. est clear

. title "Zero order tests"


*******************************************************************************
*******************************************************************************
*****                                                                     *****
*****                                                                     *****
*****                          Zero order tests                           *****
*****                                                                     *****
*****                                                                     *****
*****                                             1 Jul 2012    13:58:04  *****
*******************************************************************************
*******************************************************************************


. set more off

. foreach var in HP2work HP2hmcare HP2probsoc HP2pbfhm HP2sxlife HP2inthob ///
>       HP2vacatn  {
  2.       forvalues k=2/2 {
  3.   logit `var' avgcumdosew2 if gender==`k', nolog
  4.   eststo w2`var'dose
  5.   }
  6.  }

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       6.63
                                                  Prob > chi2     =     0.0100
Log likelihood = -203.24758                       Pseudo R2       =     0.0160

-------------+----------------------------------------------------------------
      HP2work |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew2 |    .2046594    .0826501      2.48    0.013      .0426681     .3666507
       _cons |   -1.260388    .1448325     -8.70    0.000     -1.544255    -.9765218
-------------+----------------------------------------------------------------

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       0.01
                                                  Prob > chi2     =     0.9082
Log likelihood = -233.72194                       Pseudo R2       =     0.0000

-------------+----------------------------------------------------------------
    HP2hmcare |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew2 |   -.0093108    .0810645     -0.11    0.909     -.1681944     .1495727
       _cons |   -.6356448      .13193     -4.82    0.000     -.8942228    -.3770668
-------------+----------------------------------------------------------------

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =      25.69
                                                  Prob > chi2     =     0.0000
Log likelihood = -170.72516                       Pseudo R2       =     0.0700

-------------+----------------------------------------------------------------
   HP2probsoc |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew2 |    .5024747    .1391198      3.61    0.000       .229805     .7751444
       _cons |   -1.841993    .1807682    -10.19    0.000     -2.196292    -1.487694
-------------+----------------------------------------------------------------

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       1.97
                                                  Prob > chi2     =     0.1602
Log likelihood = -138.91068                       Pseudo R2       =     0.0070

-------------+----------------------------------------------------------------
     HP2pbfhm |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew2 |    .1290516    .0853896      1.51    0.131     -.0383089     .2964121
       _cons |    -2.03386    .1824844    -11.15    0.000     -2.391523    -1.676197
-------------+----------------------------------------------------------------

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =      13.99
                                                  Prob > chi2     =     0.0002
Log likelihood = -200.62862                       Pseudo R2       =     0.0337

-------------+----------------------------------------------------------------
    HP2sxlife |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew2 |    .3220323    .1022608      3.15    0.002      .1216048     .5224598
       _cons |   -1.353229    .1520692     -8.90    0.000      -1.65128    -1.055179
-------------+----------------------------------------------------------------

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       5.33
                                                  Prob > chi2     =     0.0210
Log likelihood = -169.44834                       Pseudo R2       =     0.0155

-------------+----------------------------------------------------------------
    HP2inthob |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew2 |    .1881347    .0796521      2.36    0.018      .0320195       .34425
       _cons |   -1.691176    .1610367    -10.50    0.000     -2.006802     -1.37555
-------------+----------------------------------------------------------------

Logistic regression                               Number of obs   =        363
                                                  LR chi2(1)      =       4.26
                                                  Prob > chi2     =     0.0389
Log likelihood = -165.38435                       Pseudo R2       =     0.0127

-------------+----------------------------------------------------------------
    HP2vacatn |      Coef.    Std. Err.       z    P>|z|      [95% Con f. Interval]
-------------+----------------------------------------------------------------
avgcumdosew2 |    .1706012    .0795148      2.15    0.032      .0147551     .3264474
       _cons |   -1.730143    .1632828    -10.60    0.000     -2.050172    -1.410115
-------------+----------------------------------------------------------------

.
.
. *------- estout code snippt
.
. estout  *,      cells(b(star fmt(%9.3f)) se(par) t p)                ///
>                 stats( bic N, fmt(%9.3f %9.0g) )        ///
>                 legend collabels(none) varlabels(_cons Constant)  ///
>                 mlabels(workw2  homecarew2 socprobsw2 pbfhmw2 sexlifew2 inthobw2 vactnw2) ///
>                                 title("Zero-order regression coefficients" ///
>                  "of reconstructed dose for females in wave 2")

Zero-order regression coefficients of reconstructed dose for females in wave 2
----------------------------------------------------------------------------------------------------------------------------
                   workw2      homecarew2      socprobsw2         pbfhmw2       sexlifew2        inthobw2         vactnw2
----------------------------------------------------------------------------------------------------------------------------
main
avgcumdosew2        0.205*         -0.009           0.502***        0.129           0.322**         0.188*          0.171*
                  (0.083)         (0.081)         (0.139)         (0.085)         (0.102)         (0.080)         (0.080)
                    2.476          -0.115           3.612           1.511           3.149           2.362           2.146
                    0.013           0.909           0.000           0.131           0.002           0.018           0.032
Constant           -1.260***       -0.636***       -1.842***       -2.034***       -1.353***       -1.691***       -1.730***
                  (0.145)         (0.132)         (0.181)         (0.182)         (0.152)         (0.161)         (0.163)
                   -8.702          -4.818         -10.190         -11.145          -8.899         -10.502         -10.596
                    0.000           0.000           0.000           0.000           0.000           0.000           0.000
----------------------------------------------------------------------------------------------------------------------------
bic               418.284         479.233         353.239         289.610         413.046         350.685         342.558
N                     363             363             363             363             363             363             363
----------------------------------------------------------------------------------------------------------------------------
* p<0.05, ** p<0.01, *** p<0.001

.