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  • #1

    how to compare 2 groups in a fixed-effects model

    Dear all,

    I'm Using 14.0 and Panel Data Wave 3-8.

    I’d like to test the following Hypotheses (I'm sorry it's phrased better in German) :

    H1a: Parents who change their relationship status have a lower Parent-Child-Relationshipquality than parents who are in a stable relationship
    H1a2: Mothers who change their relationship status have a lower P-C-R than Fathers with a changing relationship status
    H0: Parents who change their relationship status don't have a lower P-C-R than parents who are in a stable relationship
    H1b: The change of the parent's relationship status has a negativ effect on the P-C-R.

    Therefore the Housman-Test showed to use FE.

    How can I show if there is a difference between the control groups (test h1a)? Like you can see in the dataex the variable which shows the change of the relationship status 'transition' is a dummy 0= no change and 1 change. If I am right stata only counts if there is a change over time. That means that I can't say anything about parents in a stable relationship. Maybe I have made a mistake with my thinking..

    To test h1b the model looks like this:
    Code:
    xtreg bezqual_kind transition depressive selfesteem fsit_a inc28 move job7 warmth_pacs monitor_pacs negcomm_pacs inconsist_pacs cwarmth_cao cmonitor_cao age cagey nkidsliv, fe
    Code:
    Fixed-effects (within) regression               Number of obs     =      4,117
Group variable: id                              Number of groups  =      1,245

R-sq:                                           Obs per group:
     within  = 0.3046                                         min =          1
     between = 0.4853                                         avg =        3.3
     overall = 0.4369                                         max =          6

                                                F(16,2856)        =      78.19
corr(u_i, Xb)  = 0.1317                         Prob > F          =     0.0000

--------------------------------------------------------------------------------
  bezqual_kind |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    transition |   .0294374   .0310101     0.95   0.343    -.0313671    .0902419
    depressive |  -.0044196   .0230625    -0.19   0.848    -.0496405    .0408013
    selfesteem |   .0075242   .0123574     0.61   0.543    -.0167062    .0317546
        fsit_a |   .0204509   .0106924     1.91   0.056    -.0005146    .0414164
         inc28 |   .0070363   .0045094     1.56   0.119    -.0018058    .0158784
          move |   .0100518    .016546     0.61   0.544    -.0223914     .042495
          job7 |   .0001806    .000748     0.24   0.809    -.0012862    .0016473
   warmth_pacs |   .0323705   .0202993     1.59   0.111    -.0074322    .0721733
  monitor_pacs |   .0603774   .0192297     3.14   0.002     .0226718     .098083
  negcomm_pacs |  -.0551816   .0175485    -3.14   0.002    -.0895907   -.0207725
inconsist_pacs |  -.0291047   .0175303    -1.66   0.097     -.063478    .0052685
   cwarmth_cao |   .3980474   .0158171    25.17   0.000     .3670333    .4290614
  cmonitor_cao |   .0960683   .0102511     9.37   0.000      .075968    .1161686
           age |  -.0240684   .0246416    -0.98   0.329    -.0723856    .0242488
         cagey |  -.0143854   .0244586    -0.59   0.556    -.0623437    .0335729
      nkidsliv |  -.0074075   .0258401    -0.29   0.774    -.0580746    .0432596
         _cons |   2.477777   .7244326     3.42   0.001     1.057314    3.898241
---------------+----------------------------------------------------------------
       sigma_u |  .35771454
       sigma_e |  .33527174
           rho |  .53235169   (fraction of variance due to u_i)
--------------------------------------------------------------------------------
F test that all u_i=0: F(1244, 2856) = 2.79                  Prob > F = 0.0000

    To test h1a2 it looks like this:

    Code:
    xtreg bezqual_kind transition depressive selfesteem fsit_a inc28 move job7 warmth_pacs monitor_pacs negcomm_pacs inconsist_pacs cwarmth_cao cmonitor_cao age cagey nkidsliv if sex_gen==1, fe  // Fathers
    Code:
    Fixed-effects (within) regression               Number of obs     =      1,229
Group variable: id                              Number of groups  =        379

R-sq:                                           Obs per group:
     within  = 0.3412                                         min =          1
     between = 0.4695                                         avg =        3.2
     overall = 0.4500                                         max =          6

                                                F(16,834)         =      26.99
corr(u_i, Xb)  = 0.1753                         Prob > F          =     0.0000

--------------------------------------------------------------------------------
  bezqual_kind |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    transition |   .0628663   .0812616     0.77   0.439     -.096635    .2223676
    depressive |   .0190544   .0483386     0.39   0.694    -.0758253    .1139341
    selfesteem |  -.0288062   .0224402    -1.28   0.200    -.0728522    .0152397
        fsit_a |   .0302963   .0198431     1.53   0.127     -.008652    .0692445
         inc28 |   .0079084   .0085265     0.93   0.354    -.0088276    .0246444
          move |  -.0260721   .0299562    -0.87   0.384    -.0848705    .0327263
          job7 |   .0016187   .0013151     1.23   0.219    -.0009627       .0042
   warmth_pacs |   .0150044   .0317076     0.47   0.636    -.0472316    .0772405
  monitor_pacs |   .0787379   .0315432     2.50   0.013     .0168245    .1406513
  negcomm_pacs |  -.0538126   .0322375    -1.67   0.095    -.1170888    .0094636
inconsist_pacs |   .0033006   .0298674     0.11   0.912    -.0553236    .0619248
   cwarmth_cao |    .360789   .0258298    13.97   0.000     .3100899    .4114881
  cmonitor_cao |   .0986978   .0155511     6.35   0.000     .0681738    .1292218
           age |  -.0185679   .0442744    -0.42   0.675    -.1054701    .0683344
         cagey |  -.0355808   .0429529    -0.83   0.408    -.1198892    .0487277
      nkidsliv |  -.0188827   .0535458    -0.35   0.724    -.1239831    .0862176
         _cons |   2.651687   1.357058     1.95   0.051    -.0119634    5.315338
---------------+----------------------------------------------------------------
       sigma_u |  .37657764
       sigma_e |  .32255129
           rho |  .57681792   (fraction of variance due to u_i)
--------------------------------------------------------------------------------
F test that all u_i=0: F(378, 834) = 3.17                    Prob > F = 0.0000

    Code:
    xtreg bezqual_kind transition depressive selfesteem fsit_a inc28 move job7 warmth_pacs monitor_pacs negcomm_pacs inconsist_pacs cwarmth_cao cmonitor_cao age cagey nkidsliv if sex_gen==2, fe  // Mothers
    Code:
    Fixed-effects (within) regression               Number of obs     =      2,888
Group variable: id                              Number of groups  =        866

R-sq:                                           Obs per group:
     within  = 0.2952                                         min =          1
     between = 0.4800                                         avg =        3.3
     overall = 0.4248                                         max =          6

                                                F(16,2006)        =      52.51
corr(u_i, Xb)  = 0.0933                         Prob > F          =     0.0000

--------------------------------------------------------------------------------
  bezqual_kind |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    transition |   .0232803   .0339835     0.69   0.493    -.0433664    .0899271
    depressive |  -.0061684    .026529    -0.23   0.816    -.0581957    .0458589
    selfesteem |   .0223192   .0148798     1.50   0.134    -.0068623    .0515007
        fsit_a |    .014227   .0128077     1.11   0.267    -.0108907    .0393448
         inc28 |   .0066106   .0053558     1.23   0.217     -.003893    .0171141
          move |   .0250636   .0198881     1.26   0.208      -.01394    .0640671
          job7 |  -.0002981   .0009132    -0.33   0.744     -.002089    .0014927
   warmth_pacs |   .0403631   .0262955     1.53   0.125    -.0112062    .0919324
  monitor_pacs |   .0538022   .0242272     2.22   0.026      .006289    .1013154
  negcomm_pacs |  -.0557938   .0209878    -2.66   0.008     -.096954   -.0146336
inconsist_pacs |  -.0452463   .0216741    -2.09   0.037    -.0877524   -.0027401
   cwarmth_cao |   .4170839   .0199913    20.86   0.000      .377878    .4562898
  cmonitor_cao |   .0959373   .0135344     7.09   0.000     .0693944    .1224802
           age |   -.029885   .0298448    -1.00   0.317    -.0884151    .0286451
         cagey |  -.0019201     .02986    -0.06   0.949    -.0604799    .0566397
      nkidsliv |  -.0005025   .0298102    -0.02   0.987    -.0589647    .0579597
         _cons |   2.450835   .8617133     2.84   0.004     .7608886    4.140782
---------------+----------------------------------------------------------------
       sigma_u |  .35222723
       sigma_e |  .34047672
           rho |  .51695837   (fraction of variance due to u_i)
--------------------------------------------------------------------------------
F test that all u_i=0: F(865, 2006) = 2.61                   Prob > F = 0.0000

    Thank you in advance!

