A problem about outputing the results of total, direct, and indirect effects after using a nonrecursive structural equation model

[Mindy Green]

Dear Stata users,

I recently performed a nonrecursive structural equation model in Stata and obtained the results successfully. However, I'm having trouble exporting the total, direct, and indirect effects into a Word document. The command I use is "estat teffects".

If any of you have experience with this or can offer any advice, I would be grateful for your help.

Thank you for your time.

Best regards,
Mindy

[Andrew Musau]

This may help: https://www.statalist.org/forums/for...egress-to-word

[Mindy Green]

Dear Andrew Musau ,

I would like to express my sincere gratitude for sharing your method with me. I have tried implementing it, and I have successfully obtained the desired results using your example. Your assistance has been immensely helpful.

However, I encountered a challenge when applying the method to my specific model. Upon using the "return list" command, the results I obtained were different, and I couldn't find the variables r(b) and r(V) in the output:

.         return list

scalars:
           r(N_groups) =  1

matrices:
            r(V_total) :  184 x 184
              r(total) :  1 x 184
         r(V_indirect) :  184 x 184
           r(indirect) :  1 x 184
           r(V_direct) :  184 x 184
             r(direct) :  1 x 184
               r(nobs) :  1 x 1

Here is the code I used:

sem ($y1 <-  $y2 $x1 $x5 $x7) ($y2 <- $y1 $x2 $x6 $x7)($y3 <-  $y4 $y1 $y2 $x1 $x5 $x7) ($y4 <- $y3 $y1 $y2 $x2 $x6 $x7), cov(e.$y1*e.$y2) cov(e.$y3*e.$y4) nocapslatent 
    //Total effects, Indirect effects and direct effects
    estat teffects
    return list
    mat b= r(total) 
    mat V= r(V_total) 
    foreach eq in direct indirect total{
    gen  `eq'= runiformint(1,185)
    }
    gen zero=0
    local vars
    forval i=1/30{
    local vars "`vars' zero"
    }
    sureg (total `vars', nocons)
    erepost b= b, rename
    erepost V=V, rename
    esttab, mlabel(none) se unstack nonumbers

And I met a problem:

.     erepost b= b, rename
conformability error

In light of this issue, I am seeking guidance on how to modify my command to obtain the desired output. It is worth mentioning that in my model, I have four equations, and they have a different number of explanatory variables. Specifically, two equations have 28 explanatory variables, while the other two have 30 explanatory variables. Could you kindly advise me on how to properly set the number in the loop to accommodate these variations?

Thank you very much for your time and support. I greatly appreciate your expertise and guidance.

Best regards,
Mindy

[Mindy Green]

Dear Andrew Musau ,

Thank you so much for your invaluable advice. I have identified the issue where I mistakenly set the wrong number in the loop, and as a result, I have successfully obtained the desired results. I cannot express enough gratitude for your assistance.

Now, I am facing another challenge. Even after applying the structural equation model with standardization, the results I obtain are still the pre-standardization results. I would like to output the results after standardization. Could you please guide me on how to achieve this? Below is the code I have been using:

sem ($y1 <-  $y2 $x1 $x5 $x7) ($y2 <- $y1 $x2 $x6 $x7)($y3 <-  $y4 $y1 $y2 $x1 $x5 $x7) ($y4 <- $y3 $y1 $y2 $x2 $x6 $x7), cov(e.$y1*e.$y2) cov(e.$y3*e.$y4) nocapslatent standardized

Once again, I deeply appreciate your generous support. Your expertise and willingness to assist have been immensely valuable to me. I eagerly look forward to your guidance on obtaining the standardized results.

Best regards,
Mindy

[Andrew Musau]

Thanks. For something similar to the results table with stars, consider:

