance         |
       e.radhlw2 |    1131.299    83.97289                    978.1272     1308.457
      e.whpsleep |    903.5692     67.0692                    781.2308     1045.066
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      5.16, Prob > chi2 = 0.0231

. sem(avgcumdosew2-> illw2)(illw2-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  illw2 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -2844.5594
Iteration 1:   log likelihood = -2844.5594

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2844.5594

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   illw2 <-       |
    avgcumdosew2 |    .1249912    .0330244      3.78    0.000      .0602647     .1897178
           _cons |     .301285    .0542982      5.55    0.000      .1948624     .4077076
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
           illw2 |    8.319311    1.778349      4.68    0.000       4.83381     11.80481
           _cons |    22.80692    1.736208     13.14    0.000      19.40402     26.20983
-----------------+----------------------------------------------------------------
 Variance         |
         e.illw2 |    .7527109    .0558714                    .6507978     .8705832
      e.whpsleep |    898.2096    66.67137                    776.5968     1038.867
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      3.91, Prob > chi2 = 0.0480

. sem(avgcumdosew2->radchw2)(radchw2-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radchw2 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -4182.5154
Iteration 1:   log likelihood = -4182.5154

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4182.5154

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radchw2 <-     |
    avgcumdosew2 |    2.827903     1.27938      2.21    0.027      .3203636     5.335442
           _cons |    59.99635    2.103541     28.52    0.000      55.87348     64.11921
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radchw2 |   -.0233248    .0478545     -0.49    0.626     -.1171179     .0704683
           _cons |    27.70313    3.402304      8.14    0.000      21.03474     34.37152
-----------------+----------------------------------------------------------------
 Variance         |
       e.radchw2 |    1129.689    83.85338                    976.7351     1306.595
      e.whpsleep |    951.7384    70.64465                    822.8781     1100.778
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      8.16, Prob > chi2 = 0.0043

. sem(avgcumdosew2->phlthw2)(phlthw2-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  phlthw2 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -3947.6519
Iteration 1:   log likelihood = -3947.6519

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3947.6519

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   phlthw2 <-     |
    avgcumdosew2 |   -1.107593    .6940629     -1.60    0.111     -2.467931     .2527452
           _cons |     80.1765     1.14117     70.26    0.000      77.93985     82.41315
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         phlthw2 |   -.4431316    .0854118     -5.19    0.000     -.6105357    -.2757275
           _cons |    61.33384    6.941518      8.84    0.000      47.72871     74.93897
-----------------+----------------------------------------------------------------
 Variance         |
       e.phlthw2 |    332.4737    24.67851                    287.4585     384.5381
      e.whpsleep |    886.6169    65.81088                    766.5736     1025.459
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      6.00, Prob > chi2 = 0.0143

. sem(avgcumdosew2->BSIanx)(BSIanx-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  BSIanx whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -3321.4125
Iteration 1:   log likelihood = -3321.4125

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3321.4125

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   BSIanx <-      |
    avgcumdosew2 |    .5377254    .1360644      3.95    0.000      .2710441     .8044067
           _cons |    8.496392    .2237153     37.98    0.000      8.057918     8.934866
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          BSIanx |    4.065276    .3890115     10.45    0.000      3.302827     4.827724
           _cons |   -10.25323    3.770224     -2.72    0.007     -17.64274     -2.86373
-----------------+----------------------------------------------------------------
 Variance         |
        e.BSIanx |    12.77758    .9484407                    11.04756     14.77851
      e.whpsleep |    732.1075    54.34211                     632.984     846.7534
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      1.11, Prob > chi2 = 0.2926

. sem(avgcumdosew2->injselfr)(injselfr->whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  injselfr whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -2614.6706
Iteration 1:   log likelihood = -2614.6706

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2614.6706

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   injselfr <-    |
    avgcumdosew2 |    .0313591    .0175903      1.78    0.075     -.0031173     .0658354
           _cons |    .6578662    .0289217     22.75    0.000      .6011807     .7145518
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
        injselfr |    16.72313    3.377628      4.95    0.000       10.1031     23.34315
           _cons |    14.77342    2.797423      5.28    0.000      9.290573     20.25627
-----------------+----------------------------------------------------------------
 Variance         |
      e.injselfr |    .2135527    .0158514                    .1846388     .2469945
      e.whpsleep |    892.1156    66.21903                    771.3279     1031.818
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      5.83, Prob > chi2 = 0.0158

. sem(avgcumdosew2->injothr)(injothr-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  injothr whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -2536.8389
Iteration 1:   log likelihood = -2536.8389

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2536.8389

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   injothr <-     |
    avgcumdosew2 |    .0165056      .01392      1.19    0.236      -.010777     .0437882
           _cons |    .8254385     .022887     36.07    0.000      .7805808     .8702963
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         injothr |    13.52601    4.363315      3.10    0.002      4.974069     22.07795
           _cons |    14.87983    3.999569      3.72    0.000      7.040818     22.71884
-----------------+----------------------------------------------------------------
 Variance         |
       e.injothr |    .1337321    .0099265                    .1156255     .1546742
      e.whpsleep |    927.7998    68.86776                    802.1806     1073.091
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      6.89, Prob > chi2 = 0.0087

. sem(avgcumdosew2->BSIhos)(BSIhos-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  BSIhos whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood =  -3268.543
Iteration 1:   log likelihood =  -3268.543

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    =  -3268.543

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   BSIhos <-      |
    avgcumdosew2 |     .039264    .1074326      0.37    0.715     -.1713001      .249828
           _cons |    7.449685    .1766394     42.17    0.000      7.103478     7.795892
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          BSIhos |    3.063457    .5507991      5.56    0.000      1.983911     4.143004
           _cons |    3.315143    4.406108      0.75    0.452     -5.320671     11.95096
-----------------+----------------------------------------------------------------
 Variance         |
        e.BSIhos |    7.965848    .5912807                    6.887314     9.213276
      e.whpsleep |    877.5761    65.13981                     758.757     1015.002
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      7.77, Prob > chi2 = 0.0053

. sem(avgcumdosew2->radchw2)(radchw2-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radchw2 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -4182.5154
Iteration 1:   log likelihood = -4182.5154

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4182.5154

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radchw2 <-     |
    avgcumdosew2 |    2.827903     1.27938      2.21    0.027      .3203636     5.335442
           _cons |    59.99635    2.103541     28.52    0.000      55.87348     64.11921
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radchw2 |   -.0233248    .0478545     -0.49    0.626     -.1171179     .0704683
           _cons |    27.70313    3.402304      8.14    0.000      21.03474     34.37152
-----------------+----------------------------------------------------------------
 Variance         |
       e.radchw2 |    1129.689    83.85338                    976.7351     1306.595
      e.whpsleep |    951.7384    70.64465                    822.8781     1100.778
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      8.16, Prob > chi2 = 0.0043

. sem(avgcumdosew2->fdferw2)(fdferw2-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  fdferw2 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood =  -4080.302
Iteration 1:   log likelihood =  -4080.302

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    =  -4080.302

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   fdferw2 <-     |
    avgcumdosew2 |    1.684583    .9692513      1.74    0.082     -.2151144     3.584281
           _cons |    13.66489    1.593631      8.57    0.000      10.54144     16.78835
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         fdferw2 |    .1116835     .063076      1.77    0.077     -.0119432     .2353103
           _cons |    24.55002    1.875403     13.09    0.000       20.8743     28.22574
-----------------+----------------------------------------------------------------
 Variance         |
       e.fdferw2 |    648.3842    48.12759                    560.5964     749.9193
      e.whpsleep |    944.2065    70.08558                     816.366     1092.067
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      6.96, Prob > chi2 = 0.0083

. sem(avgcumdosew2->sepaw2)(sepaw2-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  sepaw2 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -2207.9735
Iteration 1:   log likelihood = -2207.9735