    Guest

    Code:
    * Example generated by -dataex-. To install: ssc install dataex
clear
input long(id cid) byte(wave sex_gen age cagey nkidsliv) double job7 byte inc28 float(transition bezqual_kind depressive selfesteem cwarmth_cao cmonitor_cao warmth_pacs monitor_pacs negcomm_pacs inconsist_pacs fsit_a move)
  111000   111203 3 2 39  9 2    0  3 0  3.833333   1         5 4.6666665   4 4.6666665 3.75 1.6666666  2.5   5 1
  111000   111203 4 2 40 10 2    0  3 1       4.5 2.2 4.6666665         5 4.5 4.6666665 4.25  3.666667  3.5   4 1
  111000   111203 5 2 41 11 2    0  0 0         3 2.6         3  3.333333 2.5         4 3.25         2  3.5 4.5 0
  111000   111203 6 2 42 12 1    3  5 1  3.166667 1.1 4.6666665 4.3333335 2.5 4.6666665  3.5 2.3333333  2.5 4.5 0
  111000   111203 7 2 43 13 1   40  5 1  3.833333 1.5         5 4.6666665 3.5         4 3.75 1.6666666 2.75   5 0
  111000   111203 8 2 44 14 1    0  5 0         4 1.4 4.6666665         5   3         4  3.5 1.6666666 2.75   5 0
 1300000  1300202 3 1 37 13 1   40  8 0         4 1.4 4.6666665         5   5 4.6666665 4.75 2.3333333  1.5   1 1
 1300000  1300202 4 1 38 14 1   50  6 0 4.3333335 2.3         4 4.6666665   4 4.6666665 4.75         2    2   2 0
 3902000  3902201 5 1 42  9 3   42  3 0 4.3333335 1.4 4.3333335 4.3333335   4 4.6666665  4.5         2 2.75 3.5 1
 3902000  3902201 6 1 42  9 3   38  4 0         4 1.4 4.3333335         5 3.5 4.6666665  4.5  2.666667  2.5   3 0
 3902000  3902201 7 1 43 10 3   43  3 0  3.833333 1.2         4 4.3333335 4.5 4.6666665 4.25 2.3333333 2.25   4 0
 3902000  3902201 8 1 44 11 3   38  3 0  3.833333 1.5 4.3333335         5   4 4.6666665 4.25 2.3333333  2.5   4 0
 4835000  4835201 5 2 41 13 1   40  5 0         3   2 4.6666665  3.666667 1.5         4  3.5 2.3333333 3.25   3 1
 4858000  4858201 5 2 29  9 1   25  6 0 4.3333335   1         5         5   5         5 4.25         1 1.25   3 1
 4858000  4858201 6 2 30 11 1   30  9 1  3.666667 1.5         5         4   5         5 4.25  3.333333  2.5   4 0
 6151000  6151201 5 2 40  8 2   25  8 0 4.1666665 1.2 4.3333335 4.6666665   4 4.3333335    5 2.3333333    2   1 1
 6151000  6151201 6 2 41  9 2   20  9 0 4.1666665 1.2 4.3333335 4.3333335   5 4.6666665 4.75 2.3333333    2   1 0
 6151000  6151201 7 2 42 10 2   30  9 0  3.833333 1.3 4.3333335  3.333333   5         5 3.75  3.333333 1.75   1 0
 6151000  6151201 8 2 43 11 2   25  8 0 4.1666665 1.1 4.3333335 4.6666665 4.5         5 4.75  2.666667 1.75 1.5 0
 6519000  6519201 4 2 41 10 1   40  5 0 4.3333335   2  2.666667         5   3         4  3.5  2.666667 3.25   4 1
 6519000  6519201 5 2 41 11 1   35  5 1       3.5 1.9         3         5   4         4 3.25  3.333333    3   3 0
 6519000  6519201 6 2 43 12 1   42  5 0       3.5 2.1  3.666667 4.3333335   4         4 3.75         3  3.5   3 0
 6519000  6519201 7 2 44 13 1   40  7 0         3 2.1         3 4.6666665   3         4    4 2.3333333  3.5   2 0
 6519000  6519201 8 2 45 14 1   40  7 0  2.833333 2.5  3.333333 4.6666665 3.5         4 3.75         3    4 1.5 0
 8948000  8948201 3 2 38  9 1 48.5  5 0       2.5 1.4         3 4.3333335   3 4.3333335  4.5         2 2.25   4 1
 8948000  8948201 4 2 39 10 1   50  5 0  3.166667 1.8         3 4.6666665   3 4.6666665    4         2 2.25 3.5 0
 8948000  8948201 5 2 40 11 1   50  6 0  3.166667 1.8  3.666667 4.3333335 3.5 4.6666665 3.75         2  2.5 3.5 0
 8948000  8948201 6 2 41 12 1   50  6 0         3 1.6         3  3.333333   3 4.3333335  3.5         2  2.5 3.5 0
 8948000  8948201 7 2 42 13 1   50  6 0  2.833333 1.6  3.666667         4 3.5         4    3         2 2.25   4 0
 8948000  8948201 8 2 43 14 1   50  5 0 2.1666667 1.6 4.3333335  3.666667   3         4 3.25         2 1.75 3.5 0
 9657000  9657201 5 2 41  8 3    0  9 0 4.1666665 1.8  3.666667         4   4 4.3333335    5         4  3.5 1.5 1
 9657000  9657201 8 2 44 11 3    9 10 0       3.5 1.9         4 4.3333335 3.5 4.3333335 4.75         3  3.5   1 0
 9917000  9917201 5 1 39  8 2   45  9 0 4.6666665 1.1 4.6666665         5 4.5         4    4         3  2.5   2 1
 9917000  9917201 6 1 40  9 2   45  9 0         4 1.3 4.3333335 4.6666665 4.5         4    4  2.666667 2.25   2 0
 9917000  9917201 8 1 42 11 2   50 10 0  4.833333 1.2 4.3333335         5   5 4.3333335 4.25  3.333333 2.25   2 0
10208000 10208201 3 1 38  8 2   40  6 0       4.5 1.7  2.666667 4.3333335   5         4    4 2.3333333 2.25 2.5 1
10208000 10208201 4 1 39  9 2   40  6 0         4 1.6 4.3333335 4.6666665 4.5         4  3.5         2  2.5   3 0
10208000 10208201 5 1 40  9 2   40  7 0  3.833333 1.6 4.3333335 4.6666665   5         4    4 2.3333333    2   2 0
10208000 10208201 6 1 41 11 2   40  7 0  3.833333 1.7         4         4 3.5         4 3.75 2.3333333 2.25 2.5 0
10208000 10208201 7 1 42 12 2   40  7 0  3.333333 1.7 4.3333335         4 3.5         4 3.75  2.666667  2.5   1 0
10208000 10208201 8 1 43 13 2   40  7 0  3.666667 1.6         4         4   4         4 4.25  2.666667 2.25   2 0
10957000 10957202 3 2 39 13 2   40  1 0  2.666667 1.1         5  3.333333   2         4 3.25         2 2.75 3.5 1
10957000 10957202 4 2 40 14 2   40  0 0       2.5 1.1 4.3333335  3.666667 2.5         4 3.75 1.6666666    4 3.5 0
10957000 10957202 5 2 41 15 2   41  3 0  3.166667 1.3         5 4.6666665   2 4.3333335  3.5 1.6666666  3.5 3.5 0
11295000 11295201 4 2 38  8 2   21  7 0       3.5 2.7         4 4.6666665 4.5 4.6666665 4.75         2 2.75 3.5 1
11295000 11295201 5 2 39  9 2   30  7 0  3.333333 2.1 4.6666665         4 4.5         5  4.5 2.3333333 2.75   4 1
11295000 11295201 6 2 40 10 2   42  4 0  3.833333 2.7         3 4.3333335 3.5 4.6666665 4.75         2 2.75   4 0
11295000 11295201 7 2 41 11 2   40  8 0  3.666667 2.4         4         4   4 4.6666665 4.25 1.6666666 2.25   3 0
11295000 11295201 8 2 42 12 2   30  7 0 4.3333335 2.1 4.3333335 4.6666665   5         4 4.75         2 2.75   3 0
12266000 12266201 3 1 37 12 1   50  6 0  3.166667   2         3  3.666667   5  3.333333 4.25         3 3.25   4 1
12266000 12266201 6 1 40 14 1   48  3 0  3.333333 2.3  3.333333         4 4.5 4.3333335    4  2.666667 4.75   5 0
12471000 12471201 6 1 31  9 2   34  9 0       2.5 1.8         4 4.3333335 4.5  3.666667 3.75         3    3   3 1
12490000 12490201 3 2 38  8 3    0  6 0 4.1666665 1.7         4 4.3333335 4.5  3.333333    5  3.333333 2.75   3 1
12490000 12490201 4 2 39  9 3    0  7 0 4.3333335 1.8         5         5   5         3 4.75         4  3.5   2 0
12490000 12490201 5 2 40 10 3   27  8 0         4 1.9 4.3333335 4.6666665   3         3  4.5         4  3.5   3 0
12490000 12490201 6 2 41 11 3   28  7 0  3.333333 1.7         4         4   5         4 3.75         3    3   3 0
12490000 12490201 7 2 42 12 3   28  8 0  2.666667 1.4  3.666667  3.666667   4         5 4.75 2.3333333    3   2 0
