Regression Ouputs von "Was Deutschland über Algorithmen und Künstliche Intelligenz weiß und denkt - Ergebnisse einer repräsentativen Bevölkerungsumfrage: Update 2022". Markus Overdiek, Bertelsmann Stiftung

Unten im Dokument finden sich Erklärungen zu den Variablen. 
-------------------------------------------------------------------------------------------------------
. mlogit V01 S01 S02 S03 S10 S13, baseoutcome(4)

Iteration 0:   log likelihood = -1248.7825  
Iteration 1:   log likelihood = -1114.6251  
Iteration 2:   log likelihood = -1101.1746  
Iteration 3:   log likelihood = -1101.0685  
Iteration 4:   log likelihood = -1101.0685  

Multinomial logistic regression                   Number of obs   =        996
                                                  LR chi2(15)     =     295.43
                                                  Prob > chi2     =     0.0000
Log likelihood = -1101.0685                       Pseudo R2       =     0.1183

------------------------------------------------------------------------------
         V01 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
         S01 |  -.7659692    .135333    -5.66   0.000    -1.031217   -.5007215
         S02 |  -.0375056   .0075518    -4.97   0.000    -.0523068   -.0227043
         S03 |   .9986029   .1086908     9.19   0.000     .7855729    1.211633
         S10 |   .1765082   .0449079     3.93   0.000     .0884902    .2645261
         S13 |  -.0548774    .060367    -0.91   0.363    -.1731947    .0634398
       _cons |   -3.17961   .7626649    -4.17   0.000    -4.674405   -1.684814
-------------+----------------------------------------------------------------
2            |
         S01 |  -.3449983   .1048516    -3.29   0.001    -.5505036   -.1394931
         S02 |  -.0186739   .0059398    -3.14   0.002    -.0303157   -.0070321
         S03 |   .6533762   .0906036     7.21   0.000     .4757964    .8309561
         S10 |   .1260136   .0359455     3.51   0.000     .0555617    .1964656
         S13 |  -.0192713   .0487014    -0.40   0.692    -.1147242    .0761816
       _cons |  -.9796727   .6241704    -1.57   0.117    -2.203024    .2436787
-------------+----------------------------------------------------------------
3            |
         S01 |  -.0943377   .1135982    -0.83   0.406    -.3169861    .1283108
         S02 |  -.0075119   .0064633    -1.16   0.245    -.0201798     .005156
         S03 |   .2606462   .0987613     2.64   0.008     .0670776    .4542147
         S10 |    .104068   .0390186     2.67   0.008      .027593     .180543
         S13 |  -.0073534   .0524738    -0.14   0.889    -.1102002    .0954934
       _cons |  -.8513702   .6841944    -1.24   0.213    -2.192367    .4896262
-------------+----------------------------------------------------------------
4            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_01a V04_01a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -853.01928  
Iteration 1:   log likelihood = -790.77008  
Iteration 2:   log likelihood = -787.76567  
Iteration 3:   log likelihood = -787.75647  
Iteration 4:   log likelihood = -787.75647  

Multinomial logistic regression                   Number of obs   =        970
                                                  LR chi2(12)     =     130.53
                                                  Prob > chi2     =     0.0000
Log likelihood = -787.75647                       Pseudo R2       =     0.0765

------------------------------------------------------------------------------
     V05_01a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_01a |  -1.981096   .2680734    -7.39   0.000     -2.50651   -1.455682
         S01 |  -.0384013   .1192946    -0.32   0.748    -.2722143    .1954117
         S02 |  -.0110145    .006744    -1.63   0.102    -.0242325    .0022035
         S03 |   .1118402   .0850987     1.31   0.189    -.0549501    .2786305
         S10 |   .0212948   .0400385     0.53   0.595    -.0571792    .0997689
         S13 |   .1066421   .0558284     1.91   0.056    -.0027796    .2160638
       _cons |   1.780016   .8104197     2.20   0.028     .1916224    3.368409
-------------+----------------------------------------------------------------
2            |
     V04_01a |  -1.425707   .1767707    -8.07   0.000    -1.772171   -1.079242
         S01 |  -.0567744   .0850323    -0.67   0.504    -.2234347    .1098859
         S02 |  -.0128771   .0048111    -2.68   0.007    -.0223067   -.0034475
         S03 |   .1000214    .061488     1.63   0.104    -.0204928    .2205357
         S10 |   .0324101   .0289567     1.12   0.263     -.024344    .0891643
         S13 |   .0174587    .039164     0.45   0.656    -.0593014    .0942187
       _cons |   3.168529   .5902773     5.37   0.000     2.011607    4.325452
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_02a V04_02a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -998.27405  
Iteration 1:   log likelihood = -930.31568  
Iteration 2:   log likelihood = -927.54365  
Iteration 3:   log likelihood = -927.53937  
Iteration 4:   log likelihood = -927.53937  

Multinomial logistic regression                   Number of obs   =        937
                                                  LR chi2(12)     =     141.47
                                                  Prob > chi2     =     0.0000
Log likelihood = -927.53937                       Pseudo R2       =     0.0709