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2207.9735

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   sepaw2 <-      |
    avgcumdosew2 |    -.000473    .0055882     -0.08    0.933     -.0114256     .0104796
           _cons |    .0224622     .009188      2.44    0.014      .0044541     .0404703
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          sepaw2 |   -23.62277    10.96315     -2.15    0.031     -45.11014    -2.135396
           _cons |    26.76527    1.627522     16.45    0.000      23.57538     29.95515
-----------------+----------------------------------------------------------------
 Variance         |
        e.sepaw2 |    .0215524    .0015998                    .0186344     .0249275
      e.whpsleep |     940.334    69.79814                    813.0178     1087.588
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      7.77, Prob > chi2 = 0.0053

. sem(avgcumdosew2->PTSDw2)(PTSDw2-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  PTSDw2 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -3664.2971
Iteration 1:   log likelihood = -3664.2971

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3664.2971

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   PTSDw2 <-      |
    avgcumdosew2 |     .538256    .3126879      1.72    0.085     -.0746011     1.151113
           _cons |    3.023189    .5141176      5.88    0.000      2.015537     4.030841
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          PTSDw2 |    .7224852    .1926812      3.75    0.000      .3448369     1.100134
           _cons |    23.71218    1.726821     13.73    0.000      20.32767     27.09668
-----------------+----------------------------------------------------------------
 Variance         |
        e.PTSDw2 |    67.48105    5.008914                    58.34448     78.04839
      e.whpsleep |    916.8496    68.05496                    792.7131     1060.426
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      6.25, Prob > chi2 = 0.0124

. sem(avgcumdosew2->depagw2)(depagw2-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  depagw2 whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -3975.9284
Iteration 1:   log likelihood = -3975.9284

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3975.9284

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   depagw2 <-     |
    avgcumdosew2 |    .9266396    .7350068      1.26    0.207     -.5139472     2.367226
           _cons |    8.991343    1.208489      7.44    0.000      6.622749     11.35994
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         depagw2 |    .2758213     .082439      3.35    0.001      .1142439     .4373987
           _cons |    23.53576    1.789031     13.16    0.000      20.02932     27.04219
-----------------+----------------------------------------------------------------
 Variance         |
       e.depagw2 |     372.857    27.67604                    322.3741     431.2453
      e.whpsleep |    923.8711    68.57614                    798.7838     1068.547
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      6.78, Prob > chi2 = 0.0092

. sem(avgcumdosew2->BSIdep)(BSIdep-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  BSIdep whpsleep


Exogenous variables


Observed:  avgcumdosew2


Fitting target model:

Iteration 0:   log likelihood = -3345.6897
Iteration 1:   log likelihood = -3345.6897

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3345.6897

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   BSIdep <-      |
    avgcumdosew2 |    .5124468    .1405158      3.65    0.000      .2370408     .7878528
           _cons |    9.196716    .2310344     39.81    0.000      8.743897     9.649535
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          BSIdep |    3.445067    .3911827      8.81    0.000      2.678363     4.211771
           _cons |   -7.019698    4.053191     -1.73    0.083     -14.96381     .9244117
-----------------+----------------------------------------------------------------
 Variance         |
        e.BSIdep |    13.62731    1.011514                    11.78225     15.76131
      e.whpsleep |    784.6999    58.24589                    678.4557     907.5816
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      2.01, Prob > chi2 = 0.1567

.
. //  wave 3 possible socio-demog covariates full Female model
.
. des WHPsleep age educ2-educ7 marrw21-marrw23 marrw25 childw2 ///
>   emplw22-emplw26 occ1w2-occ8w2 inc1w2-inc4w2 radhlw3 radtlw3 radchw3

              storage  display     value
variable name   type   format      label      variable label
-------------


WHPsleep          double   %9.0g                        Wtd Health Profile Sleep Pt 1 subscale


age               byte     %8.0g                    *   Respondent's age


educ2             byte     %8.0g                        educ==2. graduated high school


educ3             byte     %8.0g                        educ==3. technical degree


educ4             byte     %8.0g                        educ==4. did not finish college/bachelor's


educ5             byte     %8.0g                        educ==5. graduated college/bachelor's


educ6             byte     %8.0g                        educ==6. finished specialist/master's degree


educ7             byte     %8.0g                        educ==7. doctor of science/phd


marrw21           byte     %8.0g                        marrw2==1. single


marrw22           byte     %8.0g                        marrw2==2. cohabitating


marrw23           byte     %8.0g                        marrw2==3. married


marrw25           byte     %8.0g                        marrw2==5. divorced


childw2           byte     %8.0g                        number of children in 1996


emplw22           byte     %8.0g                        emplw2==1. full time


emplw23           byte     %8.0g                        emplw2==2. part time


emplw24           byte     %8.0g                        emplw2==3. voluntary


emplw25           byte     %8.0g                        emplw2==4. retired


emplw26           byte     %8.0g                        emplw2==5. unemployed


occ1w2            byte     %15.0g        LABJ           profess executive administration in 1996


occ2w2            byte     %15.0g        LABJ           technical sales admin support in 1996


occ3w2            byte     %15.0g        LABJ           service occup protective services in 1996


occ4w2            byte     %15.0g        LABJ           precision prod mechan craft construction in 1996


occ5w2            byte     %15.0g        LABJ           factory laborer machinist transp cleaner in 1996


occ6w2            byte     %15.0g        LABJ           farming agricul forestry fishing trapping logging in 1996


occ7w2            byte     %15.0g        LABJ           homemaking caregiving in 1996


occ8w2            byte     %15.0g        LABJ           student in 1996


inc1w2            byte     %15.0g        LABJ           Income is not sufficient for basic neccessities in 1996


inc2w2            byte     %15.0g        LABJ           Income is just sufficient for basic neccessities in 1996


inc3w2            byte     %15.0g        LABJ           Income is sufficient for basics plus extra purchases/savings in 1996


inc4w2            byte     %15.0g        LABJ           Income allows to comfortably afford luxury items in 1996


radhlw3           byte     %8.0g                        Self-perceived Chornobyl health threat in wave 3


radtlw3           byte     %8.0g                        believed % of cumulative radiation exposed to in a lifetime NOW


radchw3           byte     %8.0g                        believed % of polution related to chornobyl NOW


.
.
. title "Full female wave three model without basis functions"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****        Full female wave three model without basis functions         *****
*****                                                                     *****
*****                                                                     *****
*****                                            29 Jun 2012    00:25:16  *****
*******************************************************************************
*******************************************************************************


. regress WHPsleep age avgcumdosew2 educ2-educ7  ///
>   emplw22-emplw26 occ1w2-occ8w2 inc1w2-inc4w2 ///
>   radhlw3 radchw3  if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 25,   362) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.2983
                                                       Root MSE      =  26.909