12490000 12490201 8 2 43 13 3   18  3 0  3.166667 1.3 4.3333335 4.3333335   5         3 3.75         3    3   2 0
13345000 13345202 3 2 39 11 2    0  8 0 4.1666665 1.5  3.666667 4.6666665   5         4  4.5         2 2.75   3 1
13588000 13588201 6 1 40  8 3   35  5 0  3.833333 1.7         4 4.3333335   4         5  4.5 2.3333333    3   3 1
13588000 13588201 7 1 41  9 3   35  5 0       4.5 1.6  2.666667         5   4         5    5 1.3333334    2   3 0
13937000 13937201 6 1 41  8 2   50  8 0       3.5 1.2 4.3333335 4.6666665   5         5  4.5         2 2.25   2 1
13937000 13937201 8 1 43 10 2   70  2 0  3.333333 1.3  3.666667         4   4         5  4.5 1.6666666    2 2.5 0
14722000 14722201 3 1 38  9 2   42  5 0  3.666667 1.3 4.3333335  3.333333   5  3.666667  3.5         3 2.25   3 1
14722000 14722201 4 1 39 10 2   39  7 0 4.6666665 1.5 4.6666665         5   4         4 3.75  2.666667 2.25   3 0
14722000 14722201 5 1 40 11 2   45  6 0  3.666667 1.4 4.6666665  3.666667   3         3  3.5         2 1.25   3 0
14722000 14722201 6 1 41 12 2   48  5 0  3.333333 1.4 4.3333335  3.666667 2.5  3.666667 3.75 2.3333333 1.25 1.5 0
14722000 14722201 7 1 42 13 2   45  7 0       3.5 1.4 4.3333335  3.333333   3         4    3 2.3333333  1.5   2 0
14722000 14722201 8 1 43 14 2   45  8 0       3.5 1.5         5  3.666667   3  3.333333 2.75 2.3333333 1.75   2 0
14898000 14898201 4 2 40 11 3   12  7 0  3.666667 1.4 4.3333335 4.3333335   5 4.3333335 4.25         3    3   2 1
14898000 14898201 6 2 42 13 3   15  7 0 4.1666665 1.4 4.6666665 4.3333335   4         4    4 2.3333333    2 2.5 0
14902000 14902201 5 2 30  8 1   25  5 0       4.5 1.3         5 4.6666665   5         5 4.75 1.3333334 1.75   4 1
14902000 14902201 6 2 31  9 1   25  7 0 4.6666665 1.3         5         5   5         5    5         1 1.25 3.5 0
14902000 14902201 8 2 33 11 1   25  7 0       4.5   1         5 4.6666665   5         5    5  2.666667    3   3 0
15595000 15595201 4 1 40  7 2   40  9 0 4.3333335 1.1         5         5   5 4.6666665 4.75 2.3333333 2.75   2 1
15595000 15595201 5 1 41  9 2   40  9 0 4.3333335   1         5         5   5         5 4.75 2.3333333 2.25   1 0
15595000 15595201 6 1 42 10 2   45 10 0       4.5 1.2         5         5   5         5    5         2 2.25   1 0
15595000 15595201 7 1 43 11 2   45  9 0         4   1         5         4   5 4.6666665    5 2.3333333  2.5   1 0
15595000 15595201 8 1 44 12 2   50  9 0         4   1         5 4.3333335 4.5 4.6666665 4.75         2 2.75   1 0
16512000 16512202 4 1 39 10 2   38  7 0 4.1666665 1.6 4.3333335         5   5         4    5  2.666667    3   3 1
16512000 16512202 5 1 40 11 2   39  7 0       4.5 1.7         4         5   5         4    5 2.3333333    3 2.5 0
16512000 16512202 6 1 41 12 2   39  8 0 4.1666665 1.5  3.666667         5 4.5  3.666667    5         2 2.75   3 0
16512000 16512202 7 1 42 13 2   40  8 0         4 1.6  3.666667 4.6666665   5  3.666667 4.75  2.666667    3   2 0
16512000 16512202 8 1 43 14 2   42  8 0  3.833333 1.6         4 4.3333335 3.5  3.666667    5 2.3333333 3.25 2.5 0
16671000 16671201 3 2 39 10 1   36  8 0  3.333333 1.5 4.3333335 4.3333335   3         4 3.75         3 3.25   2 1
16671000 16671201 4 2 40 11 1   40  8 0  3.166667 1.3         4  3.666667   4         4 3.75 2.3333333    3   3 0
16829000 16829201 3 2 38 12 2 19.5  6 0  3.833333 2.4         3 4.3333335 4.5  3.666667 3.75         3  2.5   2 1
16829000 16829201 4 2 39 13 2   19  6 0 1.6666666 2.6  3.666667 2.3333333   3         3    3         3 2.25   2 0
16829000 16829201 5 2 40 14 2   19  0 0       2.5 2.3  3.333333  3.666667 2.5  3.333333 3.25 2.3333333 2.25   3 0
17018000 17018203 3 2 38 10 5    0 10 0  3.833333 1.2 4.3333335 4.3333335 4.5 4.3333335 3.75  3.333333 2.25   1 1
17018000 17018203 4 2 40 11 5    3  9 0         4 1.4 4.3333335         4   4 4.3333335 3.75  2.666667 1.75   1 0
17018000 17018203 5 2 41 12 5    6 10 0  3.833333 1.3         4         5   4         4  3.5  2.666667 2.25   1 0
17018000 17018203 6 2 41 13 5    0 10 0  3.666667 1.4 4.3333335 4.3333335   3 4.3333335 3.25  2.666667 2.25   1 1
17018000 17018203 7 2 43 14 5    0 10 0         4 1.5 4.3333335         5   5 4.3333335  3.5 2.3333333  2.5   1 0
17018000 17018203 8 2 43 15 5    0  8 0       4.5 1.3 4.3333335         5   3 4.3333335 3.75         3 2.75   2 1
17464000 17464202 3 2 38  9 3    0  7 0  4.833333 1.7  3.666667         5   5         4  4.5         2 2.75 2.5 1
17464000 17464202 4 2 40 11 3    0  8 0 4.1666665 1.9  2.666667         5   5 4.6666665  4.5 2.3333333 2.25   1 0
17464000 17464202 5 2 40 11 3   20  8 0         4 1.5         4 4.3333335   5         5    5         2 2.25 1.5 0
17464000 17464202 6 2 41 12 3   20 10 0  3.166667 1.4         4  3.666667   5         4  4.5 2.3333333  2.5   1 0
18011000 18011201 3 1 39  8 1   50  8 0  2.666667 1.2 4.3333335  3.666667 3.5  3.666667 3.75  3.333333    3   2 1
end
label values wave WAVE_prt3
label def WAVE_prt3 3 "3 2010/11", modify
label values sex_gen sex_genlbl_ac3
label def sex_genlbl_ac3 1 "1 Male", modify
label def sex_genlbl_ac3 2 "2 Female", modify
label values age age_ac3
label values nkidsliv nkids_ac3
label values job7 liste233_ac3
label def liste233_ac3 0 "0 Keine", modify
label values inc28 liste4_ac3
label def liste4_ac3 0 "0 Sehr unzufrieden", modify
label def liste4_ac3 10 "10 Sehr zufrieden", modify
label values transition transition
label def transition 0 "0 k. Veränderung", modify
label def transition 1 "1 Veränderung", modify
label values bezqual_kind bezqual_kind
label values depressive depressive
label def depressive 1 "1 niedrig", modify
label values selfesteem selfesteem
label def selfesteem 5 "5 hoch", modify
label values cwarmth_cao cwarmth_cao
label def cwarmth_cao 5 "5 hoch", modify
label values cmonitor_cao cmonitor_cao
label def cmonitor_cao 5 "5 hoch", modify
label values warmth_pacs warmth_pacs
label def warmth_pacs 5 "5 hoch", modify
label values monitor_pacs monitor_pacs
label def monitor_pacs 5 "5 hoch", modify
label values negcomm_pacs negcomm_pacs
label def negcomm_pacs 1 "1 niedrig", modify
label values inconsist_pacs inconsist_pacs
label values fsit_a fsit_a
label def fsit_a 1 "1 gut", modify
label def fsit_a 5 "5 weniger gut", modify
label values move move
label def move 0 "kein Umzug", modify
label def move 1 "Umzug", modify
    Last edited by sladmin; 28 Jan 2019, 09:17. Reason: anonymize original poster
    Tags: None
  • #2
    Guest:
    I'm not sure I've got it right by I would interact -transition- with -sex_gen- among your predictors:
    Code:
    i.transition##i.sex_gen
    Last edited by sladmin; 28 Jan 2019, 09:18. Reason: anonymize original poster
    Kind regards,
    Carlo
    (Stata 19.0)

