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.
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. 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
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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
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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
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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
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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
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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