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7427166    .1649309      4.50    0.000      .4183736      1.06706
avgcumdosew2 |    1.687798    .8614091      1.96    0.051     -.0061968     3.381792
       educ2 |    30.06657    8.215358      3.66    0.000      13.91075     46.22239
       educ3 |    36.03663    8.803373      4.09    0.000      18.72445      53.3488
       educ4 |    34.54477    10.51194      3.29    0.001      13.87264     55.21691
       educ5 |    27.67033    9.226143      3.00    0.003       9.52676      45.8139
       educ6 |    27.33203    8.792954      3.11    0.002      10.04034     44.62372
       educ7 |   -7.379096    16.20249     -0.46    0.649     -39.24192     24.48373
     emplw22 |    .5895921    6.339245      0.09    0.926     -11.87678     13.05596
     emplw23 |    15.87806    8.013316      1.98    0.048      .1195625     31.63655
     emplw24 |    18.49187    7.898133      2.34    0.020      2.959883     34.02385
     emplw25 |    23.33398    10.69182      2.18    0.030      2.308111     44.35985
     emplw26 |    3.654458    6.296151      0.58    0.562     -8.727168     16.03608
      occ1w2 |    16.87438    6.937686      2.43    0.015      3.231149     30.51761
      occ2w2 |    9.640776    7.474695      1.29    0.198     -5.058502     24.34005
      occ3w2 |    23.72966    7.953086      2.98    0.003      8.089606     39.36971
      occ4w2 |    18.15649    8.946031      2.03    0.043       .563773     35.74921
      occ5w2 |    18.26299    13.21503      1.38    0.168     -7.724881     44.25087
      occ6w2 |    25.66908    12.76855      2.01    0.045      .5592253     50.77893
      occ7w2 |    12.27132    9.097276      1.35    0.178      -5.61883     30.16146
      occ8w2 |    23.47595    9.558157      2.46    0.015      4.679466     42.27244
      inc1w2 |    -17.9678    7.418056     -2.42    0.016      -32.5557    -3.379905
      inc2w2 |   -18.46107    7.016913     -2.63    0.009      -32.2601    -4.662038
      inc3w2 |   -20.04603    7.438916     -2.69    0.007     -34.67494    -5.417109
      inc4w2 |   -21.93059    11.62547     -1.89    0.060     -44.79253     .9313551
     radhlw3 |    .1740996    .0510472      3.41    0.001      .0737133      .274486
     radchw3 |   -.1118385    .0450148     -2.48    0.013      -.200362    -.0233151
       _cons |   -49.71701    10.90123     -4.56    0.000      -71.1547    -28.27933
-------------+----------------------------------------------------------------

.
.
.
. title "Full female wave three model with basis functions"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****         Full female wave three model with basis functions           *****
*****                                                                     *****
*****                                                                     *****
*****                                            29 Jun 2012    00:25:16  *****
*******************************************************************************
*******************************************************************************


. des bffsl2-bffsl25

              storage  display     value
variable name   type   format      label      variable label
-------------


bffsl2            float    %9.0g                         max(0, emplw25 + 1.14467e-009)


bffsl3            float    %9.0g                        max(, deaw1 - 1)* bffsl1


bffsl4            float    %9.0g                        max(0,1- deaw1) * bffsl1


bffsl5            float    %9.0g                        (docvisitsw1 = . )* bffsl4


bffsl6            float    %9.0g                        (docvisitsw1 != . )*bffsl4


bffsl7            float    %9.0g                        max(0, airw2-10)*bffsl5


bffsl8            float    %9.0g                        max(0, 10- airw2 )*bffsl5


bffsl9            float    %9.0g                        max(0, BSIhos - 9)*bffsl7


bffsl10           float    %9.0g                        max(0, 9 - BSIhos) * bffsl7


bffsl11           float    %9.0g                         max(0, cloud - 10) * bffsl6


bffsl12           float    %9.0g                        max(0, 10-cloud)*bffsl6


bffsl13           float    %9.0g                        max(0, airw3 - 20) * bffsl8


bffsl15           float    %9.0g                        (suchrw1 != . ) * bffsl8


bffsl17           float    %9.0g                        max(0, suchrw1 + 1.85275e-007) * bffsl15


bffsl18           float    %9.0g                        (trgovw2 != . ) * bffsl1


bffsl19           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


bffsl20           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


bffsl23           float    %9.0g                        max(0, trgovw2 - 10) * bffsl18


bffsl24           float    %9.0g                        max(0, phlthw3 - 40) * bffsl18


bffsl25           float    %9.0g                         max(0, 40 - phlthw3) * bffsl18


.
. local bfw3  bffsl10

. regress WHPsleep age avgcumdosew2 educ2-educ5 marrw31-marrw33 marrw35  ///
>   emplw32-emplw34 occ1w3-occ7w3  inc1w3-inc4w3 radhlw3 radchw3 ///
>   havmil bffsl10  BSIhos if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 28,   362) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.3649
                                                       Root MSE      =  25.677

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .4882763    .1680419      2.91    0.004      .1578153     .8187372
avgcumdosew2 |    1.688826    .9729002      1.74    0.083     -.2244199     3.602072
       educ2 |    7.675207    5.794773      1.32    0.186     -3.720438     19.07085
       educ3 |    6.132777     3.30265      1.86    0.064     -.3620122     12.62757
       educ4 |    1.787371     7.13958      0.25    0.802     -12.25289     15.82763
       educ5 |   -4.862332    4.402797     -1.10    0.270      -13.5206     3.795939
     marrw31 |   -15.17928    5.582338     -2.72    0.007     -26.15717      -4.2014
     marrw32 |    6.111993    11.28906      0.54    0.589     -16.08838     28.31237
     marrw33 |    -1.71739    4.594214     -0.37    0.709     -10.75209     7.317309
     marrw35 |    3.621643    7.468505      0.48    0.628     -11.06546     18.30875
     emplw32 |   -3.306185    5.050647     -0.65    0.513     -13.23848     6.626109
     emplw33 |   -.2230059    5.517028     -0.04    0.968     -11.07246     10.62644
     emplw34 |    12.77977    5.639444      2.27    0.024      1.689587     23.86996
      occ1w3 |    11.43249    4.560934      2.51    0.013      2.463232     20.40174
      occ2w3 |    2.060698    4.693638      0.44    0.661     -7.169522     11.29092
      occ3w3 |    11.96934    5.303341      2.26    0.025      1.540113     22.39856
      occ4w3 |   -.1502708    6.117542     -0.02    0.980     -12.18065     11.88011
      occ5w3 |    19.85864     15.5609      1.28    0.203     -10.74248     50.45975
      occ6w3 |   -.7354694    14.28713     -0.05    0.959     -28.83166     27.36072
      occ7w3 |     .359425    4.320353      0.08    0.934     -8.136716     8.855566
      inc1w3 |   -1.070833    4.330438     -0.25    0.805     -9.586806     7.445141
      inc2w3 |   -11.19944    4.909908     -2.28    0.023     -20.85496    -1.543913
      inc3w3 |   -12.63728    4.810354     -2.63    0.009     -22.09702    -3.177529
      inc4w3 |   -5.541647    12.52949     -0.44    0.659     -30.18137     19.09808
     radhlw3 |    .1344038    .0471274      2.85    0.005      .0417259     .2270818
     radchw3 |   -.0744139     .039826     -1.87    0.063     -.1527333     .0039054
      havmil |    .0055066    .0050917      1.08    0.280     -.0045064     .0155197
     bffsl10 |    .0045609    .0018027      2.53    0.012      .0010158      .008106
      BSIhos |    2.746736    .5393766      5.09    0.000      1.686031     3.807441
       _cons |   -27.00168    10.50159     -2.57    0.011     -47.65346    -6.349906
-------------+----------------------------------------------------------------

.
. scalar fw3wbf = e(r2_a)

. scalar list fw3
       fw3 =  .63678289

.
.
.
.
. // wave 3 trimmed model of possible socio-demog covariates Female model
.
. sw, pr(.05):  regress WHPsleep age educ2-educ6 marrw31-marrw33   ///
>   emplw31-emplw33 occ2w3-occ7w3 inc1w3-inc4w3 if gender==2


  begin with full model


p = 0.8978 >= 0.0500  removing inc3w3


p = 0.8734 >= 0.0500  removing inc2w3


p = 0.8331 >= 0.0500  removing marrw32


p = 0.7755 >= 0.0500  removing emplw33


p = 0.7085 >= 0.0500  removing occ3w3


p = 0.6424 >= 0.0500  removing educ6


p = 0.8462 >= 0.0500  removing educ5


p = 0.5565 >= 0.0500  removing inc4w3


p = 0.4618 >= 0.0500  removing occ5w3


p = 0.3247 >= 0.0500  removing occ6w3


p = 0.3185 >= 0.0500  removing occ4w3


p = 0.2429 >= 0.0500  removing marrw33


p = 0.1992 >= 0.0500  removing educ4


p = 0.1428 >= 0.0500  removing educ2


p = 0.0768 >= 0.0500  removing emplw32


p = 0.1257 >= 0.0500  removing occ7w3


p = 0.0662 >= 0.0500  removing emplw31


      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  5,   357) =   19.93
       Model |  75437.4701     5   15087.494           Prob > F      =  0.0000
    Residual |  270269.671   357  757.057901           R-squared     =  0.2182
-------------+------------------------------           Adj R-squared =  0.2073
       Total |  345707.141   362  954.992102           Root MSE      =  27.515