------------------------------------------------------------------------------
     V05_02a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_02a |  -2.023992   .2129348    -9.51   0.000    -2.441337   -1.606647
         S01 |   .0125589   .0964985     0.13   0.896    -.1765747    .2016924
         S02 |  -.0183566   .0055501    -3.31   0.001    -.0292345   -.0074786
         S03 |   .0058544   .0685294     0.09   0.932    -.1284608    .1401696
         S10 |   .0117263    .033191     0.35   0.724    -.0533269    .0767796
         S13 |   .0328743   .0446593     0.74   0.462    -.0546563    .1204048
       _cons |   3.726603    .650883     5.73   0.000     2.450896     5.00231
-------------+----------------------------------------------------------------
2            |
     V04_02a |  -1.206377   .1784568    -6.76   0.000    -1.556146   -.8566084
         S01 |   .0695828   .0864774     0.80   0.421    -.0999098    .2390753
         S02 |  -.0122878   .0049633    -2.48   0.013    -.0220156     -.00256
         S03 |   .0537864   .0609391     0.88   0.377    -.0656521     .173225
         S10 |    .003578   .0297824     0.12   0.904    -.0547945    .0619505
         S13 |   .0049928   .0400262     0.12   0.901    -.0734572    .0834427
       _cons |   2.820083   .5881547     4.79   0.000     1.667321    3.972845
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_03a V04_03a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -844.22681  
Iteration 1:   log likelihood = -799.43971  
Iteration 2:   log likelihood = -798.72998  
Iteration 3:   log likelihood = -798.72925  
Iteration 4:   log likelihood = -798.72925  

Multinomial logistic regression                   Number of obs   =        965
                                                  LR chi2(12)     =      91.00
                                                  Prob > chi2     =     0.0000
Log likelihood = -798.72925                       Pseudo R2       =     0.0539

------------------------------------------------------------------------------
     V05_03a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_03a |   -1.43033   .2888524    -4.95   0.000     -1.99647   -.8641894
         S01 |  -.2728622   .1418477    -1.92   0.054    -.5508786    .0051543
         S02 |  -.0077569   .0077702    -1.00   0.318    -.0229863    .0074725
         S03 |  -.0306231   .0980504    -0.31   0.755    -.2227984    .1615522
         S10 |   .0554496   .0460932     1.20   0.229    -.0348914    .1457906
         S13 |   .1389786   .0657322     2.11   0.034     .0101459    .2678112
       _cons |  -.2334588   .8871535    -0.26   0.792    -1.972248     1.50533
-------------+----------------------------------------------------------------
2            |
     V04_03a |  -1.005493   .1492024    -6.74   0.000    -1.297925   -.7130622
         S01 |   .0208063   .0710374     0.29   0.770    -.1184245     .160037
         S02 |  -.0025723   .0039803    -0.65   0.518    -.0103735    .0052288
         S03 |   .0071322   .0502278     0.14   0.887    -.0913125    .1055769
         S10 |   .0899889    .024179     3.72   0.000      .042599    .1373788
         S13 |   .0163112   .0329718     0.49   0.621    -.0483124    .0809349
       _cons |   1.192361   .4778471     2.50   0.013     .2557975    2.128924
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_04a V04_04a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -946.69876  
Iteration 1:   log likelihood = -887.69753  
Iteration 2:   log likelihood = -885.63357  
Iteration 3:   log likelihood = -885.63031  
Iteration 4:   log likelihood = -885.63031  

Multinomial logistic regression                   Number of obs   =        937
                                                  LR chi2(12)     =     122.14
                                                  Prob > chi2     =     0.0000
Log likelihood = -885.63031                       Pseudo R2       =     0.0645

------------------------------------------------------------------------------
     V05_04a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_04a |  -1.803136   .2147583    -8.40   0.000    -2.224055   -1.382218
         S01 |  -.0239895   .1025205    -0.23   0.815     -.224926    .1769471
         S02 |   -.012861   .0057589    -2.23   0.026    -.0241481   -.0015738
         S03 |   .0436549   .0729563     0.60   0.550    -.0993369    .1866467
         S10 |   .0508663   .0349948     1.45   0.146    -.0177223    .1194549
         S13 |   .0175867   .0471059     0.37   0.709    -.0747391    .1099125
       _cons |   3.593007   .6905265     5.20   0.000       2.2396    4.946414
-------------+----------------------------------------------------------------
2            |
     V04_04a |  -.8898832    .210531    -4.23   0.000    -1.302516   -.4772501
         S01 |  -.1146107   .1021686    -1.12   0.262    -.3148574    .0856361
         S02 |  -.0018649   .0057198    -0.33   0.744    -.0130755    .0093457
         S03 |   .0312317   .0726379     0.43   0.667     -.111136    .1735993
         S10 |   .0015822   .0348283     0.05   0.964    -.0666799    .0698443
         S13 |   .0410391   .0469578     0.87   0.382    -.0509965    .1330747
       _cons |   1.990902   .6921041     2.88   0.004     .6344029    3.347401
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_05a V04_05a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -862.04391  
Iteration 1:   log likelihood = -807.58564  
Iteration 2:   log likelihood =  -805.9203  
Iteration 3:   log likelihood = -805.91765  
Iteration 4:   log likelihood = -805.91765  

Multinomial logistic regression                   Number of obs   =        927
                                                  LR chi2(12)     =     112.25
                                                  Prob > chi2     =     0.0000
Log likelihood = -805.91765                       Pseudo R2       =     0.0651

------------------------------------------------------------------------------
     V05_05a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_05a |  -1.794504   .2182584    -8.22   0.000    -2.222283   -1.366726
         S01 |  -.0736034   .1028028    -0.72   0.474    -.2750931    .1278863
         S02 |  -.0046453   .0057877    -0.80   0.422     -.015989    .0066983
         S03 |  -.0228141   .0733946    -0.31   0.756    -.1666649    .1210367
         S10 |   .0252084   .0350016     0.72   0.471    -.0433936    .0938103
         S13 |   .0720224   .0474478     1.52   0.129    -.0209735    .1650183
       _cons |   3.607323   .6956372     5.19   0.000     2.243899    4.970747
-------------+----------------------------------------------------------------
2            |
     V04_05a |  -.7325152   .2318108    -3.16   0.002    -1.186856   -.2781744
         S01 |  -.0998283   .1111689    -0.90   0.369    -.3177153    .1180588
         S02 |   .0019621   .0062361     0.31   0.753    -.0102604    .0141845
         S03 |  -.0089851   .0793313    -0.11   0.910    -.1644717    .1465015
         S10 |  -.0436553   .0375717    -1.16   0.245    -.1172945    .0299839
         S13 |   .0485921   .0513037     0.95   0.344    -.0519614    .1491455
       _cons |   1.760814   .7531496     2.34   0.019      .284668     3.23696
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_06a V04_06a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -782.83665  
Iteration 1:   log likelihood = -720.29253  
Iteration 2:   log likelihood = -719.36362  
Iteration 3:   log likelihood = -719.36261  
Iteration 4:   log likelihood = -719.36261  

