First, we replicated the finding that people believed the paying the fine obligation was tacked onto the body more than anything else. zoib paytracksbody if payorder == 1 & comprehend == 2, robust Iteration 0: log pseudolikelihood = -255.22053 Iteration 1: log pseudolikelihood = -233.36594 Iteration 2: log pseudolikelihood = -233.00395 Iteration 3: log pseudolikelihood = -233.00382 Iteration 4: log pseudolikelihood = -233.00382 ML fit of zoib Number of obs = 261 Wald chi2(0) = . Log pseudolikelihood = -233.00382 Prob > chi2 = . ------------------------------------------------------------------------------ | Robust paytracksb~y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- proportion | _cons | -.0864161 .1260564 -0.69 0.493 -.3334822 .16065 -------------+---------------------------------------------------------------- oneinflate | _cons | -.4300364 .1572541 -2.73 0.006 -.7382488 -.121824 -------------+---------------------------------------------------------------- zeroinflate | _cons | -.1238695 .1441435 -0.86 0.390 -.4063856 .1586466 -------------+---------------------------------------------------------------- ln_phi | _cons | .1433044 .121872 1.18 0.240 -.0955603 .3821692 ------------------------------------------------------------------------------ with [DV] being the variablename either pay jail car or spouse. This initial analysis will help give an idea of how participants decide what obligations track what aspects. We predict we will replicate the majority of participants will select tracking the body for the pay and car variables, but tracking the mind for jail and spouse variables. Second, we predict that not all variables will show the same amount of 'obligation is destroyed' responses (selecting zero from both options). This will be tested by predicting the zeroes option from the scenario. This will be done using the following code: probit na1st i.dv, vce(robust) We predict the obligations that track the mind will show the greatest amount of obligation negation. Third, we predict that there will be sex differences in the spouse response, such that men will be more likely to say the obligation tracks the body and women more likely to say the obligation tracks the mind. We also predict that there will be an interaction based on whether the participants are answering for JOhn or Jane. We will test this using the following code: zoib tracksbodyspouse female##jane, zeroinflate(female##jane) oneinflate(female##jane) robust Finally, we will test the possibility that responses change over time, meaning order effects in the response options. We do not predict this will be the case, however. There are indeed order effects for all of the variables. For pay, the likleihood of choosing the tracking body goes down over scenarios. For jail and car, the likleihood of choosingn tracking the body goes up!. For car as well, the liklihood of choosing the mind also goes up after repreated viewings. Only for spouse does the liklihood the mind goes up over repeated scenarios, suggesting people are altering their responses the more familiar they become with these types of scenariors. We also found the sex differences but only for female participoants who were more likely to schoose a monogmistic response (either aLL BODY or all mind) than men were. zoib spousetracksbody, zeroinflate(i.female) oneinflate(i.female) robust Iteration 0: log pseudolikelihood = -1060.9309 Iteration 1: log pseudolikelihood = -1011.9659 Iteration 2: log pseudolikelihood = -1010.9796 Iteration 3: log pseudolikelihood = -1010.9793 Iteration 4: log pseudolikelihood = -1010.9793 ML fit of zoib Number of obs = 1226 Wald chi2(0) = . Log pseudolikelihood = -1010.9793 Prob > chi2 = . ------------------------------------------------------------------------------ | Robust spousetrac~y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- proportion | _cons | -.0558881 .0339434 -1.65 0.100 -.1224159 .0106397 -------------+---------------------------------------------------------------- oneinflate | female | Female | .4570229 .1529339 2.99 0.003 .157278 .7567679 _cons | -1.332492 .1097552 -12.14 0.000 -1.547608 -1.117376 -------------+---------------------------------------------------------------- zeroinflate | female | Female | .4170998 .1390871 3.00 0.003 .144494 .6897056 _cons | -1.051978 .0985633 -10.67 0.000 -1.245159 -.8587975 -------------+---------------------------------------------------------------- ln_phi | _cons | 1.473979 .0738234 19.97 0.000 1.329288 1.61867 ------------------------------------------------------------------------------ . probit spousetracksbody i.female if spousetracksbody == 0 | spousetracksbody == 1, vce(robust) Iteration 0: log pseudolikelihood = -361.59074 Iteration 1: log pseudolikelihood = -361.56503 Iteration 2: log pseudolikelihood = -361.56503 Probit regression Number of obs = 528 Wald chi2(1) = 0.05 Prob > chi2 = 0.8208 Log pseudolikelihood = -361.56503 Pseudo R2 = 0.0001 ---------------------------------------------------------------------------------- | Robust spousetracksbody | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- female | Female | .0249274 .1100452 0.23 0.821 -.1907573 .2406121 _cons | -.1755395 .0807626 -2.17 0.030 -.3338312 -.0172478 ---------------------------------------------------------------------------------- . zoib paytracksbody payorder, zeroinflate(payorder) oneinflate(payorder) robust Iteration 0: log pseudolikelihood = -1330.7409 Iteration 1: log pseudolikelihood = -1211.9713 Iteration 2: log pseudolikelihood = -1208.1513 Iteration 3: log pseudolikelihood = -1208.1463 Iteration 4: log pseudolikelihood = -1208.1463 ML fit of zoib Number of obs = 1403 Wald chi2(1) = 1.92 Log pseudolikelihood = -1208.1463 Prob > chi2 = 0.1657 ------------------------------------------------------------------------------ | Robust paytracksb~y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- proportion | payorder | -.0526121 .037957 -1.39 0.166 -.1270064 .0217823 _cons | .0483906 .1057935 0.46 0.647 -.1589608 .255742 -------------+---------------------------------------------------------------- oneinflate | payorder | -.2654542 .0692384 -3.83 0.000 -.4011589 -.1297495 _cons | -.6678655 .1771451 -3.77 0.000 -1.015063 -.3206675 -------------+---------------------------------------------------------------- zeroinflate | payorder | -.0848798 .058459 -1.45 0.147 -.1994575 .0296978 _cons | -.7020696 .1596682 -4.40 0.000 -1.015014 -.3891257 -------------+---------------------------------------------------------------- ln_phi | _cons | .3815811 .0491075 7.77 0.000 .2853321 .4778301 ------------------------------------------------------------------------------ . hist paytracksbody if payorder == 1 & comp1pin == 1 & comp2bday == 1 (bin=10, start=0, width=.1) . zoib jailtracksbody jailorder, zeroinflate(jailorder) oneinflate(jailorder) robust Iteration 0: log pseudolikelihood = -1156.0782 Iteration 1: log pseudolikelihood = -1112.5845 Iteration 2: log pseudolikelihood = -1111.3181 Iteration 3: log pseudolikelihood = -1111.3172 Iteration 4: log pseudolikelihood = -1111.3172 ML fit of zoib Number of obs = 1326 Wald chi2(1) = 0.04 Log pseudolikelihood = -1111.3172 Prob > chi2 = 0.8493 ------------------------------------------------------------------------------ | Robust jailtracks~y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- proportion | jailorder | -.0056789 .0298787 -0.19 0.849 -.0642401 .0528822 _cons | -.0719867 .0864471 -0.83 0.405 -.2414198 .0974465 -------------+---------------------------------------------------------------- oneinflate | jailorder | .1549988 .0698059 2.22 0.026 .0181819 .2918158 _cons | -1.593244 .2030695 -7.85 0.000 -1.991253 -1.195235 -------------+---------------------------------------------------------------- zeroinflate | jailorder | .0129075 .0549507 0.23 0.814 -.0947939 .120609 _cons | -.5174807 .1530476 -3.38 0.001 -.8174485 -.2175128 -------------+---------------------------------------------------------------- ln_phi | _cons | 1.521155 .0706067 21.54 0.000 1.382768 1.659541 ------------------------------------------------------------------------------ . zoib cartracksbody carorder, zeroinflate(carorder) oneinflate(carorder) robust Iteration 0: log pseudolikelihood = -1187.5345 Iteration 1: log pseudolikelihood = -1133.1167 Iteration 2: log pseudolikelihood = -1131.6823 Iteration 3: log pseudolikelihood = -1131.6816 Iteration 4: log pseudolikelihood = -1131.6816 ML fit of zoib Number of obs = 1331 Wald chi2(1) = 1.44 Log pseudolikelihood = -1131.6816 Prob > chi2 = 0.2305 ------------------------------------------------------------------------------ | Robust cartracksb~y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- proportion | carorder | .0345054 .028774 1.20 0.230 -.0218906 .0909015 _cons | -.0831177 .076487 -1.09 0.277 -.2330294 .0667941 -------------+---------------------------------------------------------------- oneinflate | carorder | .2433311 .0649393 3.75 0.000 .1160525 .3706097 _cons | -1.81486 .1821837 -9.96 0.000 -2.171933 -1.457786 -------------+---------------------------------------------------------------- zeroinflate | carorder | .2136124 .056432 3.79 0.000 .1030077 .3242171 _cons | -1.289281 .1551905 -8.31 0.000 -1.593449 -.9851132 -------------+---------------------------------------------------------------- ln_phi | _cons | 1.297821 .0644837 20.13 0.000 1.171435 1.424207 ------------------------------------------------------------------------------ . zoib spousetracksbody spouseorder, zeroinflate(spouseorder) oneinflate(spouseorder) robust Iteration 0: log pseudolikelihood = -1060.9309 Iteration 1: log pseudolikelihood = -1014.4564 Iteration 2: log pseudolikelihood = -1013.7641 Iteration 3: log pseudolikelihood = -1013.7639 Iteration 4: log pseudolikelihood = -1013.7639 ML fit of zoib Number of obs = 1226 Wald chi2(1) = 1.53 Log pseudolikelihood = -1013.7639 Prob > chi2 = 0.2160 ------------------------------------------------------------------------------ | Robust spousetrac~y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- proportion | spouseorder | -.0373116 .0301571 -1.24 0.216 -.0964185 .0217953 _cons | .0349855 .07898 0.44 0.658 -.1198124 .1897835 -------------+---------------------------------------------------------------- oneinflate | spouseorder | .0556072 .069355 0.80 0.423 -.0803262 .1915406 _cons | -1.247485 .1886313 -6.61 0.000 -1.617196 -.8777747 -------------+---------------------------------------------------------------- zeroinflate | spouseorder | .198582 .0632891 3.14 0.002 .0745375 .3226264 _cons | -1.359219 .1794538 -7.57 0.000 -1.710942 -1.007496 -------------+---------------------------------------------------------------- ln_phi | _cons | 1.476344 .0734871 20.09 0.000 1.332312 1.620376 ------------------------------------------------------------------------------ It tracking the body August 14, 2019 Here are the results form people’s first responses to each of the tracking scenarios zoib paytracksbody if payorder == 1, robust Iteration 0: log pseudolikelihood = -367.07211 Iteration 1: log pseudolikelihood = -330.92904 Iteration 2: log pseudolikelihood = -330.06886 Iteration 3: log pseudolikelihood = -330.06842 Iteration 4: log pseudolikelihood = -330.06842 ML fit of zoib Number of obs = 351 Wald chi2(0) = . Log pseudolikelihood = -330.06842 Prob > chi2 = . ------------------------------------------------------------------------------ | Robust paytracksb~y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- proportion | _cons | -.0000288 .0938174 -0.00 1.000 -.1839075 .1838499 -------------+---------------------------------------------------------------- oneinflate | _cons | -.8865186 .1374105 -6.45 0.000 -1.155838 -.617199 -------------+---------------------------------------------------------------- zeroinflate | _cons | -.6607119 .1271963 -5.19 0.000 -.9100121 -.4114118 -------------+---------------------------------------------------------------- ln_phi | _cons | .5189115 .1215484 4.27 0.000 .2806811 .7571419 ------------------------------------------------------------------------------ Who is going to jail? The brain and both of them Who’s driving the car? Who’s driving the spouse? EVRYOONE