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .8189911    .1267422      6.46    0.000       .569736     1.068246
      inc1w3 |    11.66423    4.116894      2.83    0.005      3.567816     19.76064
       educ3 |     8.23918    3.072232      2.68    0.008      2.197233     14.28113
      occ2w3 |   -14.30809    4.857046     -2.95    0.003     -23.86011    -4.756069
     marrw31 |   -11.67479    5.484449     -2.13    0.034     -22.46068    -.8889018
       _cons |   -17.25879     6.67096     -2.59    0.010     -30.37811    -4.139475
-------------+----------------------------------------------------------------

.
. regress  WHPsleep age avgcumdosew3  marrw31-marrw33  emplw31-emplw33 ///
>     BSIhos if gender==2

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F(  9,   353) =   13.75
       Model |  89747.8284     9  9971.98093           Prob > F      =  0.0000
    Residual |  255959.312   353  725.097202           R-squared     =  0.2596
-------------+------------------------------           Adj R-squared =  0.2407
       Total |  345707.141   362  954.992102           Root MSE      =  26.928

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .6759349    .1468074      4.60    0.000      .3872078      .964662
avgcumdosew3 |    1.414307    .8214894      1.72    0.086     -.2013218     3.029936
     marrw31 |    -16.3239    6.186269     -2.64    0.009     -28.49048    -4.157319
     marrw32 |    1.190303    10.26874      0.12    0.908      -19.0053     21.38591
     marrw33 |   -6.121742    3.611338     -1.70    0.091     -13.22419     .9807022
     emplw31 |   -9.117175    3.495174     -2.61    0.009     -15.99116    -2.243191
     emplw32 |   -8.694552    6.244721     -1.39    0.165     -20.97609     3.586985
     emplw33 |   -21.92251    27.09071     -0.81    0.419       -75.202     31.35698
      BSIhos |    2.861133     .503412      5.68    0.000      1.871069     3.851197
       _cons |   -19.56301     10.5665     -1.85    0.065     -40.34423      1.21821
-------------+----------------------------------------------------------------

.
. local bfw3 bffsl9 bffsl10 bffsl12 bffsl13 bffsl24 bffsl25

.
. regress WHPsleep age avgcumdosew3  marrw31-marrw33 marrw35  ///
>   radhlw3 radchw3 ///
>    bffsl10  BSIhos if gender==2

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F( 10,   352) =   15.50
       Model |  105687.391    10  10568.7391           Prob > F      =  0.0000
    Residual |  240019.749   352  681.874288           R-squared     =  0.3057
-------------+------------------------------           Adj R-squared =  0.2860
       Total |  345707.141   362  954.992102           Root MSE      =  26.113

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7619818    .1324233      5.75    0.000      .5015415     1.022422
avgcumdosew3 |    1.514995    .7990515      1.90    0.059     -.0565207      3.08651
     marrw31 |   -14.10159    6.453492     -2.19    0.030     -26.79385    -1.409342
     marrw32 |    5.353814     10.2582      0.52    0.602     -14.82125     25.52888
     marrw33 |   -3.796629    4.139713     -0.92    0.360     -11.93831     4.345053
     marrw35 |    3.629092    6.306794      0.58    0.565     -8.774645     16.03283
     radhlw3 |    .1557481    .0460893      3.38    0.001      .0651031      .246393
     radchw3 |   -.0993872    .0437691     -2.27    0.024     -.1854691    -.0133053
     bffsl10 |    .0047105    .0011763      4.00    0.000       .002397     .0070241
      BSIhos |    2.980555     .499805      5.96    0.000      1.997575     3.963534
       _cons |   -37.80625    9.195954     -4.11    0.000     -55.89217    -19.72033
-------------+----------------------------------------------------------------

.
. scalar tw3bf = e(r2_a)

. scalar list tw3bf
     tw3bf =   .2859896

.
. regress WHPsleep age avgcumdosew3 marrw31-marrw33 marrw35 childw3 ///
>   emplw32-emplw34  inc1w3-inc4w3 radhlw3 radchw3 ///
>    BSIhos if gender==2

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F( 17,   345) =    8.80
       Model |  104587.982    17  6152.23426           Prob > F      =  0.0000
    Residual |  241119.158   345  698.896111           R-squared     =  0.3025
-------------+------------------------------           Adj R-squared =  0.2682
       Total |  345707.141   362  954.992102           Root MSE      =  26.437

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .4832453    .1670392      2.89    0.004       .154702     .8117887
avgcumdosew3 |    1.316681    .8131783      1.62    0.106     -.2827302     2.916092
     marrw31 |   -16.80699    7.030174     -2.39    0.017     -30.63438    -2.979592
     marrw32 |    4.236803    10.39682      0.41    0.684     -16.21232     24.68593
     marrw33 |   -3.712888    4.226796     -0.88    0.380     -12.02642     4.600644
     marrw35 |    3.369539    6.423136      0.52    0.600     -9.263896     16.00297
     childw3 |   -.2526361    1.913173     -0.13    0.895     -4.015587     3.510315
     emplw32 |    .8995578    5.807647      0.15    0.877     -10.52329     12.32241
     emplw33 |   -10.78011    26.61301     -0.41    0.686     -63.12428     41.56406
     emplw34 |    9.353136    4.321233      2.16    0.031       .853859     17.85241
      inc1w3 |    6.235478    5.140047      1.21    0.226     -3.874295     16.34525
      inc2w3 |   -2.810748     4.30041     -0.65    0.514     -11.26907     5.647572
      inc3w3 |   -5.099266    4.630256     -1.10    0.272     -14.20635     4.007817
      inc4w3 |    1.830134    10.62954      0.17    0.863     -19.07673     22.73699
     radhlw3 |    .1529282    .0479937      3.19    0.002      .0585311     .2473253
     radchw3 |   -.0955692    .0451323     -2.12    0.035     -.1843383    -.0068001
      BSIhos |    2.467077    .5053882      4.88    0.000      1.473048     3.461107
       _cons |   -18.88468    11.31944     -1.67    0.096     -41.14848     3.379117
-------------+----------------------------------------------------------------

.
. scalar tw3nobf = e(r2_a)

. scalar list tw3nobf
   tw3nobf =  .26816556

.
. scalar r2chabfw3 = tw3bf - tw3nobf

. scalar list r2chabfw3
 r2chabfw3 =  .01782405

.
. scalar w3numbf = 1

.
.
. matrix define FemaleWHPsleepr2w3 = ( fw3 , tw3bf, tw3nobf, w3numbf, r2chabfw3)

. matrix colnames FemaleWHPsleepr2w3 = FullBFR2a TR2aBF  TR2aNoBF  NumBF  BFR2cha

. matrix rownames FemaleWHPsleepr2w3 = wave3

. matlist FemaleWHPsleepr2w3

   | FullBFR2a    TR2aBF    TR2aNoBF    NumBF    BFR2cha
-------------+-------------------------------------------------------
wave3   |  .6367829     .2859896     .2681656            1      .017824

.
. matrix define  FemaleWHPsleepr2 = (FemaleWHPsleepr2w1 \ FemaleWHPsleepr2w2 \ FemaleWHPsleepr2w3 )

. matlist FemaleWHPsleepr2

   | FullBFR~1    TR2BFw1    TR2NoBFw1    w1NumBF    BFR2chaw1
-------------+-------------------------------------------------------
wave1   |  .3392537     .3368749      .257486            3     .0793889
wave2   |  .5774594     .2940981     .2645954            7     .0295027
wave3   |  .6367829     .2859896     .2681656            1      .017824