Multinomial logistic regression                   Number of obs   =        964
                                                  LR chi2(12)     =     126.95
                                                  Prob > chi2     =     0.0000
Log likelihood = -719.36261                       Pseudo R2       =     0.0811

------------------------------------------------------------------------------
     V05_06a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_06a |  -1.689826   .3612787    -4.68   0.000    -2.397919   -.9817327
         S01 |  -.1401281   .1774839    -0.79   0.430    -.4879902     .207734
         S02 |   .0014007   .0096272     0.15   0.884    -.0174682    .0202696
         S03 |   .2524467   .1217409     2.07   0.038     .0138389    .4910545
         S10 |  -.0941808    .056665    -1.66   0.097    -.2052422    .0168807
         S13 |   .0396968   .0831896     0.48   0.633    -.1233519    .2027454
       _cons |   .1445975   1.132574     0.13   0.898    -2.075207    2.364402
-------------+----------------------------------------------------------------
2            |
     V04_06a |  -1.414099   .1674716    -8.44   0.000    -1.742337   -1.085861
         S01 |   .0061226   .0715584     0.09   0.932    -.1341292    .1463743
         S02 |  -.0008183   .0040262    -0.20   0.839    -.0087095    .0070728
         S03 |   .2445405    .051291     4.77   0.000     .1440119    .3450691
         S10 |  -.0089737   .0244517    -0.37   0.714    -.0568981    .0389507
         S13 |  -.0791341   .0333652    -2.37   0.018    -.1445286   -.0137395
       _cons |   2.391156   .5093539     4.69   0.000     1.392841    3.389472
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_07a V04_07a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -887.59737  
Iteration 1:   log likelihood =  -842.3377  
Iteration 2:   log likelihood = -837.01516  
Iteration 3:   log likelihood = -837.00647  
Iteration 4:   log likelihood = -837.00647  

Multinomial logistic regression                   Number of obs   =        964
                                                  LR chi2(12)     =     101.18
                                                  Prob > chi2     =     0.0000
Log likelihood = -837.00647                       Pseudo R2       =     0.0570

------------------------------------------------------------------------------
     V05_07a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_07a |  -2.083986   .2577349    -8.09   0.000    -2.589137   -1.578835
         S01 |  -.1225389   .1210486    -1.01   0.311    -.3597897     .114712
         S02 |  -.0022119   .0068892    -0.32   0.748    -.0157145    .0112907
         S03 |   .1003857   .0881099     1.14   0.255    -.0723064    .2730779
         S10 |   -.025186   .0418602    -0.60   0.547    -.1072306    .0568586
         S13 |  -.0174524   .0567016    -0.31   0.758    -.1285855    .0936806
       _cons |   4.840618   .8508766     5.69   0.000      3.17293    6.508305
-------------+----------------------------------------------------------------
2            |
     V04_07a |  -1.234388   .2602716    -4.74   0.000    -1.744511   -.7242651
         S01 |  -.0680193   .1227971    -0.55   0.580    -.3086973    .1726587
         S02 |  -.0094207   .0069728    -1.35   0.177    -.0230871    .0042457
         S03 |   .1027026   .0893482     1.15   0.250    -.0724166    .2778219
         S10 |   .0031389   .0424095     0.07   0.941    -.0799821      .08626
         S13 |   .0216955    .057599     0.38   0.706    -.0911964    .1345874
       _cons |   3.206906   .8647489     3.71   0.000      1.51203    4.901783
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_08a V04_08a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -693.68673  
Iteration 1:   log likelihood =  -660.9703  
Iteration 2:   log likelihood = -656.05567  
Iteration 3:   log likelihood = -656.00602  
Iteration 4:   log likelihood = -656.00599  

Multinomial logistic regression                   Number of obs   =        955
                                                  LR chi2(12)     =      75.36
                                                  Prob > chi2     =     0.0000
Log likelihood = -656.00599                       Pseudo R2       =     0.0543

------------------------------------------------------------------------------
     V05_08a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_08a |  -2.181771   .4554243    -4.79   0.000    -3.074386   -1.289156
         S01 |   .1286964    .205803     0.63   0.532    -.2746701    .5320628
         S02 |  -.0324851   .0123627    -2.63   0.009    -.0567155   -.0082547
         S03 |  -.1206366   .1521701    -0.79   0.428    -.4188845    .1776112
         S10 |   .0989262   .0717025     1.38   0.168    -.0416081    .2394605
         S13 |    .079617   .0976391     0.82   0.415     -.111752     .270986
       _cons |   1.402863   1.454773     0.96   0.335     -1.44844    4.254166
-------------+----------------------------------------------------------------
2            |
     V04_08a |  -1.421642   .2281231    -6.23   0.000    -1.868755   -.9745285
         S01 |   .0516402   .0735344     0.70   0.483    -.0924846    .1957651
         S02 |  -.0081238   .0041441    -1.96   0.050     -.016246   -1.56e-06
         S03 |   .0788034   .0512031     1.54   0.124    -.0215528    .1791597
         S10 |  -.0040264   .0246713    -0.16   0.870    -.0523813    .0443285
         S13 |   .0605564   .0342837     1.77   0.077    -.0066385    .1277512
       _cons |   1.722579   .6146678     2.80   0.005     .5178524    2.927306
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_09a V04_09a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -738.78012  
Iteration 1:   log likelihood =  -699.7195  
Iteration 2:   log likelihood = -699.38839  
Iteration 3:   log likelihood = -699.38675  
Iteration 4:   log likelihood = -699.38675  