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(LS_w LS_h SWB_w SWB_h Trust_w Trust2_w Trust3_w Trust_h Trust2_h Trust3_h) double Age_w float(Edu_year_w Ethnicity_w Health_w Pension_w) double Age_h float(Edu_year_h Ethnicity_h Pension_h Health_h Familysize Children_ratio Business Debt)
4.3333335         5 4.1 4.1  3.333333 4.5 4.5 4.3333335   4 4.5 49 12 1 1 1 52 12 1 1 1 3         0 0 0
        4 4.3333335 4.1 4.3  3.333333 2.5 3.5 4.3333335   4 4.5 27 16 1 1 1 29 16 1 1 0 2         0 0 1
 3.666667         5 4.8 4.8  3.333333   4   4         2   4   4 28 12 1 1 1 37 11 1 1 0 2  .3333333 0 0
 3.666667 4.3333335 3.8 4.8         4 4.5   4 4.3333335   5 4.5 25 16 1 0 1 27 16 1 1 1 4         0 0 1
        5         5   5 4.1  3.333333   4   5  3.333333 4.5   5 52 12 1 1 1 51 15 1 1 1 4         0 0 1
4.6666665 4.6666665 4.1 4.1         4   4   4         3   4   4 48 15 1 1 1 50 12 1 1 1 3         0 0 0
 2.666667         4   3 4.3  3.666667   3   4         4   4   4 42 12 1 1 1 44 12 1 1 1 3       .25 0 0
        5         5   5 4.5  3.333333   4   5         4   4   4 50 12 1 0 1 53 15 1 1 1 3         0 0 0
        5         5   5 4.5  3.333333   4   4  3.666667 4.5   5 45 12 1 1 1 58 12 1 1 1 6         0 0 0
4.3333335         5   5 3.5  3.333333   4   4  2.666667 4.5   4 37 12 1 1 1 38 12 1 1 1 3         0 0 0
4.6666665         5 4.3   3  3.333333 4.5 4.5         4 4.5 4.5 57 12 1 0 1 58 12 1 1 0 3         0 0 0
 3.666667         5 3.9 3.2         4 4.5   4  3.666667   4   4 30 15 1 1 1 32 15 1 1 1 5 .16666667 0 0
 3.666667         5   5 4.5  2.666667 4.5 4.5         4   4 4.5 44 15 1 1 1 49  9 1 1 1 5         0 0 0
 3.666667  3.666667 4.8   5         4   4   4         3 3.5   4 39 15 1 1 1 40 12 1 1 1 4        .2 0 0
4.3333335         5 4.1 4.3  2.666667 4.5   5 4.6666665 4.5   5 34 15 1 1 1 35 15 1 1 1 4        .2 0 0
        4         5 4.6 4.8         2   4   4 4.3333335   5 3.5 34 16 1 1 1 38 16 1 1 1 2  .3333333 0 1
        3         5 4.1 4.1         3   4   4  3.333333   3   4 46 12 0 1 1 48 16 0 1 1 3         0 0 0
        4         4 4.1   2  3.333333   3   4  3.333333 3.5 4.5 36  6 1 0 1 40  9 1 1 1 4  .3333333 0 0
        4         5   5 4.5 4.3333335 4.5   5  3.666667   4   4 32  0 1 1 1 32  9 1 1 1 2        .5 1 0
        3         5 4.1 3.9  3.666667   3 4.5  3.666667 4.5   4 30 16 1 1 1 34 16 1 1 0 2        .5 0 1
1.6666666 4.6666665   3   5 4.3333335   4   3 4.6666665 4.5 4.5 39 12 1 1 1 43  7 1 1 1 2        .5 1 1
        5         5   5   5         4 4.5 4.5         4 4.5   4 53  9 1 1 1 54 16 1 1 1 2         0 0 1
 3.666667         4 3.9 3.9         3   4   4         4   4 4.5 45 16 1 1 1 54 15 1 1 1 3         0 0 0
 3.666667         5 4.1   5         4   4   3  3.333333   4   4 37 16 1 1 1 38 15 1 1 1 2  .3333333 1 1