.
. matrix define H1p1FemDoseSleep= FemaleWHPsleepr2

. title "Possible moderators for female sleep in wave three: bffsl10Xd3"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****   Possible moderators for female sleep in wave three: bffsl10Xd3    *****
*****                                                                     *****
*****                                                                     *****
*****                                            29 Jun 2012    00:25:16  *****
*******************************************************************************
*******************************************************************************


.
.
.  foreach var in age marrw31-marrw33 marrw35  ///
>   radhlw3 radchw3 bffsl10  BSIhos {
  2.  cap gen `var'Xd3 = `var'*avgcumdosew3
  3.   }

.
. regress WHPsleep age avgcumdosew3 marrw31-marrw33 marrw35 childw3 ///
>   emplw32-emplw34  inc1w3-inc4w3 radhlw3 radchw3 ///
>    BSIhos ageXd3 radchw3Xd3-BSIhosXd3 if gender==2

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F( 21,   341) =    8.06
       Model |   114652.42    21  5459.63905           Prob > F      =  0.0000
    Residual |  231054.721   341  677.579826           R-squared     =  0.3316
-------------+------------------------------           Adj R-squared =  0.2905
       Total |  345707.141   362  954.992102           Root MSE      =   26.03

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .5587434    .1836397      3.04    0.003      .1975342     .9199526
avgcumdosew3 |     1.43102    7.515935      0.19    0.849     -13.35241     16.21445
     marrw31 |   -15.86278    6.984458     -2.27    0.024     -29.60083    -2.124738
     marrw32 |    5.876703    10.26938      0.57    0.568      -14.3226     26.07601
     marrw33 |   -3.082632    4.187486     -0.74    0.462     -11.31919     5.153922
     marrw35 |    2.467704    6.344745      0.39    0.698     -10.01206     14.94747
     childw3 |   -.2138891    1.888029     -0.11    0.910     -3.927538      3.49976
     emplw32 |   -2.961668    5.816309     -0.51    0.611     -14.40203     8.478692
     emplw33 |   -10.82915    26.20437     -0.41    0.680     -62.37172     40.71341
     emplw34 |    7.821716    4.317577      1.81    0.071     -.6707209     16.31415
      inc1w3 |    4.084687    5.118908      0.80    0.425     -5.983925      14.1533
      inc2w3 |   -4.312996    4.253494     -1.01    0.311     -12.67939     4.053393
      inc3w3 |   -7.069872    4.590758     -1.54    0.124     -16.09964     1.959897
      inc4w3 |   -.0881462    10.48177     -0.01    0.993     -20.70521     20.52892
     radhlw3 |    .1392344    .0476703      2.92    0.004      .0454696     .2329992
     radchw3 |   -.1082018    .0609507     -1.78    0.077     -.2280885     .0116848
      BSIhos |    2.553895    .7168089      3.56    0.000      1.143971     3.963819
      ageXd3 |   -.0470208    .1005569     -0.47    0.640     -.2448107     .1507692
  radchw3Xd3 |    .0123911    .0375527      0.33    0.742     -.0614729     .0862552
  bffsl10Xd3 |    .0041303    .0011078      3.73    0.000      .0019514     .0063093
   BSIhosXd3 |    .2219479    .5486449      0.40    0.686     -.8572065     1.301102
       _cons |   -21.54971    12.86766     -1.67    0.095     -46.85969      3.76028
-------------+----------------------------------------------------------------

.
.
. des bffsl10 bffsl7 bffsl5 bffsl4 bffsl1

              storage  display     value
variable name   type   format      label      variable label
-------------


bffsl10           float    %9.0g                        max(0, 9 - BSIhos) * bffsl7


bffsl7            float    %9.0g                        max(0, airw2-10)*bffsl5


bffsl5            float    %9.0g                        (docvisitsw1 = . )* bffsl4


bffsl4            float    %9.0g                        max(0,1- deaw1) * bffsl1


bffsl1            float    %9.0g                        max(0, BSIposymp-15)


.
. summ bffsl10 if gender==2

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     bffsl10 |       363    326.5234    1195.353          0       8400

. * cutpoints are  0 and 0
. tab bffsl10 oblnow

max(0, 9 - |
 BSIhos) * |    current oblast
    bffsl7 |  10. kyiv  23. zhyto |     Total
-----------+----------------------+----------
         0 |       514         96 |       610
       120 |         1          0 |         1
       160 |         3          0 |         3
       320 |         2          0 |         2
       340 |         2          0 |         2
       360 |         1          0 |         1
       399 |         1          0 |         1
       440 |         1          0 |         1
       464 |         1          0 |         1
       480 |         5          0 |         5
       520 |         1          0 |         1
       560 |         0          1 |         1
       580 |         1          0 |         1
       600 |         4          0 |         4
       660 |         1          0 |         1
       720 |         2          0 |         2
       750 |         1          0 |         1
       800 |         2          0 |         2
       880 |         1          0 |         1
       900 |         1          0 |         1
       920 |         2          0 |         2
       945 |         1          0 |         1
       960 |         2          0 |         2
       990 |         2          0 |         2
      1080 |         1          1 |         2
      1120 |         3          0 |         3
      1200 |         1          0 |         1
      1320 |         1          0 |         1
      1350 |         1          0 |         1
      1440 |         2          0 |         2
      1456 |         1          0 |         1
      1480 |         1          0 |         1
      1500 |         1          0 |         1
      1520 |         2          0 |         2
      1560 |         2          0 |         2
      1650 |         1          0 |         1
      1680 |         1          0 |         1
      1800 |         3          0 |         3
      1920 |         2          0 |         2
      2100 |         2          0 |         2
      2340 |         1          0 |         1
      2520 |         1          0 |         1
      2760 |         1          0 |         1
      3300 |         0          1 |         1
      3840 |         2          0 |         2
      3960 |         1          0 |         1
      4080 |         1          0 |         1
      4230 |         1          0 |         1
      4320 |         2          0 |         2
      5100 |         1          0 |         1
      5400 |         4          0 |         4
      5760 |         1          0 |         1
      5904 |         1          0 |         1
      6020 |         1          0 |         1
      6400 |         1          0 |         1
      6440 |         1          0 |         1
      6840 |         1          0 |         1
      7020 |         1          0 |         1
      7080 |         1          0 |         1
      7200 |         1          0 |         1
      7360 |         1          0 |         1
      8400 |         1          0 |         1
     21600 |         1          0 |         1
-----------+----------------------+----------
     Total |       604         99 |       703


.
.
.
. title "Possible mediators for Female sleep in wave 3"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****           Possible mediators for Female sleep in wave 3             *****
*****                                                                     *****
*****                                                                     *****
*****                                            29 Jun 2012    00:25:16  *****
*******************************************************************************
*******************************************************************************


. * age, BSIanxiety, fear of eating contaminated food, fear of going outdoors,
. * PTSD in wave 1, depression in wave 3
.
. sem(avgcumdosew3->age)(age-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  age whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -3858.2134
Iteration 1:   log likelihood = -3858.2134

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3858.2134

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   age <-         |
    avgcumdosew3 |    1.058366    .3502924      3.02    0.003      .3718061     1.744927
           _cons |    48.94293    .7447571     65.72    0.000      47.48323     50.40263
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
             age |    .9980208    .1262348      7.91    0.000      .7506051     1.245436
           _cons |   -23.87083    6.513018     -3.67    0.000     -36.63611    -11.10555
-----------------+----------------------------------------------------------------
 Variance         |
           e.age |    137.0097    10.16981                    118.4593      158.465
      e.whpsleep |    812.4615    60.30655                    702.4585     939.6906
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      2.92, Prob > chi2 = 0.0876

. sem(avgcumdosew3->radhlw3)(radhlw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radhlw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -4263.3998
Iteration 1:   log likelihood = -4263.3998