Multinomial logistic regression                   Number of obs   =        941
                                                  LR chi2(12)     =      78.79
                                                  Prob > chi2     =     0.0000
Log likelihood = -699.38675                       Pseudo R2       =     0.0533

------------------------------------------------------------------------------
     V05_09a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_09a |   -1.39447   .4862882    -2.87   0.004    -2.347578    -.441363
         S01 |   .2064487   .2288382     0.90   0.367     -.242066    .6549633
         S02 |  -.0033351   .0127363    -0.26   0.793    -.0282978    .0216275
         S03 |  -.3351907    .183697    -1.82   0.068    -.6952303    .0248489
         S10 |  -.0189743   .0772059    -0.25   0.806    -.1702951    .1323465
         S13 |   .0566934   .1053549     0.54   0.590    -.1497985    .2631853
       _cons |   .5790889   1.544381     0.37   0.708    -2.447843     3.60602
-------------+----------------------------------------------------------------
2            |
     V04_09a |  -1.286923   .1760293    -7.31   0.000    -1.631934   -.9419122
         S01 |  -.0317003   .0689849    -0.46   0.646    -.1669082    .1035076
         S02 |  -.0029556   .0039004    -0.76   0.449    -.0106001     .004689
         S03 |   .0769268   .0486316     1.58   0.114    -.0183893    .1722429
         S10 |   .0054066   .0236287     0.23   0.819    -.0409049    .0517181
         S13 |   .0440036   .0321161     1.37   0.171    -.0189429      .10695
       _cons |   1.746769   .5099136     3.43   0.001     .7473565    2.746181
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_10a V04_10a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -889.39879  
Iteration 1:   log likelihood = -831.37664  
Iteration 2:   log likelihood =  -826.3216  
Iteration 3:   log likelihood = -826.28038  
Iteration 4:   log likelihood = -826.28036  

Multinomial logistic regression                   Number of obs   =        952
                                                  LR chi2(12)     =     126.24
                                                  Prob > chi2     =     0.0000
Log likelihood = -826.28036                       Pseudo R2       =     0.0710

------------------------------------------------------------------------------
     V05_10a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_10a |  -2.085817   .2949499    -7.07   0.000    -2.663909   -1.507726
         S01 |  -.0830961       .121    -0.69   0.492    -.3202517    .1540595
         S02 |  -.0211542   .0071082    -2.98   0.003    -.0350861   -.0072223
         S03 |   .2295334   .0900688     2.55   0.011     .0530017     .406065
         S10 |  -.0615877    .042415    -1.45   0.146    -.1447195     .021544
         S13 |   .1126589   .0563164     2.00   0.045     .0022808     .223037
       _cons |   4.877842   .9248148     5.27   0.000     3.065238    6.690445
-------------+----------------------------------------------------------------
2            |
     V04_10a |  -1.249882   .2845704    -4.39   0.000    -1.807629   -.6921339
         S01 |  -.1426713   .1123786    -1.27   0.204    -.3629293    .0775866
         S02 |  -.0208685   .0066434    -3.14   0.002    -.0338893   -.0078476
         S03 |   .2190823   .0846004     2.59   0.010     .0532686    .3848961
         S10 |  -.0202184     .03966    -0.51   0.610    -.0979505    .0575136
         S13 |   .1764744   .0526213     3.35   0.001     .0733385    .2796103
       _cons |   3.527269   .8819135     4.00   0.000      1.79875    5.255787
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_11a V04_11a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -552.56226  
Iteration 1:   log likelihood =  -525.2139  
Iteration 2:   log likelihood = -523.33554  
Iteration 3:   log likelihood = -523.12675  
Iteration 4:   log likelihood = -523.07861  
Iteration 5:   log likelihood = -523.06936  
Iteration 6:   log likelihood = -523.06733  
Iteration 7:   log likelihood = -523.06683  
Iteration 8:   log likelihood = -523.06673  
Iteration 9:   log likelihood = -523.06671  

Multinomial logistic regression                   Number of obs   =        942
                                                  LR chi2(12)     =      58.99
                                                  Prob > chi2     =     0.0000
Log likelihood = -523.06671                       Pseudo R2       =     0.0534

------------------------------------------------------------------------------
     V05_11a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_11a |   10.99264   1099.587     0.01   0.992    -2144.159    2166.144
         S01 |   6.495129   229.6465     0.03   0.977    -443.6037    456.5939
         S02 |   .0501034   .0523854     0.96   0.339    -.0525701    .1527768
         S03 |   .2426075   .4681705     0.52   0.604    -.6749897    1.160205
         S10 |   .1610458   .2758101     0.58   0.559    -.3795321    .7016236
         S13 |  -.2650532   .3313393    -0.80   0.424    -.9144663    .3843598
       _cons |  -37.98002   2211.139    -0.02   0.986    -4371.732    4295.772
-------------+----------------------------------------------------------------
2            |
     V04_11a |  -1.550671   .2539742    -6.11   0.000    -2.048451    -1.05289
         S01 |   .0667604   .0776071     0.86   0.390    -.0853467    .2188676
         S02 |  -.0130308   .0043802    -2.97   0.003    -.0216157   -.0044458
         S03 |   .0411751   .0542877     0.76   0.448    -.0652268     .147577
         S10 |   .0325599   .0261352     1.25   0.213    -.0186642     .083784
         S13 |   .0323316   .0358988     0.90   0.368    -.0380288     .102692
       _cons |   1.928046   .6572168     2.93   0.003     .6399248    3.216167
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_12a V04_12a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -748.33036  
Iteration 1:   log likelihood = -709.94929  
Iteration 2:   log likelihood = -704.35864  
Iteration 3:   log likelihood = -704.31498  
Iteration 4:   log likelihood = -704.31494  