        3         5 3.2 4.3 4.3333335 4.5   5         4   4   4 41 19 1 1 1 40 15 1 1 1 5         0 0 1
 3.666667         5 3.9 2.9         4 4.5   4         2   4   4 54 12 1 1 1 53 15 1 1 1 2         0 1 0
        5 4.3333335 2.2 4.1         4   4   4  3.333333 4.5   5 42 16 1 1 1 48 15 1 1 1 2  .3333333 1 0
        3         4 4.2   3         2   4   4         2   3   4 46 15 1 1 1 46 15 1 1 1 3         0 0 0
        4 4.3333335 3.6 4.2 4.3333335   4   3  2.666667 3.5   3 35 12 1 1 0 38 12 0 1 1 2  .3333333 1 0
4.3333335         4 4.1 3.6         4   4   4         4   4   4 41  9 1 1 1 46  9 1 1 1 3         0 1 1
        4  3.666667 3.6   2         3   4   4  3.666667   4   4 35 15 1 1 1 36 15 1 1 1 3        .4 0 0
        4         5 3.7   3         4   4 3.5  3.333333   4   5 47  6 1 1 0 52  9 1 1 1 2         0 1 1
4.3333335         5 4.1   5  3.333333   4   4  2.666667   4   4 48 16 1 1 1 51  9 1 1 1 2         0 0 0
4.6666665         5 4.1   5  2.666667   4   4         4   4 4.5 27 16 1 1 1 27 16 1 1 1 2         0 0 0
        5         5 3.4 4.1  2.666667 3.5 4.5  3.333333   4   4 56  8 1 0 1 56  9 1 1 1 3         0 0 0
4.6666665 4.3333335   5 3.9         2   4   4         4 4.5   4 40 16 1 1 1 38 16 1 1 1 3       .25 0 1
 3.666667         5 4.1   4         2   4 4.5  3.333333 3.5 3.5 43 12 1 1 1 45 12 1 1 1 3         0 0 1
        5 4.3333335   5 4.2  3.666667   4   5  3.666667   4   4 55  9 1 0 0 53 12 1 1 1 3         0 0 1
1.3333334         4 1.9   4         3   5   5         1 1.5   1 50  6 1 0 1 51  0 1 0 0 3       .25 0 0
 2.666667         5   3   3 4.3333335   5 4.5         4   4   4 52  9 1 0 1 51  8 1 0 1 3         0 0 1
2.3333333         5   3   3 1.6666666 3.5 3.5 1.3333334   4   4 55  9 1 1 1 55  9 1 0 0 3         0 0 0
1.3333334         5 4.1 4.1         4 4.5   5 2.3333333   4   4 44  9 1 1 1 44  9 1 0 1 5 .16666667 0 0
        5         5 4.6   5         2   4   4  3.666667 4.5   4 37  9 1 1 1 38 12 1 1 1 3       .25 0 0
4.3333335         4 4.1 3.4         3   4   4  3.333333   4   4 30 15 1 1 1 30 15 1 1 1 4  .3333333 0 1
 3.666667         5 4.1   5         4   5 4.5         4 4.5   4 53  9 1 0 1 52  0 1 1 1 6         0 0 1
 3.333333         5 3.7 4.1 1.3333334   4   3         4   3   5 54  9 1 1 1 54  9 1 1 1 7         0 0 0
        4 4.3333335 4.1 4.6  3.666667   4 4.5         4   4 4.5 28 15 1 1 1 29 12 1 1 1 5  .2857143 0 0
        5         4 3.6 4.1         3 3.5 3.5         4   4   4 42  9 1 1 1 43  9 1 0 0 3       .25 1 0
 3.666667         5 4.5   5  2.666667 4.5 4.5         3   3   3 38  6 1 1 1 40 15 1 1 1 5 .16666667 0 0
        4  3.666667 3.7 4.1  3.666667 3.5 4.5         3   3   3 41 15 1 1 1 47 12 1 0 0 2  .3333333 1 1
end
label values Age_w age
label values Age_h age