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4263.3998

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radhlw3 <-     |
    avgcumdosew3 |    2.751602    1.027538      2.68    0.007      .7376654     4.765539
           _cons |    57.70689    2.184649     26.41    0.000      53.42506     61.98873
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radhlw3 |    .2449287    .0449114      5.45    0.000      .1569039     .3329534
           _cons |    11.30063    3.151775      3.59    0.000      5.123269       17.478
-----------------+----------------------------------------------------------------
 Variance         |
       e.radhlw3 |    1178.923    87.50785                    1019.303     1363.539
      e.whpsleep |    880.2405    65.33758                    761.0606     1018.084
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      4.30, Prob > chi2 = 0.0381

. sem(avgcumdosew3-> radfmw3)(radfmw3->whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radfmw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood =  -4255.987
Iteration 1:   log likelihood =  -4255.987

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    =  -4255.987

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radfmw3 <-     |
    avgcumdosew3 |    2.423642    .9998022      2.42    0.015      .4640659     4.383218
           _cons |    65.65475    2.125681     30.89    0.000       61.4885     69.82101
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radfmw3 |    .2296432    .0465604      4.93    0.000      .1383866     .3208998
           _cons |     10.4986     3.55683      2.95    0.003      3.527338     17.46985
-----------------+----------------------------------------------------------------
 Variance         |
       e.radfmw3 |    1116.138    82.84757                    965.0192     1290.923
      e.whpsleep |    892.5477    66.25111                    771.7015     1032.318
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      4.80, Prob > chi2 = 0.0285

. sem(avgcumdosew3-> illw3)(illw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  illw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -3030.1232
Iteration 1:   log likelihood = -3030.1232

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3030.1232

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   illw3 <-       |
    avgcumdosew3 |    .1284565    .0341324      3.76    0.000      .0615584     .1953547
           _cons |    .5563644    .0725688      7.67    0.000      .4141321     .6985968
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
           illw3 |    6.624894    1.349148      4.91    0.000      3.980613     9.269174
           _cons |    21.53605    1.838383     11.71    0.000      17.93289     25.13922
-----------------+----------------------------------------------------------------
 Variance         |
         e.illw3 |    1.300836    .0965571                    1.124709     1.504543
      e.whpsleep |    893.0409    66.28772                    772.1279     1032.889
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      3.52, Prob > chi2 = 0.0608

. sem(avgcumdosew3->radchw3)(radchw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radchw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -4279.7422
Iteration 1:   log likelihood = -4279.7422

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4279.7422

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radchw3 <-     |
    avgcumdosew3 |    1.497864    1.033363      1.45    0.147     -.5274916     3.523219
           _cons |     59.6792    2.197035     27.16    0.000      55.37309     63.98531
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radchw3 |    .0035358    .0467727      0.08    0.940     -.0881371     .0952087
           _cons |    26.02728     3.30036      7.89    0.000      19.55869     32.49586
-----------------+----------------------------------------------------------------
 Variance         |
       e.radchw3 |    1192.329    88.50296                    1030.894     1379.044
      e.whpsleep |    952.3463    70.68977                    823.4036     1101.481
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      7.37, Prob > chi2 = 0.0066

. sem(avgcumdosew3->phlthw3)(phlthw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  phlthw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -4056.3777
Iteration 1:   log likelihood = -4056.3777

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4056.3777

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   phlthw3 <-     |
    avgcumdosew3 |   -1.596683    .5946571     -2.69    0.007      -2.76219    -.4311767
           _cons |     67.2054    1.264301     53.16    0.000      64.72741     69.68338
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         phlthw3 |   -.5281408    .0758079     -6.97    0.000     -.6767215      -.37956
           _cons |    60.72512    5.177747     11.73    0.000      50.57692     70.87332
-----------------+----------------------------------------------------------------
 Variance         |
       e.phlthw3 |    394.8419    29.30791                    341.3824      456.673
      e.whpsleep |    840.0396    62.35359                    726.3027     971.5874
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      3.71, Prob > chi2 = 0.0542

. sem(avgcumdosew3->BSIanx)(BSIanx-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  BSIanx whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -3409.2829
Iteration 1:   log likelihood = -3409.2829

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3409.2829

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   BSIanx <-      |
    avgcumdosew3 |    .4074564    .1071377      3.80    0.000      .1974703     .6174425
           _cons |    8.488279    .2277857     37.26    0.000      8.041828     8.934731
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          BSIanx |    4.065276    .3890115     10.45    0.000      3.302827     4.827724
           _cons |   -10.25323    3.770224     -2.72    0.007     -17.64274     -2.86373
-----------------+----------------------------------------------------------------
 Variance         |
        e.BSIanx |    12.81666    .9513419                    11.08135     14.82371
      e.whpsleep |    732.1075    54.34211                     632.984     846.7534
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      1.11, Prob > chi2 = 0.2921

. sem(avgcumdosew3->injselfr)(injselfr->whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  injselfr whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood =   -2702.32
Iteration 1:   log likelihood =   -2702.32

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    =   -2702.32

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   injselfr <-    |
    avgcumdosew3 |    .0219131    .0138422      1.58    0.113     -.0052172     .0490434
           _cons |    .6596152      .02943     22.41    0.000      .6019334      .717297
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
        injselfr |    16.72313    3.377628      4.95    0.000       10.1031     23.34315
           _cons |    14.77342    2.797423      5.28    0.000      9.290573     20.25627
-----------------+----------------------------------------------------------------
 Variance         |
      e.injselfr |    .2139454    .0158805                    .1849783     .2474486
      e.whpsleep |    892.1156    66.21903                    771.3279     1031.818
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      5.74, Prob > chi2 = 0.0166

. sem(avgcumdosew3->injothr)(injothr-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  injothr whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -2624.2226
Iteration 1:   log likelihood = -2624.2226

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2624.2226

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   injothr <-     |
    avgcumdosew3 |    .0123358     .010946      1.13    0.260     -.0091179     .0337895
           _cons |    .8253952    .0232722     35.47    0.000      .7797825     .8710079
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         injothr |    13.52601    4.363315      3.10    0.002      4.974069     22.07795
           _cons |    14.87983    3.999569      3.72    0.000      7.040818     22.71884
-----------------+----------------------------------------------------------------
 Variance         |
       e.injothr |     .133782    .0099302                    .1156686     .1547319
      e.whpsleep |    927.7998    68.86776                    802.1806     1073.091
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      6.59, Prob > chi2 = 0.0103

. sem(avgcumdosew3->BSIhos)(BSIhos-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  BSIhos whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood =  -3355.883
Iteration 1:   log likelihood =  -3355.883

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    =  -3355.883

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   BSIhos <-      |
    avgcumdosew3 |    .0247118    .0844695      0.29    0.770     -.1408453     .1902689
           _cons |     7.45515    .1795906     41.51    0.000      7.103159     7.807141
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          BSIhos |    3.063457    .5507991      5.56    0.000      1.983911     4.143004
           _cons |    3.315143    4.406108      0.75    0.452     -5.320671     11.95096
-----------------+----------------------------------------------------------------
 Variance         |
        e.BSIhos |      7.9669    .5913588                    6.888224     9.214494
      e.whpsleep |    877.5761    65.13981                     758.757     1015.002
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      7.51, Prob > chi2 = 0.0061

. sem(avgcumdosew3->radchw3)(radchw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  radchw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -4279.7422
Iteration 1:   log likelihood = -4279.7422

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4279.7422

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   radchw3 <-     |
    avgcumdosew3 |    1.497864    1.033363      1.45    0.147     -.5274916     3.523219
           _cons |     59.6792    2.197035     27.16    0.000      55.37309     63.98531
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         radchw3 |    .0035358    .0467727      0.08    0.940     -.0881371     .0952087
           _cons |    26.02728     3.30036      7.89    0.000      19.55869     32.49586
-----------------+----------------------------------------------------------------
 Variance         |
       e.radchw3 |    1192.329    88.50296                    1030.894     1379.044
      e.whpsleep |    952.3463    70.68977                    823.4036     1101.481
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      7.37, Prob > chi2 = 0.0066