Multinomial logistic regression                   Number of obs   =        969
                                                  LR chi2(12)     =      88.03
                                                  Prob > chi2     =     0.0000
Log likelihood = -704.31494                       Pseudo R2       =     0.0588

------------------------------------------------------------------------------
     V05_12a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_12a |  -2.373752   .3302524    -7.19   0.000    -3.021035   -1.726469
         S01 |  -.0634872   .1371332    -0.46   0.643    -.3322633    .2052889
         S02 |  -.0150779   .0079624    -1.89   0.058    -.0306839    .0005281
         S03 |   .2024042   .1021618     1.98   0.048     .0021707    .4026377
         S10 |  -.0644098   .0484355    -1.33   0.184    -.1593416     .030522
         S13 |  -.0636848   .0650618    -0.98   0.328    -.1912035     .063834
       _cons |   5.514216   1.022993     5.39   0.000     3.509187    7.519245
-------------+----------------------------------------------------------------
2            |
     V04_12a |  -1.750753   .2975683    -5.88   0.000    -2.333976    -1.16753
         S01 |  -.0547673   .1195296    -0.46   0.647    -.2890409    .1795064
         S02 |  -.0130106   .0070077    -1.86   0.063    -.0267454    .0007242
         S03 |    .226196   .0902254     2.51   0.012     .0493576    .4030345
         S10 |  -.0563201    .042897    -1.31   0.189    -.1403966    .0277564
         S13 |  -.0761444   .0572476    -1.33   0.183    -.1883476    .0360588
       _cons |   5.767742    .928339     6.21   0.000     3.948231    7.587253
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_13a V04_13a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -771.20828  
Iteration 1:   log likelihood = -740.56104  
Iteration 2:   log likelihood = -740.04219  
Iteration 3:   log likelihood = -740.04174  
Iteration 4:   log likelihood = -740.04174  

Multinomial logistic regression                   Number of obs   =        920
                                                  LR chi2(12)     =      62.33
                                                  Prob > chi2     =     0.0000
Log likelihood = -740.04174                       Pseudo R2       =     0.0404

------------------------------------------------------------------------------
     V05_13a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_13a |  -1.362534   .2922964    -4.66   0.000    -1.935424   -.7896431
         S01 |  -.2044264   .1423437    -1.44   0.151    -.4834149    .0745621
         S02 |   -.004339   .0077897    -0.56   0.578    -.0196064    .0109285
         S03 |  -.0141697   .0995402    -0.14   0.887     -.209265    .1809256
         S10 |  -.0349682   .0465857    -0.75   0.453    -.1262745    .0563381
         S13 |   .0523459   .0667951     0.78   0.433    -.0785701     .183262
       _cons |   1.080004    .932548     1.16   0.247    -.7477568    2.907764
-------------+----------------------------------------------------------------
2            |
     V04_13a |  -1.061144   .1663343    -6.38   0.000    -1.387153   -.7351346
         S01 |  -.0756257   .0757199    -1.00   0.318    -.2240339    .0727825
         S02 |  -.0059789    .004246    -1.41   0.159    -.0143009    .0023431
         S03 |  -.0120664   .0539187    -0.22   0.823    -.1177451    .0936122
         S10 |   .0326667   .0255602     1.28   0.201    -.0174304    .0827638
         S13 |  -.0032287   .0354742    -0.09   0.927    -.0727569    .0662994
       _cons |   2.555019   .5338333     4.79   0.000     1.508725    3.601313
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_14a V04_14a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -834.32636  
Iteration 1:   log likelihood = -777.68136  
Iteration 2:   log likelihood = -762.47593  
Iteration 3:   log likelihood = -762.35944  
Iteration 4:   log likelihood = -762.35928  
Iteration 5:   log likelihood = -762.35928  

Multinomial logistic regression                   Number of obs   =        973
                                                  LR chi2(12)     =     143.93
                                                  Prob > chi2     =     0.0000
Log likelihood = -762.35928                       Pseudo R2       =     0.0863

------------------------------------------------------------------------------
     V05_14a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_14a |  -2.684677   .2970771    -9.04   0.000    -3.266937   -2.102416
         S01 |  -.1859547   .1298854    -1.43   0.152    -.4405254     .068616
         S02 |  -.0122541   .0073715    -1.66   0.096    -.0267021    .0021938
         S03 |   .0182716   .0939946     0.19   0.846    -.1659544    .2024976
         S10 |   .0402227   .0446561     0.90   0.368    -.0473017    .1277471
         S13 |   .1865365   .0612082     3.05   0.002     .0665708    .3065023
       _cons |   5.430387   .9013186     6.02   0.000     3.663835    7.196939
-------------+----------------------------------------------------------------
2            |
     V04_14a |  -1.799031   .3056577    -5.89   0.000    -2.398109   -1.199953
         S01 |  -.1465463   .1338365    -1.09   0.274    -.4088611    .1157685
         S02 |  -.0120596   .0075785    -1.59   0.112    -.0269133     .002794
         S03 |   .1498491   .0960862     1.56   0.119    -.0384763    .3381745
         S10 |    .019638   .0458917     0.43   0.669     -.070308    .1095841
         S13 |   .1379148   .0628744     2.19   0.028     .0146832    .2611464
       _cons |   3.410398   .9274614     3.68   0.000     1.592608    5.228189
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_15a V04_15a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -629.58715  
Iteration 1:   log likelihood = -609.90859  
Iteration 2:   log likelihood = -609.34789  
Iteration 3:   log likelihood =  -609.2931  
Iteration 4:   log likelihood = -609.28272  
Iteration 5:   log likelihood =  -609.2804  
Iteration 6:   log likelihood = -609.27986  
Iteration 7:   log likelihood = -609.27974  
Iteration 8:   log likelihood = -609.27971  