global y1 LS_w
    global y2 LS_h
    global y3 SWB_w
    global y4 SWB_h
    global x1 Trust_w Trust2_w Trust3_w
    global x2 Trust_h Trust2_h Trust3_h
    global x3 Age_w Edu_year_w Ethnicity_w Health_w Pension_w Health_w 
    global x4 Age_h Edu_year_h Ethnicity_h  Health_w Pension_h Health_h
    global x5  Familysize Children_ratio  Business Debt 
   *I CURTAIL THE NO. OF ITERATIONS AS I AM JUST ILLUSTRATING TECHNIQUE  
   sem ($y1 <-  $y2 $x1 $x3 $x5) ($y2 <- $y1 $x2 $x4 $x5)($y3 <-  $y4 $y1 $y2 $x1 $x3 $x5) ($y4 <- $y3 $y1 $y2 $x2 $x4 $x5), ///
   cov(e.$y1*e.$y2) cov(e.$y3*e.$y4) nocapslatent standardized iterate(20) nolog
    estadd mat b_st= r(table)["b", 1...]
    estadd mat se_st= r(table)["se", 1...]
    estadd mat p_st= r(table)["pvalue", 1...]
    esttab, cells("b_st(star) se_st") p(p_st) starlevels(* 0.1 ** 0.05 *** 0.01) label ///
    nonumbers collab(Coef. SE, lhs(Variables))