. sem(avgcumdosew3->fdferw3)(fdferw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  fdferw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -4123.7411
Iteration 1:   log likelihood = -4123.7411

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4123.7411

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   fdferw3 <-     |
    avgcumdosew3 |    1.370612    .6738871      2.03    0.042      .0498176     2.691406
           _cons |    9.418911    1.432752      6.57    0.000      6.610768     12.22705
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         fdferw3 |    .0913384    .0713637      1.28    0.201     -.0485319     .2312086
           _cons |     25.2339    1.798737     14.03    0.000      21.70844     28.75935
-----------------+----------------------------------------------------------------
 Variance         |
       e.fdferw3 |    507.0657    37.63794                    438.4117     586.4707
      e.whpsleep |    948.0828     70.3733                    819.7174      1096.55
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      6.74, Prob > chi2 = 0.0094

. sem(avgcumdosew3->sepaw3)(sepaw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  sepaw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -2447.7215
Iteration 1:   log likelihood = -2447.7215

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -2447.7215

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   sepaw3 <-      |
    avgcumdosew3 |    .0051544    .0066596      0.77    0.439     -.0078982     .0182069
           _cons |    .0461471    .0141589      3.26    0.001      .0183961     .0738981
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          sepaw3 |    9.544387    7.255468      1.32    0.188     -4.676069     23.76484
           _cons |    25.74509    1.659927     15.51    0.000      22.49169     28.99848
-----------------+----------------------------------------------------------------
 Variance         |
        e.sepaw3 |    .0495202    .0036757                    .0428155      .057275
      e.whpsleep |    947.8428    70.35549                    819.5099     1096.272
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      7.11, Prob > chi2 = 0.0077

. sem(avgcumdosew3->PTSDw3)(PTSDw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  PTSDw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -3759.5671
Iteration 1:   log likelihood = -3759.5671

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3759.5671

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   PTSDw3 <-      |
    avgcumdosew3 |    .1887747    .2539819      0.74    0.457     -.3090206       .68657
           _cons |    4.545116    .5399912      8.42    0.000      3.486752     5.603479
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          PTSDw3 |    .8723618    .1851303      4.71    0.000      .5095132      1.23521
           _cons |    22.08176    1.803557     12.24    0.000      18.54685     25.61666
-----------------+----------------------------------------------------------------
 Variance         |
        e.PTSDw3 |    72.02699    5.346345                    62.27492     83.30621
      e.whpsleep |    897.4642    66.61604                    775.9523     1038.005
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      6.81, Prob > chi2 = 0.0090

. sem(avgcumdosew3->depagw3)(depagw3-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  depagw3 whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -4092.8599
Iteration 1:   log likelihood = -4092.8599

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -4092.8599

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   depagw3 <-     |
    avgcumdosew3 |    .1789491    .6298562      0.28    0.776     -1.055546     1.413445
           _cons |    13.09433    1.339138      9.78    0.000      10.46966     15.71899
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
         depagw3 |    .2885463    .0754457      3.82    0.000      .1406754     .4364172
           _cons |    22.40428    1.878896     11.92    0.000      18.72171     26.08685
-----------------+----------------------------------------------------------------
 Variance         |
       e.depagw3 |    442.9684     32.8802                    382.9928      512.336
      e.whpsleep |     915.472     67.9527                    791.5219     1058.832
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      7.34, Prob > chi2 = 0.0067

. sem(avgcumdosew3->BSIdep)(BSIdep-> whpsleep) if gender==2, nocapslatent

Endogenous variables


Observed:  BSIdep whpsleep


Exogenous variables


Observed:  avgcumdosew3


Fitting target model:

Iteration 0:   log likelihood = -3433.7019
Iteration 1:   log likelihood = -3433.7019

Structural equation model                       Number of obs      =       363
 Estimation method = ml
 Log likelihood    = -3433.7019

-----------------+----------------------------------------------------------------
                 |                 OIM
                 |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-----------------+----------------------------------------------------------------
 Structural       |
   BSIdep <-      |
    avgcumdosew3 |    .3811686    .1106861      3.44    0.001      .1642277     .5981094
           _cons |    9.197558    .2353299     39.08    0.000       8.73632     9.658796
  ---------------+----------------------------------------------------------------
   whpsleep <-    |
          BSIdep |    3.445067    .3911827      8.81    0.000      2.678363     4.211771
           _cons |   -7.019698    4.053191     -1.73    0.083     -14.96381     .9244117
-----------------+----------------------------------------------------------------
 Variance         |
        e.BSIdep |    13.67969    1.015402                    11.82754     15.82189
      e.whpsleep |    784.6999    58.24589                    678.4557     907.5816
-----------------+----------------------------------------------------------------
LR test of model vs. saturated: chi2(1)   =      2.05, Prob > chi2 = 0.1523

.
.
.
. *** Female sleep regression summary begins here
. * this section requires estout.add by Ben Jann at ETH
. est clear

. regress WHPsleep age avgcumdosew1 inc1w1-inc4w1 ///
>     marrw11-marrw16 bffsl4 bffsl10 bffsl11  BSIhos ///
>      if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 16,   362) =   15.56
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.3662
                                                       Root MSE      =  25.165

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .8578777     .166374      5.16    0.000      .5306968     1.185059
avgcumdosew1 |    6.398469    2.576871      2.48    0.013      1.330951     11.46599
      inc1w1 |   -4.983327    4.876462     -1.02    0.308     -14.57308     4.606425
      inc2w1 |   -9.770151    3.958723     -2.47    0.014     -17.55513    -1.985168
      inc3w1 |   -4.092424    4.778323     -0.86    0.392     -13.48918     5.304334
      inc4w1 |   -9.313265    6.127037     -1.52    0.129     -21.36232     2.735791
     marrw11 |      6.7235    6.517339      1.03    0.303     -6.093101      19.5401
     marrw12 |    33.90256    21.39437      1.58    0.114     -8.170298     75.97541
     marrw13 |    2.362157    7.541906      0.31    0.754     -12.46929     17.19361
     marrw14 |    57.13913    13.75949      4.15    0.000      30.08057      84.1977
     marrw15 |    2.248905    15.63139      0.14    0.886     -28.49083     32.98864
     marrw16 |     13.8564    19.02823      0.73    0.467     -23.56335     51.27614
      bffsl4 |    .3391336     .078603      4.31    0.000      .1845578     .4937094
     bffsl10 |    .0038976      .00166      2.35    0.019      .0006331     .0071621
     bffsl11 |   -.0026421    .0010676     -2.47    0.014     -.0047415    -.0005427
      BSIhos |    1.514132    .6783504      2.23    0.026      .1801291     2.848134
       _cons |   -38.92315    8.959197     -4.34    0.000     -56.54175    -21.30454
-------------+----------------------------------------------------------------

. eststo wave1

.
. regress WHPsleep age  avgcumdosew2 ///
>     radhlw2 radchw2 educ2-educ7 occ1w2-occ8w2 inc1w2-inc4w2 BSIhos ///
>     bffsl10 if gender==2, vce(cluster id)

Linear regression                                      Number of obs =     363
                                                       F( 23,   362) =       .
                                                       Prob > F      =       .
                                                       R-squared     =  0.3409
                                                       Root MSE      =  25.964