Multinomial logistic regression                   Number of obs   =        941
                                                  LR chi2(12)     =      40.61
                                                  Prob > chi2     =     0.0001
Log likelihood = -609.27971                       Pseudo R2       =     0.0323

------------------------------------------------------------------------------
     V05_15a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_15a |   11.95606   761.8552     0.02   0.987    -1481.253    1505.165
         S01 |   .3754111   .4444991     0.84   0.398    -.4957911    1.246613
         S02 |  -.0351915   .0240231    -1.46   0.143     -.082276    .0118929
         S03 |  -.4089003   .3549547    -1.15   0.249    -1.104599    .2867982
         S10 |  -.0499956   .1344692    -0.37   0.710    -.3135504    .2135592
         S13 |   .1342918   .2015276     0.67   0.505     -.260695    .5292786
       _cons |  -25.71328   1523.712    -0.02   0.987    -3012.134    2960.707
-------------+----------------------------------------------------------------
2            |
     V04_15a |  -1.070087   .2616698    -4.09   0.000     -1.58295   -.5572236
         S01 |   .0222913   .0712969     0.31   0.755    -.1174481    .1620306
         S02 |  -.0037762   .0040311    -0.94   0.349    -.0116769    .0041246
         S03 |   .1337212   .0496896     2.69   0.007     .0363313     .231111
         S10 |   .0236096   .0242644     0.97   0.331    -.0239476    .0711669
         S13 |   .0446973   .0329727     1.36   0.175    -.0199279    .1093226
       _cons |   .5728226   .6459759     0.89   0.375    -.6932669    1.838912
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_16a V04_16a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -748.30538  
Iteration 1:   log likelihood = -694.94183  
Iteration 2:   log likelihood = -690.73777  
Iteration 3:   log likelihood = -690.70975  
Iteration 4:   log likelihood = -690.70974  

Multinomial logistic regression                   Number of obs   =        950
                                                  LR chi2(12)     =     115.19
                                                  Prob > chi2     =     0.0000
Log likelihood = -690.70974                       Pseudo R2       =     0.0770

------------------------------------------------------------------------------
     V05_16a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_16a |  -2.233386   .3032232    -7.37   0.000    -2.827693    -1.63908
         S01 |  -.0587009   .1314939    -0.45   0.655    -.3164242    .1990224
         S02 |  -.0147343   .0074351    -1.98   0.048    -.0293069   -.0001617
         S03 |  -.0707718   .0962704    -0.74   0.462    -.2594583    .1179147
         S10 |   .0797616   .0458227     1.74   0.082    -.0100492    .1695724
         S13 |   .1160378   .0612644     1.89   0.058    -.0040381    .2361137
       _cons |   2.887738   .9191402     3.14   0.002     1.086256    4.689219
-------------+----------------------------------------------------------------
2            |
     V04_16a |   -1.71827   .2356134    -7.29   0.000    -2.180063   -1.256476
         S01 |  -.0447993   .0910607    -0.49   0.623    -.2232749    .1336764
         S02 |  -.0047336   .0050639    -0.93   0.350    -.0146586    .0051914
         S03 |   .1183472   .0651475     1.82   0.069    -.0093395    .2460339
         S10 |   .0469351   .0318591     1.47   0.141    -.0155075    .1093777
         S13 |    .104971   .0418367     2.51   0.012     .0229727    .1869694
       _cons |   3.194784   .6825537     4.68   0.000     1.857003    4.532564
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V05_17a V04_17a S01 S02 S03 S10 S13, baseoutcome(3)

Iteration 0:   log likelihood = -668.77276  
Iteration 1:   log likelihood =  -648.7207  
Iteration 2:   log likelihood = -640.95281  
Iteration 3:   log likelihood = -640.81035  
Iteration 4:   log likelihood = -640.80958  
Iteration 5:   log likelihood = -640.80958  

Multinomial logistic regression                   Number of obs   =        927
                                                  LR chi2(12)     =      55.93
                                                  Prob > chi2     =     0.0000
Log likelihood = -640.80958                       Pseudo R2       =     0.0418

------------------------------------------------------------------------------
     V05_17a |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
     V04_17a |  -2.358715   .6116302    -3.86   0.000    -3.557488   -1.159942
         S01 |  -.2956938   .2884246    -1.03   0.305    -.8609956    .2696081
         S02 |   .0029225   .0163288     0.18   0.858    -.0290813    .0349263
         S03 |  -.4753651   .2358011    -2.02   0.044    -.9375268   -.0132034
         S10 |   .0353556   .0942416     0.38   0.708    -.1493545    .2200658
         S13 |   .3496822   .1556793     2.25   0.025     .0445563     .654808
       _cons |  -.2079645   1.901337    -0.11   0.913    -3.934517    3.518588
-------------+----------------------------------------------------------------
2            |
     V04_17a |  -1.176134   .2338732    -5.03   0.000    -1.634517    -.717751
         S01 |    .057136   .0709559     0.81   0.421     -.081935    .1962071
         S02 |  -.0028646   .0039911    -0.72   0.473    -.0106871    .0049579
         S03 |   .1070512   .0493039     2.17   0.030     .0104173    .2036851
         S10 |   .0208396   .0241192     0.86   0.388    -.0264332    .0681124
         S13 |   .0469053   .0329116     1.43   0.154    -.0176002    .1114107
       _cons |   .9523342   .5825652     1.63   0.102    -.1894725    2.094141
-------------+----------------------------------------------------------------
3            |  (base outcome)
------------------------------------------------------------------------------