    Comment

    • #3
      Originally posted by Carlo Lazzaro View Post
      Guest:
      I'm not sure I've got it right by I would interact -transition- with -sex_gen- among your predictors:
      Code:
      i.transition##i.sex_gen
      Hi Carlo,

      I guess that's not what i wanted to know :
      Is there a possibility to show if parents in a stable relationship (transition==0) have a higher parent-child-relationshipquality to their children than parents in unstable relationships (transition==1)? I thought that this is not possible because stata only 'counts' the obs with a change.
      Last edited by sladmin; 28 Jan 2019, 09:18. Reason: anonymize original poster

      Comment

      • #4
        Guest:
        try:
        -testparm(i.transition)-
        Last edited by sladmin; 28 Jan 2019, 09:18. Reason: anonymize original poster
        Kind regards,
        Carlo
        (Stata 19.0)

        Comment

        • #5
          Originally posted by Carlo Lazzaro View Post
          Guest:
          try:
          -testparm(i.transition)-
          Maybe I have to do OLS regression and take the parent-child-relationship-quality of wave 8 as dependent variable. the independent variable would compare parents who experienced a change with parents without a change ??
          Last edited by sladmin; 28 Jan 2019, 09:18. Reason: anonymize original poster

          Comment

          • #6
            /edit

            Code:
            gen bezqual_a_wave8=.
replace bezqual_a_wave8= bezqual_anker if wave ==8

regress bezqual_a_wave8 i.transition

            Comment

            • #7
              Guest:
              doesn't -testparm- give you what you're after?
              Last edited by sladmin; 28 Jan 2019, 09:18. Reason: anonymize original poster
              Kind regards,
              Carlo
              (Stata 19.0)

              Comment

              • #8
                Originally posted by Carlo Lazzaro View Post
                Guest:
                doesn't -testparm- give you what you're after?
                Code:
                . testparm(i.transition)
no such variables;
the specified varlist does not identify any testable coefficients
r(111);
                Last edited by sladmin; 28 Jan 2019, 09:19. Reason: anonymize original poster

                Comment

                • #9
                  Guest:
                  I'm probably missing out on something, but:
                  Code:
                  . xtset id wave
       panel variable:  id (unbalanced)
        time variable:  wave, 3 to 8, but with gaps
                delta:  1 unit

. xtreg bezqual_kind transition depressive selfesteem fsit_a inc28 move job7 warmth_pacs monitor_pacs negcomm_pacs inconsist_pacs cwarmth_cao cmonitor_cao age cagey nkidsliv, fe

Fixed-effects (within) regression               Number of obs     =        100
Group variable: id                              Number of groups  =         29

R-sq:                                           Obs per group:
     within  = 0.6419                                         min =          1
     between = 0.0755                                         avg =        3.4
     overall = 0.1992                                         max =          6

                                                F(16,55)          =       6.16
corr(u_i, Xb)  = -0.3956                        Prob > F          =     0.0000

--------------------------------------------------------------------------------
  bezqual_kind |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
    transition |  -.3112258   .2554528    -1.22   0.228    -.8231647    .2007131
    depressive |   .2586801   .2072746     1.25   0.217    -.1567075    .6740677
    selfesteem |  -.1136406   .1020224    -1.11   0.270     -.318098    .0908169
        fsit_a |   .2524674   .0839588     3.01   0.004     .0842101    .4207247
         inc28 |   .0108446   .0330554     0.33   0.744       -.0554    .0770892
          move |   .0914197   .1050512     0.87   0.388    -.1191076    .3019469
          job7 |   .0039869   .0066953     0.60   0.554    -.0094308    .0174047
   warmth_pacs |  -.0922729   .1179806    -0.78   0.438    -.3287112    .1441654
  monitor_pacs |   .1639286    .136161     1.20   0.234    -.1089442    .4368013
  negcomm_pacs |   .2012919   .1213961     1.66   0.103    -.0419914    .4445752
inconsist_pacs |  -.1560829   .1245318    -1.25   0.215    -.4056502    .0934844
   cwarmth_cao |   .5881641    .092449     6.36   0.000     .4028922     .773436
  cmonitor_cao |   .1393749   .0717155     1.94   0.057    -.0043462     .283096
           age |  -.0728931   .1695725    -0.43   0.669    -.4127239    .2669378
         cagey |   .0924232   .1632328     0.57   0.574    -.2347027    .4195491
      nkidsliv |   .0230413   .3416313     0.07   0.946    -.6616032    .7076858
         _cons |   1.271169    5.16992     0.25   0.807    -9.089582    11.63192
---------------+----------------------------------------------------------------
       sigma_u |  .64178232
       sigma_e |  .31019701
           rho |  .81062603   (fraction of variance due to u_i)
--------------------------------------------------------------------------------
F test that all u_i=0: F(28, 55) = 2.86                      Prob > F = 0.0004

. testparm(transition)

 ( 1)  transition = 0

       F(  1,    55) =    1.48
            Prob > F =    0.2283
                  Sidelight: -fvvarlist- notation for categorical variables and interactions is highly recommended.
                  Last edited by sladmin; 28 Jan 2019, 09:19. Reason: anonymize original poster
                  Kind regards,
                  Carlo
                  (Stata 19.0)

                  Comment

                  • #10
                    Originally posted by Carlo Lazzaro View Post
                    Guest:
                    I'm probably missing out on something, but:
                    Code:
                    . xtset id wave
panel variable: id (unbalanced)
time variable: wave, 3 to 8, but with gaps
delta: 1 unit