(Std. Err. adjusted for 363 clusters in id)
-------------+----------------------------------------------------------------
             |               Robust
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .8914268    .1347691      6.61    0.000      .6263981     1.156456
avgcumdosew2 |    2.154052    .8922784      2.41    0.016      .3993514     3.908752
     radhlw2 |    .1208159    .0476982      2.53    0.012      .0270155     .2146163
     radchw2 |   -.1292914    .0426107     -3.03    0.003      -.213087    -.0454957
       educ2 |    14.10377    6.455016      2.18    0.030       1.40973     26.79781
       educ3 |    15.52526    5.360612      2.90    0.004      4.983411     26.06711
       educ4 |      11.949    8.465017      1.41    0.159     -4.697786     28.59578
       educ5 |    6.170848    6.371697      0.97    0.333     -6.359342     18.70104
       educ6 |    8.879311    5.239619      1.69    0.091     -1.424604     19.18323
       educ7 |    -17.6245    14.13895     -1.25    0.213      -45.4293     10.18031
      occ1w2 |    9.025681    7.230648      1.25    0.213     -5.193669     23.24503
      occ2w2 |    1.533179    7.756436      0.20    0.843     -13.72015     16.78651
      occ3w2 |    14.65375    7.613966      1.92    0.055     -.3194108     29.62691
      occ4w2 |    6.126081    9.117084      0.67    0.502     -11.80302     24.05518
      occ5w2 |    3.567388    11.33283      0.31    0.753     -18.71906     25.85384
      occ6w2 |    19.59527    10.31017      1.90    0.058     -.6800677     39.87062
      occ7w2 |    11.74104     7.67155      1.53    0.127     -3.345358     26.82744
      occ8w2 |     18.1066    8.189295      2.21    0.028      2.002032     34.21116
      inc1w2 |   -11.36438    7.459033     -1.52    0.128     -26.03285     3.304102
      inc2w2 |   -13.44399     7.17852     -1.87    0.062     -27.56083     .6728512
      inc3w2 |   -15.91007    7.475102     -2.13    0.034     -30.61015    -1.209987
      inc4w2 |   -20.64762    12.80378     -1.61    0.108     -45.82674     4.531506
      BSIhos |    2.888212    .5357928      5.39    0.000      1.834555      3.94187
     bffsl10 |    .0048435    .0017371      2.79    0.006      .0014273     .0082596
       _cons |   -50.55326    10.28341     -4.92    0.000     -70.77598    -30.33054
-------------+----------------------------------------------------------------

. eststo wave2

.
.
. regress WHPsleep age avgcumdosew3  marrw31-marrw33 marrw35  ///
>   radhlw3 radchw3 ///
>    bffsl10  BSIhos if gender==2

      Source |       SS       df       MS              Number of obs =     363
-------------+------------------------------           F( 10,   352) =   15.50
       Model |  105687.391    10  10568.7391           Prob > F      =  0.0000
    Residual |  240019.749   352  681.874288           R-squared     =  0.3057
-------------+------------------------------           Adj R-squared =  0.2860
       Total |  345707.141   362  954.992102           Root MSE      =  26.113

-------------+----------------------------------------------------------------
     WHPsleep |      Coef.    Std. Err.       t    P>|t|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
         age |    .7619818    .1324233      5.75    0.000      .5015415     1.022422
avgcumdosew3 |    1.514995    .7990515      1.90    0.059     -.0565207      3.08651
     marrw31 |   -14.10159    6.453492     -2.19    0.030     -26.79385    -1.409342
     marrw32 |    5.353814     10.2582      0.52    0.602     -14.82125     25.52888
     marrw33 |   -3.796629    4.139713     -0.92    0.360     -11.93831     4.345053
     marrw35 |    3.629092    6.306794      0.58    0.565     -8.774645     16.03283
     radhlw3 |    .1557481    .0460893      3.38    0.001      .0651031      .246393
     radchw3 |   -.0993872    .0437691     -2.27    0.024     -.1854691    -.0133053
     bffsl10 |    .0047105    .0011763      4.00    0.000       .002397     .0070241
      BSIhos |    2.980555     .499805      5.96    0.000      1.997575     3.963534
       _cons |   -37.80625    9.195954     -4.11    0.000     -55.89217    -19.72033
-------------+----------------------------------------------------------------

. eststo wave3

.  estout  *, cells(b(star fmt(%9.3f)) se(par)  p)                ///
>             stats(r2_a bic N, fmt(%9.3f %9.0g) labels(adjR^2 BIC))  ///
>             legend collabels(none) varlabels(_cons Constant)  ///
>              title("Trimmed Regression Models" ///
>             "of reconstructed dose on female sleep")

Trimmed Regression Models of reconstructed dose on female sleep
------------------------------------------------------------
                    wave1           wave2           wave3
------------------------------------------------------------
age                 0.858***        0.891***        0.762***
                  (0.166)         (0.135)         (0.132)
                    0.000           0.000           0.000
avgcumdosew1        6.398*
                  (2.577)
                    0.013
inc1w1             -4.983
                  (4.876)
                    0.308
inc2w1             -9.770*
                  (3.959)
                    0.014
inc3w1             -4.092
                  (4.778)
                    0.392
inc4w1             -9.313
                  (6.127)
                    0.129
marrw11             6.723
                  (6.517)
                    0.303
marrw12            33.903
                 (21.394)
                    0.114
marrw13             2.362
                  (7.542)
                    0.754
marrw14            57.139***
                 (13.759)
                    0.000
marrw15             2.249
                 (15.631)
                    0.886
marrw16            13.856
                 (19.028)
                    0.467
bffsl4              0.339***
                  (0.079)
                    0.000
bffsl10             0.004*          0.005**         0.005***
                  (0.002)         (0.002)         (0.001)
                    0.019           0.006           0.000
bffsl11            -0.003*
                  (0.001)
                    0.014
BSIhos              1.514*          2.888***        2.981***
                  (0.678)         (0.536)         (0.500)
                    0.026           0.000           0.000
avgcumdosew2                        2.154*
                                  (0.892)
                                    0.016
radhlw2                             0.121*
                                  (0.048)
                                    0.012
radchw2                            -0.129**
                                  (0.043)
                                    0.003
educ2                              14.104*
                                  (6.455)
                                    0.030
educ3                              15.525**
                                  (5.361)
                                    0.004
educ4                              11.949
                                  (8.465)
                                    0.159
educ5                               6.171
                                  (6.372)
                                    0.333
educ6                               8.879
                                  (5.240)
                                    0.091
educ7                             -17.624
                                 (14.139)
                                    0.213
occ1w2                              9.026
                                  (7.231)
                                    0.213
occ2w2                              1.533
                                  (7.756)
                                    0.843
occ3w2                             14.654
                                  (7.614)
                                    0.055
occ4w2                              6.126
                                  (9.117)
                                    0.502
occ5w2                              3.567
                                 (11.333)
                                    0.753
occ6w2                             19.595
                                 (10.310)
                                    0.058
occ7w2                             11.741
                                  (7.672)
                                    0.127
occ8w2                             18.107*
                                  (8.189)
                                    0.028
inc1w2                            -11.364
                                  (7.459)
                                    0.128
inc2w2                            -13.444
                                  (7.179)
                                    0.062
inc3w2                            -15.910*
                                  (7.475)
                                    0.034
inc4w2                            -20.648
                                 (12.804)
                                    0.108
avgcumdosew3                                        1.515
                                                  (0.799)
                                                    0.059
marrw31                                           -14.102*
                                                  (6.453)
                                                    0.030
marrw32                                             5.354
                                                 (10.258)
                                                    0.602
marrw33                                            -3.797
                                                  (4.140)
                                                    0.360
marrw35                                             3.629
                                                  (6.307)
                                                    0.565
radhlw3                                             0.156***
                                                  (0.046)
                                                    0.001
radchw3                                            -0.099*
                                                  (0.044)
                                                    0.024
Constant          -38.923***      -50.553***      -37.806***
                  (8.959)        (10.283)         (9.196)
                    0.000           0.000           0.000
------------------------------------------------------------
adjR^2              0.337           0.294           0.286
BIC              3454.624        3510.085        3452.337
N                     363             363             363
------------------------------------------------------------
* p<0.05, ** p<0.01, *** p<0.001

.
. *------------------------------------------------------------------------------
.
.
. save chwide27june2012, replace
file chwide27june2012.dta saved