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  • #16
    Hanna:
    just type:
    Code:
    dataex V130 Womansright_index V240 V248 V239 V115V147 V24 V85 V23 in 1/100
    then copy and paste on the General forum.
    Kind regards,
    Carlo
    (Stata 15.1 SE)

    Comment


    • #17
      dataex V130 Womansright_index V240 V248 V239 V115V147 V24 V85 V23 in 1/100

      Comment


      • #18

        dataex V130 Womansright_index V240 V248 V239 V115 V147 V24 V85 V23 in 1/100

        copy starting from the next line -----------------------
        Code:
        * Example generated by -dataex-. To install: ssc install dataex
        clear
        input int V130 float Womansright_index int(V240 V248 V239 V115 V147 V24 V85 V23)
        .  .3333333 0 7 6 2 2 0 2  8
        3  .4166667 0 9 6 3 2 0 2  6
        3  .4166667 1 7 6 3 3 0 2  9
        3  .4166667 1 7 6 3 3 0 1  3
        3  .6666667 1 7 6 3 2 0 1  9
        3 .16666667 1 7 6 2 3 0 2  5
        3        .5 1 9 5 2 3 0 2  8
        3       .25 0 7 5 3 3 0 2  8
        3       .25 0 7 5 3 2 0 1  6
        3  .4166667 1 6 4 3 3 0 1  4
        4  .5833333 1 9 6 3 2 0 1  8
        3  .3333333 0 7 4 2 2 0 1  4
        3  .5833333 1 9 8 2 2 0 3  8
        2 .16666667 0 7 7 4 2 0 1  7
        4  .3333333 1 9 7 4 2 0 1 10
        3  .4166667 1 9 6 2 2 0 1  9
        4  .5833333 1 9 8 3 3 . 1  7
        1  .4166667 0 9 5 3 3 0 2 10
        3  .6666667 1 7 5 4 2 1 1  8
        2  .5833333 0 7 3 4 2 0 1  4
        4  .5833333 0 7 5 4 2 0 1  7
        3  .5833333 1 4 4 4 2 0 1  8
        4 .16666667 0 7 4 4 2 0 1  9
        4  .5833333 1 7 5 3 2 1 3  5
        4        .5 0 5 3 4 2 0 1 10
        3       .75 1 7 3 4 3 0 1  8
        3  .3333333 1 7 4 4 2 . 1  7
        3  .3333333 0 7 4 4 2 0 1  8
        3        .5 1 9 5 4 2 0 1  9
        3        .5 0 9 4 3 2 0 3  3
        3        .5 1 7 3 3 3 0 1 10
        2        .5 0 9 3 3 2 0 1  6
        3        .5 0 7 6 4 2 0 1  8
        3        .5 0 7 6 4 3 . 1  9
        2        .5 1 7 5 3 2 0 1  9
        2  .5833333 1 5 5 4 2 0 1  6
        3  .6666667 1 9 5 4 2 0 1 10
        3  .6666667 1 7 5 4 2 1 1 10
        3  .5833333 1 7 5 4 2 0 1  8
        2  .5833333 1 7 5 4 2 0 1  5
        3        .5 0 7 4 4 2 0 1  9
        2        .5 0 7 4 4 3 1 1  9
        3        .5 0 5 6 4 2 0 1 10
        3        .5 0 8 5 4 2 0 1  8
        2        .5 1 9 5 4 2 0 1  9
        3        .5 1 7 5 4 2 0 1  5
        2        .5 1 5 5 3 2 0 1  7
        3        .5 1 7 5 4 2 0 1  6
        4  .4166667 0 8 5 2 2 0 2  5
        .       .25 0 9 8 3 2 0 3  5
        3        .5 1 7 5 3 2 0 1  9
        3        .5 1 5 5 4 2 0 1  9
        3        .5 0 7 6 3 2 0 1  9
        2        .5 0 7 5 3 2 0 1  6
        3        .5 1 7 3 3 2 . 3  5
        3  .5833333 1 5 4 4 3 0 3  9
        3        .5 0 5 5 4 2 0 1  5
        3        .5 1 7 4 3 2 0 1 10
        3  .5833333 1 7 4 4 3 0 1  5
        3        .5 0 7 4 3 2 0 1  7
        3  .4166667 1 9 6 1 2 0 3  7
        3        .5 0 5 5 2 3 0 1  7
        3  .6666667 0 9 8 3 3 0 1  7
        4  .9166667 1 9 5 3 2 0 2  5
        3  .6666667 0 9 8 1 2 0 1 10
        3       .75 0 9 8 1 2 0 1  9
        3  .6666667 0 9 4 4 3 0 1  7
        3  .4166667 1 7 4 4 2 0 1  1
        3  .6666667 1 9 8 3 3 0 1  8