Res.:

.    *I CURTAIL THE NO. OF ITERATIONS AS I AM JUST ILLUSTRATING TECHNIQUE  
.    sem ($y1 <-  $y2 $x1 $x3 $x5) ($y2 <- $y1 $x2 $x4 $x5)($y3 <-  $y4 $y1 $y2 $x1 $x3 $x5) ($y4 <- $y3 $y1 $y2 $x2 $x4 $x5), ///
>    cov(e.$y1*e.$y2) cov(e.$y3*e.$y4) nocapslatent standardized iterate(20) nolog

Endogenous variables

Observed:  LS_w LS_h SWB_w SWB_h

Exogenous variables

Observed:  Trust_w Trust2_w Trust3_w Age_w Edu_year_w Ethnicity_w Health_w Pension_w Familysize Children_ratio Business Debt Trust_h
           Trust2_h Trust3_h Age_h Edu_year_h Ethnicity_h Pension_h Health_h
convergence not achieved

Structural equation model                       Number of obs     =         50
Estimation method  = ml
Log likelihood     = -953.00081

-------------------------------------------------------------------------------------
                    |                 OIM
       Standardized |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
Structural          |
  LS_w              |
               LS_h |   1.009953   .2346061     4.30   0.000     .5501338    1.469773
            Trust_w |  -.3313821   .1777661    -1.86   0.062    -.6797972     .017033
           Trust2_w |  -.4192148    .405344    -1.03   0.301    -1.213674    .3752448
           Trust3_w |   .3978032   .1838001     2.16   0.030     .0375616    .7580449
              Age_w |  -.2296086    .212886    -1.08   0.281    -.6468574    .1876403
         Edu_year_w |   .1021706   .3725697     0.27   0.784    -.6280525    .8323937
        Ethnicity_w |   .1556869   .1605037     0.97   0.332    -.1588946    .4702685
           Health_w |  -.2387249   .2865192    -0.83   0.405    -.8002923    .3228425
          Pension_w |  -.1325372   .2055096    -0.64   0.519    -.5353286    .2702543
         Familysize |   .0085769   .2362815     0.04   0.971    -.4545264    .4716801
     Children_ratio |  -.1502032   .2214452    -0.68   0.498    -.5842278    .2838215
           Business |   .5615699   .2953701     1.90   0.057    -.0173449    1.140485
               Debt |   -.117186   .2201968    -0.53   0.595    -.5487637    .3143917
              _cons |  -2.951638   .3597834    -8.20   0.000    -3.656801   -2.246476
  ------------------+----------------------------------------------------------------
  LS_h              |
               LS_w |  -.2214619   .6076643    -0.36   0.716    -1.412462    .9695383
           Health_w |   .0609303   .1370167     0.44   0.657    -.2076174    .3294781
         Familysize |   .0096486   .1404336     0.07   0.945    -.2655962    .2848935
     Children_ratio |   -.296056   .1578451    -1.88   0.061    -.6054267    .0133146
           Business |  -.0207819   .1799656    -0.12   0.908     -.373508    .3319443
               Debt |  -.1125479   .1554002    -0.72   0.469    -.4171267    .1920309
            Trust_h |    .015952   .1323138     0.12   0.904    -.2433783    .2752823
           Trust2_h |   .4710699   .2601476     1.81   0.070      -.03881    .9809498
           Trust3_h |  -.0915595   .1627565    -0.56   0.574    -.4105563    .2274372
              Age_h |   .1159197   .1725456     0.67   0.502    -.2222634    .4541029
         Edu_year_h |  -.0091518    .137993    -0.07   0.947    -.2796132    .2613095
        Ethnicity_h |  -.0949503   .1319395    -0.72   0.472     -.353547    .1636465
          Pension_h |   .2516495   .1599626     1.57   0.116    -.0618715    .5651705
           Health_h |   .0009631   .1172808     0.01   0.993    -.2289029    .2308292
              _cons |   7.453336   .7864925     9.48   0.000     5.911839    8.994833
  ------------------+----------------------------------------------------------------
  SWB_w             |
               LS_w |   .5656112    .111932     5.05   0.000     .3462286    .7849938
               LS_h |   .2162904    .201821     1.07   0.284    -.1792714    .6118523
              SWB_h |  -.0708946   .3258502    -0.22   0.828    -.7095493    .5677601
            Trust_w |   .0953484   .1327466     0.72   0.473      -.16483    .3555269
           Trust2_w |   .0572644   .1444744     0.40   0.692    -.2259003    .3404291
           Trust3_w |    .075877   .1358751     0.56   0.577    -.1904333    .3421873
              Age_w |  -.1443707   .1484035    -0.97   0.331    -.4352363    .1464948
         Edu_year_w |  -.2278452   .1791322    -1.27   0.203    -.5789379    .1232475
        Ethnicity_w |   -.056593   .1194499    -0.47   0.636    -.2907106    .1775245
           Health_w |   .3316282   .1673781     1.98   0.048     .0035732    .6596832
          Pension_w |  -.0338484   .1224168    -0.28   0.782    -.2737809    .2060842
         Familysize |   .0637285   .1273846     0.50   0.617    -.1859409    .3133978
     Children_ratio |   .0407367   .1433636     0.28   0.776    -.2402508    .3217241
           Business |  -.3992411    .192452    -2.07   0.038    -.7764401   -.0220422
               Debt |    .167144   .1172722     1.43   0.154    -.0627053    .3969933
              _cons |   1.235715   .1198714    10.31   0.000     1.000771    1.470658
  ------------------+----------------------------------------------------------------
  SWB_h             |
               LS_w |  -.1345691    .819081    -0.16   0.870    -1.739938      1.4708
               LS_h |     .00437   .4837806     0.01   0.993    -.9438226    .9525626
              SWB_w |   .2889209   1.330895     0.22   0.828    -2.319586    2.897428
           Health_w |   .2582466   .3145667     0.82   0.412    -.3582927     .874786
         Familysize |   .0142046   .1940445     0.07   0.942    -.3661157    .3945248
     Children_ratio |  -.1879232   .2323377    -0.81   0.419    -.6432967    .2674504
           Business |   .0738629   .4581669     0.16   0.872    -.8241276    .9718535
               Debt |  -.0923653   .2715063    -0.34   0.734    -.6245078    .4397772
            Trust_h |   .3926569   .1815229     2.16   0.031     .0368786    .7484352
           Trust2_h |   .2920772   .2362108     1.24   0.216    -.1708875    .7550419
           Trust3_h |  -.5613457   .1918513    -2.93   0.003    -.9373674    -.185324
              Age_h |  -.0835399   .2203976    -0.38   0.705    -.5155112    .3484315
         Edu_year_h |  -.2585548   .1947699    -1.33   0.184    -.6402968    .1231872
        Ethnicity_h |   -.037613   .1408687    -0.27   0.789    -.3137106    .2384845
          Pension_h |   .1746767   .1969367     0.89   0.375    -.2113122    .5606656
           Health_h |  -.0599908   .1808622    -0.33   0.740    -.4144742    .2944926
              _cons |   4.873628   2.157287     2.26   0.024      .645424    9.101833
--------------------+----------------------------------------------------------------
         var(e.LS_w)|   1.515948    .495225                      .7991282    2.875756
         var(e.LS_h)|   .6813143   .0938633                      .5200907    .8925157
        var(e.SWB_w)|   .5249118   .1134978                      .3435875    .8019279
        var(e.SWB_h)|   .7289291   .2684014                      .3542106    1.500061
--------------------+----------------------------------------------------------------
  cov(e.LS_w,e.LS_h)|  -.6467363   .4597867    -1.41   0.160    -1.547902    .2544291
cov(e.SWB_w,e.SWB_h)|  -.0444024   1.034903    -0.04   0.966    -2.072776    1.983971
-------------------------------------------------------------------------------------
Note: The LR test of model vs. saturated is not reported because the fitted
      model is not full rank.
Warning: convergence not achieved

.     estadd mat b_st= r(table)["b", 1...]

