---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- name: log: C:\Users\therriault\Desktop\Research\projects_current\QP\QPv7\bjps\therriault_bjps_final\02_cces08_analysis.log log type: text opened on: 22 Nov 2014, 16:49:17 . ************************************************************************; . * File-Name: 02_cces08_analysis.do *; . * Date: November 22, 2014 *; . * Author: Andrew Therriault *; . * Purpose: Final analyses for BJPS article *; . * Data In: cces_2008_prepped.dta *; . * Data Out: None *; . * Log File: 02_cces08_analysis.log *; . * Status: Submission *; . * Machine: AMT-ThinkPad2 *; . ************************************************************************; . use cces_2008_prepped.dta; . ************************************************************************; . * *; . * Tabulating general results by wording and doing t-tests *; . * *; . ************************************************************************; . table splitabc, contents(mean beval_health mean > beval_socsec mean beval_iraq mean beval_terror > mean beval_econ) format(%12.3g); --------------------------------------------------------------------------------------- prompt | randomization | ucb444-452 | mean(beva~h) mean(beva~c) mean(beva~q) mean(beva~r) mean(beva~n) -----------------+--------------------------------------------------------------------- better job | .33 .333 .458 .559 .344 better ideas | .335 .344 .459 .568 .332 better qualified | .374 .392 .559 .659 .409 --------------------------------------------------------------------------------------- . table splitabc, contents(mean beval_taxes mean > beval_educ mean beval_energy mean beval_immig) > format(%12.3g); ------------------------------------------------------------------------- prompt | randomization | ucb444-452 | mean(beva~s) mean(beva~c) mean(beva~y) mean(beva~g) -----------------+------------------------------------------------------- better job | .427 .297 .367 .441 better ideas | .446 .307 .407 .425 better qualified | .539 .38 .462 .525 ------------------------------------------------------------------------- . #delimit cr delimiter now cr . . local evars health socsec iraq terror econ taxes educ energy immig . . while "`evars'" ~="" { 2. . gettoken e evars : evars 3. . di "************************************************" 4. di "************************************************" 5. di "************** Issue `e' *************************" 6. di "************************************************" 7. di "************************************************" 8. . ttest beval_`e' if splitabc ~= 3, by(splitabc) 9. ttest beval_`e' if splitabc ~= 2, by(splitabc) 10. . } ************************************************ ************************************************ ************** Issue health ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 233 .3304721 .0308822 .4713958 .2696268 .3913174 better i | 197 .3350254 .0337142 .4732019 .2685361 .4015146 ---------+-------------------------------------------------------------------- combined | 430 .3325581 .0227464 .4716785 .28785 .3772663 ---------+-------------------------------------------------------------------- diff | -.0045533 .0457058 -.094389 .0852824 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.0996 Ho: diff = 0 degrees of freedom = 428 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.4603 Pr(|T| > |t|) = 0.9207 Pr(T > t) = 0.5397 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 233 .3304721 .0308822 .4713958 .2696268 .3913174 better q | 211 .3744076 .0333971 .4851205 .3085711 .4402441 ---------+-------------------------------------------------------------------- combined | 444 .3513514 .0226816 .477931 .3067744 .3959283 ---------+-------------------------------------------------------------------- diff | -.0439355 .0454223 -.133206 .045335 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -0.9673 Ho: diff = 0 degrees of freedom = 442 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1670 Pr(|T| > |t|) = 0.3339 Pr(T > t) = 0.8330 ************************************************ ************************************************ ************** Issue socsec ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 219 .3333333 .0319275 .4724845 .2704072 .3962595 better i | 183 .3442623 .0352188 .4764306 .2747727 .4137519 ---------+-------------------------------------------------------------------- combined | 402 .3383085 .0236272 .4737236 .2918598 .3847571 ---------+-------------------------------------------------------------------- diff | -.010929 .0475011 -.104312 .0824541 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.2301 Ho: diff = 0 degrees of freedom = 400 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.4091 Pr(|T| > |t|) = 0.8181 Pr(T > t) = 0.5909 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 219 .3333333 .0319275 .4724845 .2704072 .3962595 better q | 199 .3919598 .034694 .489419 .3235426 .460377 ---------+-------------------------------------------------------------------- combined | 418 .361244 .0235234 .4809367 .3150048 .4074832 ---------+-------------------------------------------------------------------- diff | -.0586265 .0470696 -.1511504 .0338975 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -1.2455 Ho: diff = 0 degrees of freedom = 416 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1068 Pr(|T| > |t|) = 0.2136 Pr(T > t) = 0.8932 ************************************************ ************************************************ ************** Issue iraq ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 236 .4576271 .0324991 .4992602 .3936004 .5216539 better i | 207 .4589372 .034719 .499519 .3904871 .5273873 ---------+-------------------------------------------------------------------- combined | 443 .4582393 .0236995 .4988163 .4116616 .5048169 ---------+-------------------------------------------------------------------- diff | -.0013101 .0475546 -.0947719 .0921518 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.0275 Ho: diff = 0 degrees of freedom = 441 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.4890 Pr(|T| > |t|) = 0.9780 Pr(T > t) = 0.5110 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 236 .4576271 .0324991 .4992602 .3936004 .5216539 better q | 229 .558952 .0328823 .4976002 .4941598 .6237441 ---------+-------------------------------------------------------------------- combined | 465 .5075269 .0232093 .5004818 .4619185 .5531352 ---------+-------------------------------------------------------------------- diff | -.1013248 .0462348 -.1921808 -.0104689 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -2.1915 Ho: diff = 0 degrees of freedom = 463 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0145 Pr(|T| > |t|) = 0.0289 Pr(T > t) = 0.9855 ************************************************ ************************************************ ************** Issue terror ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 211 .5592417 .0342602 .4976587 .4917036 .6267798 better i | 185 .5675676 .0365224 .496758 .4955111 .639624 ---------+-------------------------------------------------------------------- combined | 396 .5631313 .0249564 .4966259 .5140674 .6121953 ---------+-------------------------------------------------------------------- diff | -.0083259 .0500824 -.106788 .0901363 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.1662 Ho: diff = 0 degrees of freedom = 394 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.4340 Pr(|T| > |t|) = 0.8681 Pr(T > t) = 0.5660 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 211 .5592417 .0342602 .4976587 .4917036 .6267798 better q | 211 .6587678 .0327176 .4752507 .5942707 .7232648 ---------+-------------------------------------------------------------------- combined | 422 .6090047 .0237824 .4885525 .5622578 .6557517 ---------+-------------------------------------------------------------------- diff | -.0995261 .047373 -.1926439 -.0064083 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -2.1009 Ho: diff = 0 degrees of freedom = 420 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0181 Pr(|T| > |t|) = 0.0362 Pr(T > t) = 0.9819 ************************************************ ************************************************ ************** Issue econ ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 227 .3436123 .0315908 .4759631 .2813622 .4058625 better i | 187 .3315508 .0345186 .4720343 .2634526 .399649 ---------+-------------------------------------------------------------------- combined | 414 .3381643 .023279 .4736572 .2924042 .3839243 ---------+-------------------------------------------------------------------- diff | .0120615 .0468298 -.0799935 .1041166 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = 0.2576 Ho: diff = 0 degrees of freedom = 412 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.6016 Pr(|T| > |t|) = 0.7969 Pr(T > t) = 0.3984 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 227 .3436123 .0315908 .4759631 .2813622 .4058625 better q | 208 .4086538 .0341675 .492771 .3412929 .4760148 ---------+-------------------------------------------------------------------- combined | 435 .3747126 .0232351 .484606 .3290454 .4203799 ---------+-------------------------------------------------------------------- diff | -.0650415 .0464632 -.156363 .02628 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -1.3998 Ho: diff = 0 degrees of freedom = 433 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0811 Pr(|T| > |t|) = 0.1623 Pr(T > t) = 0.9189 ************************************************ ************************************************ ************** Issue taxes ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 