. xtreg bezqual_kind transition depressive selfesteem fsit_a inc28 move job7 warmth_pacs monitor_pacs negcomm_pacs inconsist_pacs cwarmth_cao cmonitor_cao age cagey nkidsliv, fe

Fixed-effects (within) regression Number of obs = 100
Group variable: id Number of groups = 29

R-sq: Obs per group:
within = 0.6419 min = 1
between = 0.0755 avg = 3.4
overall = 0.1992 max = 6

F(16,55) = 6.16
corr(u_i, Xb) = -0.3956 Prob > F = 0.0000

--------------------------------------------------------------------------------
bezqual_kind | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
transition | -.3112258 .2554528 -1.22 0.228 -.8231647 .2007131
depressive | .2586801 .2072746 1.25 0.217 -.1567075 .6740677
selfesteem | -.1136406 .1020224 -1.11 0.270 -.318098 .0908169
fsit_a | .2524674 .0839588 3.01 0.004 .0842101 .4207247
inc28 | .0108446 .0330554 0.33 0.744 -.0554 .0770892
move | .0914197 .1050512 0.87 0.388 -.1191076 .3019469
job7 | .0039869 .0066953 0.60 0.554 -.0094308 .0174047
warmth_pacs | -.0922729 .1179806 -0.78 0.438 -.3287112 .1441654
monitor_pacs | .1639286 .136161 1.20 0.234 -.1089442 .4368013
negcomm_pacs | .2012919 .1213961 1.66 0.103 -.0419914 .4445752
inconsist_pacs | -.1560829 .1245318 -1.25 0.215 -.4056502 .0934844
cwarmth_cao | .5881641 .092449 6.36 0.000 .4028922 .773436
cmonitor_cao | .1393749 .0717155 1.94 0.057 -.0043462 .283096
age | -.0728931 .1695725 -0.43 0.669 -.4127239 .2669378
cagey | .0924232 .1632328 0.57 0.574 -.2347027 .4195491
nkidsliv | .0230413 .3416313 0.07 0.946 -.6616032 .7076858
_cons | 1.271169 5.16992 0.25 0.807 -9.089582 11.63192
---------------+----------------------------------------------------------------
sigma_u | .64178232
sigma_e | .31019701
rho | .81062603 (fraction of variance due to u_i)
--------------------------------------------------------------------------------
F test that all u_i=0: F(28, 55) = 2.86 Prob > F = 0.0004

. testparm(transition)

( 1) transition = 0

F( 1, 55) = 1.48
Prob > F = 0.2283
                    Sidelight: -fvvarlist- notation for categorical variables and interactions is highly recommended.
                    it worked! thank you

                    I thought if its a dummy variable i don't have to type "i.transition" or "i.move" only if there are more than 2 categories.
                    Last edited by sladmin; 28 Jan 2019, 09:19. Reason: anonymize original poster

                    Comment

                    • #11
                      Guest:
                      I would mildly disagree with your last statement.
                      In this recent threads (https://www.statalist.org/forums/for...dummy-variable), Clyde clerly explained why using -fvvarlist- is a very good habit even with two-level categorical variables.
                      Last edited by sladmin; 28 Jan 2019, 09:19. Reason: anonymize original poster
                      Kind regards,
                      Carlo
                      (Stata 19.0)

                      Comment

                      • #12
                        Originally posted by Carlo Lazzaro View Post
                        Guest:
                        I would mildly disagree with your last statement.
                        In this recent threads (https://www.statalist.org/forums/for...dummy-variable), Clyde clerly explained why using -fvvarlist- is a very good habit even with two-level categorical variables.
                        Thank you! Indeed I unterstand why using this is a good idea. I will change it in my fe model.

                        doesn't -testparm- give you what you're after?
                        my professor replied and told me to run OLS regression (now I have to do this).
                        I prepared the data like you can see below:

                        Code:
                        gen bezqual_a8=. // Beziehungsqualität Anker Welle 8
replace bezqual_a8 = bezqual_anker if wave==8
mvdecode bezqual_a8, mv (-99/-1=.a)
drop missings
egen missings = rowmiss (bezqual_a8)
keep if missings==0

gen bezqual_k8=. // Bezehungsqualität Kind Welle 8
replace bezqual_k8 = bezqual_kind if wave==8
mvdecode bezqual_k8, mv (-99/-1=.a)
drop missings
egen missings = rowmiss (bezqual_k8)
keep if missings==0

gen trans_w8=. // Veränderungen Beziehungsstatus von Anker
replace trans_w8=0 if transition_count ==0
replace trans_w8=1 if transition_count >=1
                        Code:
                        regress bezqual_a8 i.trans_w8
estimates store step1

regress bezqual_a8 i.trans_w8 depressive selfesteem
estimates store step2

regress bezqual_a8 i.trans_w8 depressive selfesteem fsit_a inc28
estimates store step3

regress bezqual_a8 i.trans_w8 depressive selfesteem fsit_a inc28 i.move
estimates store step4

regress bezqual_a8 i.trans_w8 depressive selfesteem fsit_a inc28 i.move_w8
estimates store step5

regress bezqual_a8 i.trans_w8 depressive selfesteem fsit_a inc28 i.move_w8 jobsit_a cwarmth_cao cmonitor_cao warmth_pacs monitor_pacs negcomm_pacs inconsist_pacs
estimates store step6

regress bezqual_a8 i.trans_w8 depressive selfesteem fsit_a inc28 i.move_w8 jobsit_a cwarmth_cao cmonitor_cao warmth_pacs monitor_pacs negcomm_pacs inconsist_pacs i.siblings i.stepparent agea_c agec_c i.femalec i.femalea i.education i.east
estimates store step7

estimates table step1 step2 step3 step4 step5 step6 step7, b(%7.4f) stats(N r2_a) star varlabel
                        Code:
                        ---------------------------------------------------------------------------------------------------------------------
                Variable |   step1        step2        step3        step4        step5        step6        step7    
-------------------------+-------------------------------------------------------------------------------------------
                trans_w8 |
                      1  |  0.0161       0.0588       0.0588       0.0677       0.0677       0.0677       0.0525    
Ankerperson: Depressiv~b |              -0.1325**    -0.1325**    -0.1435**    -0.1435**    -0.1435**    -0.0563    
Ankerperson: Selbstwer~e |               0.1221***    0.1221***    0.1232***    0.1232***    0.1232***    0.0653**  
Finanzielle Situation ~  |                                        -0.0060      -0.0060      -0.0060      -0.0057    
Zufriedenheit mit fina~S |                                        -0.0095      -0.0095      -0.0095      -0.0115    
                         |
                 move_w8 |
                      1  |                                        -0.1475*     -0.1475*     -0.1475*     -0.0475    
 Erwerbsstatus von Anker |                                                                               -0.0347    
Kind: Emotionale Wärme~r |                                                                                0.0588*    
Kind: Monitoring in de~n |                                                                                0.0431*    
Emotionale Wärme in de~n |                                                                                0.2455***  
Monitoring in der Erzi~t |                                                                                0.2141***  
Negative Kommunikation~r |                                                                               -0.1712***  
Inkonsistente Erziehun~p |                                                                               -0.0609*    
                         |
                siblings |
          1 Geschwister  |                                                                               -0.0295    
          2 Geschwister  |                                                                               -0.0266    
3 oder mehr Geschwister  |                                                                               -0.0315    
                         |
              stepparent |
        Stiefelternteil  |                                                                               -0.0039    
     Pflege/Adoptivkind  |                                                                               -0.0194    
 Zentriertes Alter Anker |                                                                                0.0017    
  Zentriertes Alter Kind |                                                                               -0.0082    
                         |
                 femalec |
               Weiblich  |                                                                               -0.0218    
                         |
                 femalea |
               Weiblich  |                                                                               -0.0616    
                         |
               education |
lower secondary educa..  |                                                                                0.0479    
upper secondary educa..  |                                                                                0.0752    
post secondary non te..  |                                                                                0.0338    
     tertiary education  |                                                                                0.0550    
                         |
                    east |
                  1 Yes  |                                                                                0.0238    
                Constant |  3.7179***    3.4753***    3.4753***    3.5763***    3.5763***    3.5763***    1.9244***  
-------------------------+-------------------------------------------------------------------------------------------
                       N |     640          640          640          640          640          640          640    
                    r2_a | -0.0014       0.0909       0.0909       0.0937       0.0937       0.0937       0.4793    
---------------------------------------------------------------------------------------------------------------------
                                                                             legend: * p<0.05; ** p<0.01; *** p<0.001