        4  .3333333 1 7 6 2 3 0 1  4
        1  .3333333 1 5 5 2 2 0 1  6
        3 .08333334 0 7 6 3 3 1 1  9
        4  .6666667 1 9 6 2 3 0 1  6
        3  .3333333 1 9 5 3 2 0 1  5
        3  .6666667 1 6 3 2 2 0 1  7
        3         0 0 9 7 2 2 0 1  8
        3  .9166667 1 7 6 3 3 0 1  5
        3         0 0 7 7 3 3 1 1  5
        3        .5 1 9 8 3 3 1 1  6
        4  .3333333 1 7 6 2 3 0 1  5
        3  .5833333 0 9 5 3 2 1 1 10
        4  .5833333 1 9 6 3 2 0 1 10
        3  .6666667 1 9 7 1 2 0 1 10
        3       .25 1 9 7 4 3 0 1 10
        3  .6666667 0 9 7 3 2 0 1  8
        3 .16666667 1 9 7 3 3 1 2  7
        3       .25 1 9 7 1 3 0 1  6
        3       .75 1 9 6 1 2 0 1  4
        4  .3333333 1 9 8 3 3 0 1 10
        3  .9166667 0 9 5 4 2 1 1 10
        3 .16666667 0 9 7 2 3 1 3  6
        2  .3333333 0 9 5 3 2 1 1  8
        2       .75 1 9 6 2 2 0 1  8
        2  .4166667 0 5 7 3 2 1 1  9
        2        .5 0 5 6 3 2 0 1  9
        . .16666667 1 9 4 2 2 0 1  6
        1  .5833333 1 9 9 3 2 0 1  8
        3  .3333333 0 9 7 3 3 1 1  7
        3         0 1 7 6 2 2 0 1  8
        2       .75 1 5 7 2 3 0 1  9
        end
        label values V130 V130
        label def V130 1 "Very bad", modify
        label def V130 2 "Fairly bad", modify
        label def V130 3 "Fairly good", modify
        label def V130 4 "Very good", modify
        label values V240 V240
        label def V240 0 "Male", modify
        label def V240 1 "Female", modify
        label values V248 V248
        label def V248 4 "Incomplete secondary school: technical/ vocational type", modify
        label def V248 5 "Complete secondary school: technical/ vocational type", modify
        label def V248 6 "Incomplete secondary school: university-preparatory type", modify
        label def V248 7 "Complete secondary school: university-preparatory type", modify
        label values V239 V239
        label def V239 3 "Third step", modify
        label def V239 4 "Fourth step", modify
        label def V239 5 "Fifth step", modify
        label def V239 6 "Sixth step", modify
        label def V239 7 "Seventh step", modify
        label def V239 8 "Eigth step", modify
        label values V115 V115
        label def V115 1 "None at all", modify
        label def V115 2 "Not very much", modify
        label def V115 3 "Quite a lot", modify
        label def V115 4 "A great deal", modify
        label values V147 V147
        label def V147 2 "Not a religious person", modify
        label def V147 3 "A religious person", modify
        label values V24 V24
        label def V24 0 "no trust", modify
        label def V24 1 "trust", modify
        label values V85 V85
        label def V85 1 "Would never", modify
        label def V85 2 "Might do", modify
        label def V85 3 "Have done", modify
        label values V23 V23
        label def V23 1 "Completely dissatisfied", modify
        label def V23 3 "3", modify
        label def V23 4 "4", modify
        label def V23 5 "5", modify
        label def V23 6 "6", modify
        label def V23 7 "7", modify
        label def V23 8 "8", modify
        copy up to and including the previous line ------------------

        Listed 100 out of 11400 observations

        .

        Comment


        • #19
          That's what you meant, isn't it?