. mlogit V06 S01 S02 S03 S10 S13, baseoutcome(2)

Iteration 0:   log likelihood = -1076.2875  
Iteration 1:   log likelihood = -1040.4722  
Iteration 2:   log likelihood = -1039.4375  
Iteration 3:   log likelihood = -1039.4354  
Iteration 4:   log likelihood = -1039.4354  

Multinomial logistic regression                   Number of obs   =       1012
                                                  LR chi2(10)     =      73.70
                                                  Prob > chi2     =     0.0000
Log likelihood = -1039.4354                       Pseudo R2       =     0.0342

------------------------------------------------------------------------------
         V06 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
         S01 |  -.1821351   .0839947    -2.17   0.030    -.3467617   -.0175085
         S02 |    -.00608   .0047417    -1.28   0.200    -.0153735    .0032135
         S03 |   .2520149   .0587286     4.29   0.000     .1369089    .3671209
         S10 |   .0965684   .0292337     3.30   0.001     .0392713    .1538654
         S13 |   .0449106   .0384661     1.17   0.243    -.0304814    .1203027
       _cons |  -2.561017   .4888709    -5.24   0.000    -3.519186   -1.602847
-------------+----------------------------------------------------------------
2            |  (base outcome)
-------------+----------------------------------------------------------------
3            |
         S01 |  -.0373338   .0727274    -0.51   0.608    -.1798768    .1052091
         S02 |    .003378   .0040829     0.83   0.408    -.0046244    .0113804
         S03 |  -.0606674   .0521175    -1.16   0.244    -.1628159    .0414811
         S10 |   .0016822   .0248238     0.07   0.946    -.0469715    .0503358
         S13 |   .0266676   .0336462     0.79   0.428    -.0392778     .092613
       _cons |  -.3797953   .4204933    -0.90   0.366    -1.203947    .4443564
------------------------------------------------------------------------------

. mlogit V07 S01 S02 S03 S10 S13, baseoutcome(2)

Iteration 0:   log likelihood = -905.78112  
Iteration 1:   log likelihood = -880.89873  
Iteration 2:   log likelihood = -879.81057  
Iteration 3:   log likelihood = -879.80627  
Iteration 4:   log likelihood = -879.80627  

Multinomial logistic regression                   Number of obs   =       1013
                                                  LR chi2(10)     =      51.95
                                                  Prob > chi2     =     0.0000
Log likelihood = -879.80627                       Pseudo R2       =     0.0287

------------------------------------------------------------------------------
         V07 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
1            |
         S01 |  -.1352461   .1040076    -1.30   0.193    -.3390974    .0686051
         S02 |  -.0195227   .0059396    -3.29   0.001    -.0311641   -.0078814
         S03 |   .2208555   .0737618     2.99   0.003     .0762851     .365426
         S10 |   .0240874   .0348621     0.69   0.490    -.0442411    .0924159
         S13 |    .053578   .0478763     1.12   0.263    -.0402578    .1474138
       _cons |  -2.191589    .581705    -3.77   0.000     -3.33171   -1.051469
-------------+----------------------------------------------------------------
2            |  (base outcome)
-------------+----------------------------------------------------------------
3            |
         S01 |   -.004598   .0736448    -0.06   0.950    -.1489392    .1397432
         S02 |   .0092511   .0041728     2.22   0.027     .0010726    .0174296
         S03 |  -.0068178   .0517743    -0.13   0.895    -.1082937     .094658
         S10 |  -.0524674   .0251078    -2.09   0.037    -.1016778   -.0032571
         S13 |  -.0000121   .0340006    -0.00   1.000     -.066652    .0666278
       _cons |  -.8256976    .426246    -1.94   0.053    -1.661124    .0097292
------------------------------------------------------------------------------

Codes der Variablen:

S01: Geschlecht
	Männer: -1
	Frauen: 1

S02: Alter

S03: Bildungsabschluss
	Ich bin von der Schule abgegangen ohne Hauptschulabschluss/Volksschulabschluss: 1
	Ich habe den Hauptschulabschluss/Volksschulabschluss bzw. Ich bin von der Realschule, polytechnischen Oberschule oder einer vergleichbaren Schule abgegangen ohne Realschulabschluss, ohne Mittlere Reife: 2
	Ich habe den Realschulabschluss (Mittlere Reife, Abschluss der 10-klassigen polytechnischen Oberschule): 3
	Ich habe die Fachhochschulreife: 4
	Ich habe die allgemeine oder fachgebundene Hochschulreife (Abitur, Fachabitur, Abschluss der 12-klassigen EOS): 5
	Ich habe ein Studium an einer Universität, Fachhochschule oder Pädagogischen Hochschule abgeschlossen: 6

S10: Montasnettoeinkommen (des Haushalts)
	Unter 500 Euro: 1
	500 - 749 Euro: 2
	750 - 999 Euro: 3
	1.000 - 1.249 Euro: 4
	1.250 . 1.499 Euro: 5
	1.500 - 1.749 Euro: 6
	1.750 - 1.999 Euro: 7
	2.000 - 2.499 Euro: 8
	2.500 - 2.999 Euro: 9
	3.000 - 3.499 Euro: 10
	3.500 - 3.999 Euro: 11
	4.000 - 4.999 Euro: 12
	5.000 - 5.999 Euro: 13
	6.000 - 7.499 Euro: 14
	7.500 - 9.999 Euro: 15
	10.000 Euro und mehr: 16