                        do you think that I have to add all other independent variables, like i do in the fe model? The adj. R2 is -0.0014, so the model is not good at all. I never had a negative value at R2 before.. :-(

                        Code:
                        * Example generated by -dataex-. To install: ssc install dataex
clear
input long id byte(wave east inc28) float(depressive selfesteem cwarmth_cao cmonitor_cao warmth_pacs monitor_pacs negcomm_pacs inconsist_pacs fsit_a jobsit_anker education stepparent siblings bezqual_a8 trans_w8 move_w8 agec_c agea_c femalec femalea)
   111000 8 1  5 1.4 4.6666665         5   3         4  3.5 1.6666666 2.75   5 0 3 1 0 3.8 1 1  1.52656  3.21406 0 1
  3902000 8 0  3 1.5 4.3333335         5   4 4.6666665 4.25 2.3333333  2.5   4 2 5 0 2 4.2 0 0 -1.47344  3.21406 1 0
  6151000 8 0  8 1.1 4.3333335 4.6666665 4.5         5 4.75  2.666667 1.75 1.5 1 5 0 1 3.6 0 0 -1.47344  2.21406 0 1
  6519000 8 1  7 2.5  3.333333 4.6666665 3.5         4 3.75         3    4 1.5 2 3 0 0 3.2 1 0  1.52656  4.21406 1 1
  8948000 8 1  5 1.6 4.3333335  3.666667   3         4 3.25         2 1.75 3.5 2 4 1 0 3.2 0 0  1.52656  2.21406 0 1
  9657000 8 0 10 1.9         4 4.3333335 3.5 4.3333335 4.75         3  3.5   1 1 5 0 2 3.6 0 0 -1.47344  3.21406 0 1
  9917000 8 1 10 1.2 4.3333335         5   5 4.3333335 4.25  3.333333 2.25   2 2 5 0 1 3.4 0 0 -1.47344  1.21406 0 0
 10208000 8 1  7 1.6         4         4   4         4 4.25  2.666667 2.25   2 2 5 0 1 3.8 0 0   .52656  2.21406 1 0
 11295000 8 0  7 2.1 4.3333335 4.6666665   5         4 4.75         2 2.75   3 1 4 0 1 3.4 0 1  -.47344  1.21406 0 1
 12490000 8 0  3 1.3 4.3333335 4.3333335   5         3 3.75         3    3   2 1 5 0 2   3 0 0   .52656  2.21406 1 1
 13937000 8 1  2 1.3  3.666667         4   4         5  4.5 1.6666666    2 2.5 2 3 0 1 3.8 0 0 -2.47344  2.21406 1 0
 14722000 8 0  8 1.5         5  3.666667   3  3.333333 2.75 2.3333333 1.75   2 2 4 0 1   3 0 0  1.52656  2.21406 1 0
 14902000 8 0  7   1         5 4.6666665   5         5    5  2.666667    3   3 1 3 1 0 4.6 0 0 -1.47344 -7.78594 1 1
 15595000 8 1  9   1         5 4.3333335 4.5 4.6666665 4.75         2 2.75   1 2 5 0 1 4.4 0 0  -.47344  3.21406 1 0
 16512000 8 0  8 1.6         4 4.3333335 3.5  3.666667    5 2.3333333 3.25 2.5 2 3 0 1   3 0 0  1.52656  2.21406 0 0
 17018000 8 0  8 1.3 4.3333335         5   3 4.3333335 3.75         3 2.75   2 0 5 0 3 3.4 0 1  2.52656  2.21406 0 1
 18356000 8 0  8   1         5         5   5         5    5         2 1.25   2 1 3 0 2 4.8 0 0   .52656 -7.78594 1 1
 18814000 8 0  5   1         5         5 3.5         5 4.75         2  1.5   3 2 2 1 0 4.8 0 0   .52656  3.21406 1 1
 19673000 8 0  8 1.5 4.3333335  3.666667 3.5 4.3333335    4         2    3 1.5 1 3 0 0   4 0 0  1.52656  3.21406 1 1
 21446000 8 0  7 1.8         3         4 2.5         3 3.75 1.6666666    2   3 2 3 0 2 3.6 0 0   .52656  1.21406 1 0
 23932000 8 1  5 1.3         5 2.3333333   3 4.6666665 4.75         1 1.75 1.5 2 4 1 1   5 0 1  1.52656  2.21406 0 1
 24908000 8 0  8 2.9  3.333333         5   5         5 4.75         3    3   1 1 3 0 1   4 0 0  1.52656  1.21406 0 1
 25498000 8 0  5 1.5  3.666667         4 4.5  3.666667 3.25         2 2.75   4 2 5 0 1 3.4 0 0  2.52656  1.21406 0 0
 25800000 8 1  9 1.3 4.3333335         4 4.5 4.6666665  3.5 1.3333334 2.25   1 1 3 0 1 4.2 0 0  2.52656  3.21406 1 1
 27564000 8 0  7 1.1 4.6666665         5   5         5    5 1.6666666 1.75   2 1 3 0 0 4.6 0 0  -.47344  1.21406 1 1
 28218000 8 0  7 1.1         5         5 4.5         5 4.75 2.3333333 3.25   3 2 5 0 2 4.2 0 0 -1.47344  2.21406 0 1
 28386000 8 0  8 1.1         4 4.6666665   5         4  3.5         2 2.75   2 2 5 0 0   4 0 0 -3.47344  3.21406 0 0
 34410000 8 0  7 1.1         5         5   5         5  4.5 1.3333334 2.25   2 2 3 0 1 4.2 0 0   .52656  3.21406 1 0
 35026000 8 1  5 1.4 4.3333335         4 3.5 4.3333335 3.25         3  3.5   3 1 4 0 2 3.4 0 1   .52656  3.21406 0 1
 35363000 8 0  7 2.5         5 4.3333335   5         4 3.75         2    3   3 0 3 0 2 3.6 0 0  2.52656  1.21406 1 1
 35799000 8 0  8   1         5         5   5         5 4.25 1.3333334 3.25   1 1 3 0 1 4.8 0 0  -.47344  2.21406 0 1
 39562000 8 0  8   1         5         5   5         5  4.5 1.6666666  1.5   2 2 5 0 1 4.2 0 0  1.52656  3.21406 1 0
 40170000 8 0  5 1.4  3.666667         4   5         4  3.5         3 2.75   3 0 5 1 2 3.8 0 0 -2.47344  3.21406 1 1
 40476000 8 1  3 2.2  3.666667 4.3333335   5         5  4.5 1.3333334 1.75   2 2 3 0 0 4.2 0 0  -.47344  1.21406 0 0
 41957000 8 1  9   2  3.666667 4.6666665   5         4  4.5 2.3333333 2.75   1 2 5 0 1 3.6 0 0 -3.47344  1.21406 1 1
 42433000 8 0  6 2.7 2.3333333 4.6666665   5         5    4         2 2.75   3 0 3 1 1 3.8 1 0   .52656  2.21406 0 1
 42654000 8 1  7 2.1  3.333333 4.3333335 2.5         4    3  3.333333  3.5   3 2 5 0 0 3.4 0 0 -1.47344 -6.78594 0 1
 45091000 8 1  7   1  3.666667         4   5 4.6666665 4.75         2  2.5   3 2 3 0 0 3.8 0 0  1.52656  3.21406 0 1
 45677000 8 1  6 1.5 4.6666665  3.333333 3.5  3.666667    4 2.3333333 2.25 2.5 1 5 0 1 3.4 0 0  2.52656  1.21406 0 1
 48484000 8 0  8 1.6  2.666667 4.6666665   4 4.3333335 4.25         2 1.75   1 2 3 1 0 3.8 1 0  1.52656  3.21406 1 1
 51364000 8 0  5 1.8  3.666667 4.6666665 3.5         4  3.5         2 3.25 3.5 2 5 0 0   4 0 1 -3.47344  2.21406 0 0
 51775000 8 0  2 1.1         4         5 4.5         5    5 2.3333333 2.75   2 1 5 0 0 4.4 0 0 -2.47344  3.21406 1 1
 55104000 8 0  2   2 4.3333335 4.6666665   5 4.3333335 4.75 1.6666666    2   5 2 5 0 2 3.6 0 0 -1.47344  2.21406 0 0
 55124000 8 0  8 1.1         5         5   5         5    5 2.3333333  1.5 1.5 1 3 0 1 4.4 0 0  -.47344  1.21406 1 1
 57892000 8 1  7 1.3         5         5   5 4.3333335  4.5 1.6666666  2.5   3 2 3 0 1 4.2 0 0 -1.47344 -6.78594 0 0
 61002000 8 0  9 1.5  3.666667  3.333333   3         4    4  2.666667 2.25   2 1 3 0 2 3.2 0 0  2.52656  3.21406 0 1
 61933000 8 0  9 1.9 4.3333335 4.3333335 2.5         3 2.75         3 2.25   1 2 3 0 1   3 0 0  -.47344  2.21406 1 0
 63248000 8 0 10 1.7  3.666667         5   4         5    4  2.666667    5   1 2 5 2 0 3.2 1 0  2.52656  3.21406 1 1
 66953000 8 0 10 1.6 4.3333335 4.6666665   5         4 3.75         2    2   2 2 5 0 2 3.6 0 0 -2.47344  3.21406 1 0
 67350000 8 0  7 1.8 4.3333335         5   3  3.666667  3.5 2.3333333  2.5   2 2 5 0 1 3.6 0 0   .52656  2.21406 1 0
 70472000 8 0  6   2 4.3333335         5   4 4.6666665    4 2.3333333    2   3 1 3 0 1 4.2 0 0 -3.47344 -8.78594 1 1
 71780000 8 0  4 2.1         2 4.6666665   5 4.6666665 3.75 2.3333333 2.25   3 2 3 0 1 3.6 0 0  2.52656  2.21406 1 1
 72344000 8 0  8 2.1  3.666667  3.666667   5         4 3.75 2.3333333  2.5 1.5 2 3 0 0 3.2 0 0   .52656  3.21406 1 0
 73923000 8 0  9 1.4         5         5   5 4.3333335  4.5         2  2.5 1.5 1 3 1 1 3.8 1 0  1.52656  3.21406 1 1
 75205000 8 0  8 1.8 4.3333335         4   4 4.6666665    4         2  2.5   4 2 2 1 1   4 1 1   .52656  2.21406 1 1
 76058000 8 1  7 1.9 4.6666665  2.666667   2         4    4 1.6666666 2.75   1 2 3 2 1 3.6 0 0   .52656  3.21406 1 0
 76321000 8 0 10 1.4         4         3   3         3  3.5 1.6666666 1.75   1 1 5 0 2 3.6 0 0  1.52656  2.21406 0 1