          Comment


          • #20
            Hanna:
            exactly!
            Very well done!
            Thanks for your efforts.
            Kind regards,
            Carlo
            (Stata 15.1 SE)

            Comment


            • #21
              Hanna:
              at a first glance, your regression suffers an excess of categorical predictors:
              Code:
              . regress V130 Womansright_index V240 V248 V239 V115 V147 V24 V85 V23
              
                    Source |       SS           df       MS      Number of obs   =        93
              -------------+----------------------------------   F(9, 83)        =      0.53
                     Model |  2.11822605         9   .23535845   Prob > F        =    0.8524
                  Residual |  37.1936019        83  .448115686   R-squared       =    0.0539
              -------------+----------------------------------   Adj R-squared   =   -0.0487
                     Total |   39.311828        92  .427302478   Root MSE        =    .66941
              
              -----------------------------------------------------------------------------------
                           V130 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
              ------------------+----------------------------------------------------------------
              Womansright_index |  -.1009144   .3927768    -0.26   0.798    -.8821316    .6803028
                           V240 |   .1016473   .1495953     0.68   0.499    -.1958917    .3991862
                           V248 |   .0837466    .054947     1.52   0.131    -.0255407    .1930339
                           V239 |  -.0358435   .0633795    -0.57   0.573    -.1619028    .0902157
                           V115 |   .0249463   .0918601     0.27   0.787    -.1577596    .2076523
                           V147 |   .1121565   .1581337     0.71   0.480     -.202365    .4266781
                            V24 |  -.1023259   .2083322    -0.49   0.625    -.5166903    .3120385
                            V85 |   .0955919    .133333     0.72   0.475    -.1696022    .3607859
                            V23 |  -.0062662   .0361575    -0.17   0.863    -.0781821    .0656496
                          _cons |   2.078786   .7702723     2.70   0.008     .5467454    3.610827
              -----------------------------------------------------------------------------------
              
              . estat ovtest
              
              Ramsey RESET test using powers of the fitted values of V130
                     Ho:  model has no omitted variables
                                F(3, 80) =      0.95
                                Prob > F =      0.4205
              
              . estat hettest
              
              Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
                       Ho: Constant variance
                       Variables: fitted values of V130
              
                       chi2(1)      =     0.05
                       Prob > chi2  =   0.8153
              
              . estat vif
              
                  Variable |       VIF       1/VIF 
              -------------+----------------------
                      V239 |      1.50    0.667012
                      V115 |      1.37    0.728434
                      V248 |      1.26    0.792934
                       V24 |      1.15    0.868171
              Womansrigh~x |      1.14    0.875247
                      V147 |      1.13    0.881785
                      V240 |      1.13    0.884258
                       V85 |      1.12    0.892015
                       V23 |      1.12    0.893010
              -------------+----------------------
                  Mean VIF |      1.21
              Actually, despite -regress postestimate- tests do not report any problem, the R-sq is simply too low (and non-significant) to make your model outperforming a mean estimate of the dependent variable:
              Code:
              . mean V130
              
              Mean estimation                   Number of obs   =         97
              
              --------------------------------------------------------------
                           |       Mean   Std. Err.     [95% Conf. Interval]
              -------------+------------------------------------------------
                      V130 |   2.927835   .0659445      2.796936    3.058734
              --------------------------------------------------------------
              Kind regards,
              Carlo
              (Stata 15.1 SE)

              Comment


              • #22
                Carlo Lazzaro Thanks a lot for your help!
                Can I just repeat if I understood it correct?
                So by too many categorical variables, you mean that I should probably take less control variables?
                R-squared is too low.. but that is not really something I can do about because it refers to the whole model, is that correct?
                About the two tests you did, that means that their aren't omitted variables and there is also no heteroskedasticity, right?
                Is there any possibility to improve my results?

                Comment


                • #23
                  I just compared your output with mine and I have different results.
                  Also for the first test (using the same command) stata displays this:
                  F(3, 5446) = 1.88
                  Prob > F = 0.1312

                  Comment


                  • #24
                    Hanna:
                    - the number of predictors to be included in the right-hand side of your regression equation should be consistent with the aim of giving a fair and true view of the data generating process;
                    - even in your original regression model the R-sq was low (and significance was due to the large sample size);
                    - as expected, re-running the post estimation tests on a larger sample size gives back different results (see and compare the degrees of freedom of the F-tests).
                    Kind regards,
                    Carlo
                    (Stata 15.1 SE)

                    Comment


                    • #25
                      Hi Hanna Hi ,

                      Sorry I am jumping into this late. I created a video on how to use dataex and code delimiters (obviously, you don't need to watch since you figured it out and successfully posted it here). I'm listing it here so future readers can find it. On YouTube it's at https://youtu.be/bXfaRCAOPbI

                      Comment

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