added matrix:
               e(b_st) :  1 x 68

.     estadd mat se_st= r(table)["se", 1...]

added matrix:
              e(se_st) :  1 x 68

.     estadd mat p_st= r(table)["pvalue", 1...]

added matrix:
               e(p_st) :  1 x 68

.     esttab, cells("b_st(star) se_st") p(p_st) starlevels(* 0.1 ** 0.05 *** 0.01) label ///
>     nonumbers collab(Coef. SE, lhs(Variables))

-------------------------------------------------
                                                 
Variables                   Coef.              SE
-------------------------------------------------
LS_w                                             
LS_h                     1.009953***     .2346061
Trust_w                 -.3313821*       .1777661
Trust2_w                -.4192148         .405344
Trust3_w                 .3978032*       .1838001
Age_w                   -.2296086         .212886
Edu_year_w               .1021706        .3725697
Ethnicity_w              .1556869        .1605037
Health_w                -.2387249        .2865192
Pension_w               -.1325372        .2055096
Familysize               .0085769        .2362815
Children_ratio          -.1502032        .2214452
Business                 .5615699*       .2953701
Debt                     -.117186        .2201968
Constant                -2.951638        .3597834
-------------------------------------------------
LS_h                                             
LS_w                    -.2214619        .6076643
Health_w                 .0609303        .1370167
Familysize               .0096486        .1404336
Children_ratio           -.296056*       .1578451
Business                -.0207819        .1799656
Debt                    -.1125479        .1554002
Trust_h                   .015952        .1323138
Trust2_h                 .4710699*       .2601476
Trust3_h                -.0915595        .1627565
Age_h                    .1159197        .1725456
Edu_year_h              -.0091518         .137993
Ethnicity_h             -.0949503        .1319395
Pension_h                .2516495        .1599626
Health_h                 .0009631        .1172808
Constant                 7.453336        .7864925
-------------------------------------------------
SWB_w                                            
LS_w                     .5656112***      .111932
LS_h                     .2162904         .201821
SWB_h                   -.0708946        .3258502
Trust_w                  .0953484        .1327466
Trust2_w                 .0572644        .1444744
Trust3_w                  .075877        .1358751
Age_w                   -.1443707        .1484035
Edu_year_w              -.2278452        .1791322
Ethnicity_w              -.056593        .1194499
Health_w                 .3316282**      .1673781
Pension_w               -.0338484        .1224168
Familysize               .0637285        .1273846
Children_ratio           .0407367        .1433636
Business                -.3992411**       .192452
Debt                      .167144        .1172722
Constant                 1.235715        .1198714
-------------------------------------------------
SWB_h                                            
LS_w                    -.1345691         .819081
LS_h                       .00437        .4837806
SWB_w                    .2889209        1.330895
Health_w                 .2582466        .3145667
Familysize               .0142046        .1940445
Children_ratio          -.1879232        .2323377
Business                 .0738629        .4581669
Debt                    -.0923653        .2715063
Trust_h                  .3926569**      .1815229
Trust2_h                 .2920772        .2362108
Trust3_h                -.5613457***     .1918513
Age_h                   -.0835399        .2203976
Edu_year_h              -.2585548        .1947699
Ethnicity_h              -.037613        .1408687
Pension_h                .1746767        .1969367
Health_h                -.0599908        .1808622
Constant                 4.873628**      2.157287
-------------------------------------------------
/                                                
var(e.LS_w)              1.515948***      .495225
var(e.LS_h)              .6813143***     .0938633
var(e.SWB_w)             .5249118***     .1134978
var(e.SWB_h)             .7289291**      .2684014
cov(e.LS_w,e.LS_h)      -.6467363        .4597867
cov(e.SWB_w,e.SWB_h)    -.0444024        1.034903
-------------------------------------------------
Observations                   50                
-------------------------------------------------

. 
end of do-file

.

[Mindy Green]

Dear Andrew Musau ,

I am extremely grateful for the code you provided, which is incredibly helpful in addressing my problem.

I also wanted to extend my appreciation for introducing me to the dataex command. Thanks to your guidance, I have learned how to utilize it effectively to present my questions more clearly. It has proven to be an incredibly useful tool and I will continue to use it.

Your kindness and support have made a significant impact on my study and work. And I am truly grateful for it.

Warmest regards,
Mindy