 77540000 8 0  8 1.5 4.3333335         4   4         4 3.75 2.3333333 2.75   2 2 5 0 1 3.8 0 0  1.52656  3.21406 1 0
 77982000 8 0 10 1.1         5         5   5         5    5         1 1.25   1 1 3 1 1   5 1 0  1.52656  2.21406 0 1
 80857000 8 0  8   1         5         5   5         5 4.75         2 2.75   1 2 3 1 1   4 1 1   .52656  1.21406 1 1
 81933000 8 0  8 1.7 4.6666665 4.3333335   5         3    4         4 2.25 2.5 2 5 0 1 3.2 0 0 -1.47344 -7.78594 0 1
 83910000 8 0  0 2.3         4 4.6666665   4         5  4.5  2.666667 3.75   4 1 3 1 0 3.8 1 0 -2.47344  2.21406 0 1
 84146000 8 0  7 1.9  3.333333         5   5 4.3333335 4.25  3.333333  2.5   2 1 5 0 0 3.8 0 0  1.52656  1.21406 1 1
 86889000 8 0  5 1.6         4 4.6666665 4.5         4    4  2.666667 2.25   3 1 5 0 0   4 0 0  1.52656  2.21406 0 1
 90440000 8 0 10 1.7 4.3333335         5   4         5  4.5  3.666667    3   1 2 5 0 1 3.8 0 0 -2.47344  1.21406 0 1
 93240000 8 1  7 1.8 4.3333335         4 2.5         4  3.5         2    2 3.5 2 3 0 1 3.4 0 0   .52656  1.21406 1 0
 93888000 8 0  7 1.6 4.3333335 4.3333335 2.5 4.3333335    4 1.3333334    3   1 2 3 1 0   4 1 0  1.52656   .21406 0 1
 94425000 8 0  5 1.9  3.333333 4.3333335   5         4 3.75 2.3333333  2.5   4 2 3 0 1 3.6 0 0  1.52656  3.21406 0 0
 94590000 8 0  5 2.4 2.3333333  2.666667   3         4 3.75         3  2.5 3.5 1 4 0 2 3.2 0 0   .52656  3.21406 1 1
 95276000 8 0  9 1.8  2.666667  3.666667 4.5 4.6666665  4.5         3    2   1 1 5 1 0   4 1 0 -1.47344  3.21406 0 1
 95759000 8 1  6 2.1  3.666667 4.6666665   5         5    5 1.6666666    2   2 2 5 1 0 3.8 0 0 -2.47344  1.21406 1 1
 97290000 8 0  6 1.3 4.3333335         4   4 4.6666665 4.25 2.3333333 2.75 2.5 2 5 1 1 3.4 0 0  1.52656 -6.78594 1 1
 97393000 8 0  8 1.5         5 4.3333335   5         5 4.75         2 2.25   1 1 3 1 1 4.6 0 0  1.52656 -6.78594 1 1
 98360000 8 1  5   1         5         4   3         4    4 1.6666666 1.75 2.5 2 3 0 1 4.4 0 0 -1.47344  1.21406 0 0
 98772000 8 1  5 1.7  3.666667  3.666667   5         4    4         2  1.5 2.5 2 3 0 3 3.4 0 0 -2.47344 -8.78594 0 0
100034000 8 0  9 1.9  3.333333 4.3333335   5         4    4  3.666667    4   2 2 5 0 2 3.4 0 0 -2.47344  3.21406 1 0
100198000 8 0  7 1.9  3.666667         4   5         4 3.75         2    3   2 2 4 0 1 3.6 0 0  1.52656  3.21406 0 0
101121000 8 1  5   2         5         3   2         3  2.5         3 3.75   5 1 3 0 2 2.8 0 0  -.47344 -6.78594 0 1
102758000 8 0  9   1         5         5   5 4.3333335    4 2.3333333    3 1.5 1 5 0 1   4 0 0 -2.47344  2.21406 1 1
104426000 8 0  3 1.9 2.3333333  3.666667   3 4.6666665 3.75  3.666667  3.5   5 2 3 1 0 3.4 0 1  -.47344 -6.78594 1 1
106294000 8 0  5 1.2         5         5   5         5  4.5 1.6666666 1.75   1 1 1 0 0 4.2 1 0  2.52656  3.21406 1 1
106393000 8 0  9 1.5         4         4   5  3.666667    3  3.333333 3.75 1.5 2 5 0 1 2.8 0 0 -3.47344  2.21406 1 0
107873000 8 0  7 1.5  3.333333 4.3333335   4         4    4         3 1.75   2 2 5 0 1 3.8 0 0 -1.47344  2.21406 0 0
109954000 8 0  5 2.9 2.3333333         4   5  3.666667    4         3  2.5 2.5 1 4 0 1   3 0 0  1.52656  2.21406 0 1
110610000 8 0  9 1.6 4.3333335         4 3.5 4.3333335  4.5         3    3   1 1 5 0 2 3.6 0 0  1.52656  3.21406 0 1
111849000 8 1  7 1.5 4.3333335         4 4.5 4.3333335 4.25  2.666667    3 2.5 2 3 0 1 3.6 0 0   .52656  2.21406 0 1
112212000 8 0  7 2.3  2.666667 4.6666665   3 4.6666665    4         2 2.25   2 2 5 0 2 4.2 0 0   .52656  2.21406 1 0
114404000 8 1  3 2.2  3.666667 4.6666665 4.5         5    5 2.3333333  2.5 4.5 0 5 0 1   4 0 0 -2.47344  2.21406 0 1
118610000 8 0  6 1.6         4 4.6666665   5         3    4         3    2 3.5 1 3 0 2 2.4 0 0 -1.47344 -7.78594 0 1
118729000 8 1  0 1.8  3.666667         4   3  3.666667    3 2.3333333    3   3 0 3 1 1 2.4 0 0  2.52656 -8.78594 0 1
119542000 8 0 10   2         4         5   2         5  4.5         2    3   1 2 5 0 1 4.2 0 0 -3.47344  1.21406 1 0
122017000 8 0  5 2.3  3.333333  3.333333   4         3 3.75         3    3   3 2 5 1 0 3.2 0 1  -.47344  2.21406 0 1
122141000 8 0  8 1.1         5 4.3333335   4 4.6666665 4.25 1.6666666 1.75 4.5 1 2 0 0 4.2 0 1  2.52656  2.21406 0 1
122556000 8 0  9 1.9  3.333333 4.3333335   4  3.666667 3.25         3 2.75   1 2 5 0 1 2.8 0 0  2.52656  3.21406 1 0
123048000 8 0  3 1.3 4.3333335 4.6666665   4         4    4 2.3333333  2.5 3.5 1 3 1 2 2.8 1 0  2.52656  3.21406 0 1
123827000 8 0  7 1.4 4.3333335         4   5         3  3.5         2  2.5 3.5 2 5 0 1 3.2 0 0  -.47344  3.21406 0 0
124978000 8 0  5 1.6  3.666667         5   5         5    5         2    2   5 1 5 0 2   4 0 0 -4.47344  1.21406 0 1
125554000 8 1  7 2.4         5 4.6666665   4         5 4.75 2.3333333 2.25   3 2 5 1 0 3.8 0 1 -2.47344 -8.78594 0 1
133474000 8 1  7 1.3 4.3333335 2.3333333 4.5         3 3.75         2  2.5 1.5 2 3 0 1 3.2 0 0   .52656  2.21406 1 0
137636000 8 0  5 2.4 4.3333335 4.6666665   5 4.6666665  4.5  2.666667 2.75   3 1 3 1 2   4 0 0 -3.47344  3.21406 0 1
end
label values wave WAVE_prt3
label values east east_ac3
label def east_ac3 0 "0 No", modify
label def east_ac3 1 "1 Yes", modify
label values inc28 liste4_ac3
label def liste4_ac3 0 "0 Sehr unzufrieden", modify
label def liste4_ac3 10 "10 Sehr zufrieden", modify
label values depressive depressive
label def depressive 1 "1 niedrig", modify
label values selfesteem selfesteem
label def selfesteem 5 "5 hoch", modify
label values cwarmth_cao cwarmth_cao
label def cwarmth_cao 5 "5 hoch", modify
label values cmonitor_cao cmonitor_cao
label def cmonitor_cao 5 "5 hoch", modify
label values warmth_pacs warmth_pacs
label def warmth_pacs 5 "5 hoch", modify
label values monitor_pacs monitor_pacs
label def monitor_pacs 5 "5 hoch", modify
label values negcomm_pacs negcomm_pacs
label def negcomm_pacs 1 "1 niedrig", modify
label values inconsist_pacs inconsist_pacs
label def inconsist_pacs 5 "5 hoch", modify
label values fsit_a fsit_a
label def fsit_a 1 "1 gut", modify
label def fsit_a 5 "5 weniger gut", modify
label values jobsit_anker jobsit_anker
label def jobsit_anker 0 "Nicht erwerbstätig", modify
label def jobsit_anker 1 "Teilzeit", modify
label def jobsit_anker 2 "Vollzeit", modify
label values education education
label def education 1 "no degree", modify
label def education 2 "lower secondary education", modify
label def education 3 "upper secondary education", modify
label def education 4 "post secondary non tertiary education", modify
label def education 5 "tertiary education", modify
label values stepparent stepparent
label def stepparent 0 "kein Stiefelternteil", modify
label def stepparent 1 "Stiefelternteil", modify
label def stepparent 2 "Pflege/Adoptivkind", modify
label values siblings siblings
label def siblings 0 "keine Geschwister", modify
label def siblings 1 "1 Geschwister", modify
label def siblings 2 "2 Geschwister", modify
label def siblings 3 "3 oder mehr Geschwister", modify
label values femalec femalec
label def femalec 0 "Männlich", modify
label def femalec 1 "Weiblich", modify
label values femalea femalea
label def femalea 0 "Männlich", modify
label def femalea 1 "Weiblich", modify