S13: Einwohnerzahl (des Wohnorts)
	Unter 2.000 Einwohner: 1
	2.000 - unter 5.000: 2
	5.000 - unter 10.000: 3
	10.000 - unter 20.000: 4
	20.000 - unter 50.000: 5
	50.000 - unter 100.000: 6
	100.000 - unter 500.000: 7
	500.000 und mehr: 8

V01: Wissen über Algorithmen und Künstliche Intelligenz (Selbsteinschätzung allgemein)
	Recht genau: 1
	Ungefähr: 2
	Kaum etwas: 3
	Höre Begriff zum ersten Mal: 4

V04_01: Wissen um den Einsatz von Algorithmen bzw. Künstliche Intelligenz in Anwendungsfällen

V04_01a: Bewertung der Kreditwürdigkeit, also ob jemand einen Kredit bekommt oder nicht
	trifft zu: 1
	trifft nicht zu: 2

V04_02a: Auswahl von möglichen Partnern bei Single-Börsen im Internet
	trifft zu: 1
	trifft nicht zu: 2

V04_03a: Vorauswahl von Bewerbern anhand bestimmter Kriterien wie Noten oder Berufserfahrung
	trifft zu: 1
	trifft nicht zu: 2

V04_04a: Individuelle Auswahl an Nachrichten und aktuellen Meldungen, die man als Internetnutzer angezeigt bekommt
	trifft zu: 1
	trifft nicht zu: 2

V04_05a: Individuelle Auswahl an Werbung, die man als Internetnutzer angezeigt bekommt
	trifft zu: 1
	trifft nicht zu: 2

V04_06a: Diagnose von Krankheiten anhand bestimmter Symptome
	trifft zu: 1
	trifft nicht zu: 2

V04_07a: Gesichtserkennung bei der Videoüberwachung 
	trifft zu: 1
	trifft nicht zu: 2

V04_08a: Vergabe von Betreuungsplätzen in Kindertagesstätten
	trifft zu: 1
	trifft nicht zu: 2

V04_09a: Beurteilung des Risikos, ob ein Straftäter rückfällig wird
	trifft zu: 1
	trifft nicht zu: 2

V04_010a: Auffinden von Unregelmäßigkeiten in Steuererklärungen
	trifft zu: 1
	trifft nicht zu: 2

V04_011a: Beobachten des Verhaltens von Patienten im Pflegeheim und Entscheidung, ob man eingreifen muss
	trifft zu: 1
	trifft nicht zu: 2

V04_012a: Frühzeitiges Erkennen von Katastrophensituationen, z.B. Naturkatastrophen
	trifft zu: 1
	trifft nicht zu: 2

V04_013a: Auswahl und Beschuss von Angriffszielen in einem militärischen Konflikt, z.B. durch Drohnen
	trifft zu: 1
	trifft nicht zu: 2

V04_014a: Rechtschreib- und Satzbaukontrolle bei der Textverarbeitung
	trifft zu: 1
	trifft nicht zu: 2

V04_015a: Einschätzung des Risikos, ob Kinder in ihren Familien misshandelt werden
	trifft zu: 1
	trifft nicht zu: 2

V04_016a: Planung von Polizeieinsätzen durch Berechnung, welche Gebiete einbruchsgefährdet sind
	trifft zu: 1
	trifft nicht zu: 2

V04_017a: Einschätzung des Risikos, ob es in Partnerschaften, in denen es bereits zu Gewalt kam, erneut zu Gewalt kommen könnte
	trifft zu: 1
	trifft nicht zu: 2

V05_01: Akzeptanz automatisierter Entscheidungen Einsatz in Anwendungsfällen / Wer entscheiden soll

V05_01a: Bewertung der Kreditwürdigkeit, also ob jemand einen Kredit bekommt oder nicht
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_02a: Auswahl von möglichen Partnern bei Single-Börsen im Internet
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_03a: Vorauswahl von Bewerbern anhand bestimmter Kriterien wie Noten oder Berufserfahrung
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_04a: Individuelle Auswahl an Nachrichten und aktuellen Meldungen, die man als Internetnutzer angezeigt bekommt
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_05a: Individuelle Auswahl an Werbung, die man als Internetnutzer angezeigt bekommt
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_06a: Diagnose von Krankheiten anhand bestimmter Symptome
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_07a: Gesichtserkennung bei der Videoüberwachung 
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_08a: Vergabe von Betreuungsplätzen in Kindertagesstätten
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_09a: Beurteilung des Risikos, ob ein Straftäter rückfällig wird
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_10a: Auffinden von Unregelmäßigkeiten in Steuererklärungen
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_11a: Beobachten des Verhaltens von Patienten im Pflegeheim und Entscheidung, ob man eingreifen muss
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_12a: Frühzeitiges Erkennen von Katastrophensituationen, z.B. Naturkatastrophen
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_13a: Auswahl und Beschuss von Angriffszielen in einem militärischen Konflikt, z.B. durch Drohnen
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_14a: Rechtschreib- und Satzbaukontrolle bei der Textverarbeitung
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_15a: Einschätzung des Risikos, ob Kinder in ihren Familien misshandelt werden
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_16a: Planung von Polizeieinsätzen durch Berechnung, welche Gebiete einbruchsgefährdet sind
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3

V05_17a: Einschätzung des Risikos, ob es in Partnerschaften, in denen es bereits zu Gewalt kam, erneut zu Gewalt kommen könnte
	Computer allein: 1
	Computer Vorschläge, Mensch entscheidet: 2
	Mensch allein: 3