                        Last edited by sladmin; 28 Jan 2019, 09:19. Reason: anonymize original poster

                        Comment

                        • #13
                          Guest:
                          I do not understand the meaning of your superivor's advice.
                          That said:
                          - if you intend to analyze a panel dataset with -regress-, it's mandatory that you -cluster- your standard errors since you do not have independent observations;
                          - you do not actually have a negative Rsq, but a negative adjusted Rsq (which are different beast, as you can retrieve in any decent textbook on regression at large). Probably, your supervisor advised you to show adjusted Rsq as a way to compare the different regression models reported in the table.
                          Last edited by sladmin; 28 Jan 2019, 09:20. Reason: anonymize original poster
                          Kind regards,
                          Carlo
                          (Stata 19.0)

                          Comment

                          • #14
                            Originally posted by Carlo Lazzaro View Post
                            Guest:
                            I do not understand the meaning of your superivor's advice.
                            That said:
                            - if you intend to analyze a panel dataset with -regress-, it's mandatory that you -cluster- your standard errors since you do not have independent observations;
                            - you do not actually have a negative Rsq, but a negative adjusted Rsq (which are different beast, as you can retrieve in any decent textbook on regression at large). Probably, your supervisor advised you to show adjusted Rsq as a way to compare the different regression models reported in the table.
                            She said:

                            (H1: Parents who change their relationship status have a lower Parent-Child-Relationshipquality than parents who are in a stable relationship)

                            "To test this Hypothese you have to do OLS regression. Take the relationship quality of the last wave (8) as dependent variable and as independent variable compare those who had a chance with those who hadn't"

                            I use the value of the relationship-quality of wave 8 as my dependent variable and the information of all waves (3-8) if there was a change of the relationship status (parents perspective). So for this regess i don't have panel data anymore. I know that its not the same but im comparing 2 models by running another regression for my other dependent variable the relationship status (child perspective).
                            Last edited by sladmin; 28 Jan 2019, 09:20. Reason: anonymize original poster

                            Comment

                            • #15
                              I am very confused about everything in this analysis. Guest, let's start from the beginning. What is a wave? I see from the code that basically the wave is the time component, right? The question you're asking is about parents who change their relationship, right? However, if you just take one wave (the 8th one) that is comparing parents that are in a relationship to parents that are out of a relationship. You don't observe the same couple going into the relationship and out of it, because you don't allow time to change. I'm not sure this is measuring the same thing. Carlo Lazzaro suggested clustering the errors if you use regress but if you're simply using one period for the estimation, how can you cluster? Each observations is a cluster.

                              Or is your advisor suggesting that you simply use the quality of the relationship in the last time period for all your observations of the same unit in the previous periods? This provides no within variation in the dependent variable, only between variation. Again, this is only changes in the quality of the relationship across parents, not within parent units. I really, really, don't understand this.

                              Having said that, I'm also confused of why if the test is about a single parameter we are using testparm instead of the test of individual significance that is directly provided by the command.
                              Last edited by sladmin; 28 Jan 2019, 09:20. Reason: anonymize original poster
                              Alfonso Sanchez-Penalver

                              Comment

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