239 .4267782 .0320608 .4956475 .3636191 .4899374 better i | 195 .4461538 .0356891 .4983717 .3757653 .5165424 ---------+-------------------------------------------------------------------- combined | 434 .4354839 .0238276 .4963924 .3886517 .482316 ---------+-------------------------------------------------------------------- diff | -.0193756 .0479484 -.1136167 .0748655 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.4041 Ho: diff = 0 degrees of freedom = 432 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.3432 Pr(|T| > |t|) = 0.6863 Pr(T > t) = 0.6568 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 239 .4267782 .0320608 .4956475 .3636191 .4899374 better q | 217 .5391705 .0339161 .4996158 .4723216 .6060195 ---------+-------------------------------------------------------------------- combined | 456 .4802632 .0234221 .500159 .4342343 .5262921 ---------+-------------------------------------------------------------------- diff | -.1123923 .0466532 -.2040752 -.0207093 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -2.4091 Ho: diff = 0 degrees of freedom = 454 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0082 Pr(|T| > |t|) = 0.0164 Pr(T > t) = 0.9918 ************************************************ ************************************************ ************** Issue educ ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 202 .2970297 .0322307 .458085 .2334759 .3605835 better i | 166 .3072289 .0359157 .462741 .2363154 .3781424 ---------+-------------------------------------------------------------------- combined | 368 .3016304 .0239578 .4595906 .2545186 .3487423 ---------+-------------------------------------------------------------------- diff | -.0101992 .0482093 -.1050013 .0846029 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.2116 Ho: diff = 0 degrees of freedom = 366 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.4163 Pr(|T| > |t|) = 0.8326 Pr(T > t) = 0.5837 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 202 .2970297 .0322307 .458085 .2334759 .3605835 better q | 200 .38 .0344081 .4866045 .3121487 .4478513 ---------+-------------------------------------------------------------------- combined | 402 .3383085 .0236272 .4737236 .2918598 .3847571 ---------+-------------------------------------------------------------------- diff | -.0829703 .0471318 -.1756272 .0096866 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -1.7604 Ho: diff = 0 degrees of freedom = 400 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0396 Pr(|T| > |t|) = 0.0791 Pr(T > t) = 0.9604 ************************************************ ************************************************ ************** Issue energy ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 229 .3668122 .0319169 .4829904 .3039224 .429702 better i | 189 .4074074 .0358355 .4926569 .336716 .4780988 ---------+-------------------------------------------------------------------- combined | 418 .3851675 .0238306 .487218 .3383244 .4320106 ---------+-------------------------------------------------------------------- diff | -.0405952 .0478971 -.1347458 .0535554 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.8475 Ho: diff = 0 degrees of freedom = 416 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1986 Pr(|T| > |t|) = 0.3972 Pr(T > t) = 0.8014 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 229 .3668122 .0319169 .4829904 .3039224 .429702 better q | 212 .4622642 .0343232 .499754 .3946037 .5299246 ---------+-------------------------------------------------------------------- combined | 441 .4126984 .0234704 .4928786 .3665704 .4588265 ---------+-------------------------------------------------------------------- diff | -.0954519 .046808 -.1874476 -.0034562 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -2.0392 Ho: diff = 0 degrees of freedom = 439 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0210 Pr(|T| > |t|) = 0.0420 Pr(T > t) = 0.9790 ************************************************ ************************************************ ************** Issue immig ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 188 .4414894 .0363124 .4978906 .3698547 .513124 better i | 134 .4253731 .0428699 .4962546 .3405782 .5101681 ---------+-------------------------------------------------------------------- combined | 322 .4347826 .0276689 .4965 .3803474 .4892178 ---------+-------------------------------------------------------------------- diff | .0161162 .0562131 -.0944777 .1267102 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = 0.2867 Ho: diff = 0 degrees of freedom = 320 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.6127 Pr(|T| > |t|) = 0.7745 Pr(T > t) = 0.3873 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 188 .4414894 .0363124 .4978906 .3698547 .513124 better q | 179 .5251397 .0374292 .5007684 .4512776 .5990017 ---------+-------------------------------------------------------------------- combined | 367 .4822888 .026119 .5003684 .4309267 .533651 ---------+-------------------------------------------------------------------- diff | -.0836503 .0521418 -.1861863 .0188857 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -1.6043 Ho: diff = 0 degrees of freedom = 365 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0548 Pr(|T| > |t|) = 0.1095 Pr(T > t) = 0.9452 . . . #delimit ; delimiter now ; . gen i = _n; . reshape long beval, i(i) j(topic) string; (note: j = _econ _educ _energy _health _immig _iraq _socsec _taxes _terror) Data wide -> long ----------------------------------------------------------------------------- Number of obs. 1000 -> 9000 Number of variables 80 -> 73 j variable (9 values) -> topic xij variables: beval_econ beval_educ ... beval_terror -> beval ----------------------------------------------------------------------------- . ttest beval if splitabc ~= 3, by(splitabc); Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 1984 .3946573 .0109761 .4889002 .3731313 .4161832 better i | 1643 .4035301 .0121073 .4907547 .3797828 .4272774 ---------+-------------------------------------------------------------------- combined | 3627 .3986766 .0081311 .4896935 .3827346 .4146186 ---------+-------------------------------------------------------------------- diff | -.0088729 .0163362 -.0409019 .0231562 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.5431 Ho: diff = 0 degrees of freedom = 3625 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.2935 Pr(|T| > |t|) = 0.5871 Pr(T > t) = 0.7065 . ttest beval if splitabc ~= 2, by(splitabc); Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 1984 .3946573 .0109761 .4889002 .3731313 .4161832 better q | 1866 .4790997 .0115678 .4996969 .4564125 .5017869 ---------+-------------------------------------------------------------------- combined | 3850 .4355844 .0079921 .4958977 .4199152 .4512536 ---------+-------------------------------------------------------------------- diff | -.0844424 .0159358 -.1156858 -.053199 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -5.2989 Ho: diff = 0 degrees of freedom = 3848 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000 . ************************************************************************; . * *; . * Tabulating defectors *; . * *; . ************************************************************************; . clear; . use cces_2008_prepped.dta; . table splitabc, contents(mean defect_health mean > defect_socsec mean defect_iraq mean defect_terror > mean defect_econ) format(%12.3g); --------------------------------------------------------------------------------------- prompt | randomization | ucb444-452 | mean(defe~h) mean(defe~c) mean(defe~q) mean(defe~r) mean(defe~n) -----------------+--------------------------------------------------------------------- better job | .162 .189 .173 .287 .173 better ideas | .196 .237 .157 .255 .235 better qualified | .262 .283 .197 .277 .261 --------------------------------------------------------------------------------------- . table splitabc, contents(mean defect_taxes mean > defect_educ mean defect_energy mean defect_immig) > format(%12.3g); ------------------------------------------------------------------------- prompt | randomization | ucb444-452 | mean(defe~s) mean(defe~c) mean(defe~y) mean(defe~g) -----------------+------------------------------------------------------- better job | .138 .276 .198 .35 better ideas | .196 .307 .229 .375 better qualified | .23 .302 .214 .404 ------------------------------------------------------------------------- . #delimit cr delimiter now cr . . local evars health socsec iraq terror econ taxes educ energy immig . . while "`evars'" ~="" { 2. . gettoken e evars : evars 3. . di "************************************************" 4. di "************************************************" 5. di "************** Issue `e' *************************" 6. di "************************************************" 7. di "************************************************" 8. . ttest defect_`e' if splitabc ~= 3, by(splitabc) 9. ttest defect_`e' if splitabc ~= 2, by(splitabc) 10. . . } ************************************************ ************************************************ ************** Issue health ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 197 .1624365 .0263465 .3697906 .1104776 .2143955 better i | 153 .1960784 .0322033 .3983324 .1324546 .2597022 ---------+-------------------------------------------------------------------- combined | 350 .1771429 .0204367 .3823361 .1369482 .2173375 ---------+-------------------------------------------------------------------- diff | -.0336419 .04122 -.1147136 .0474299 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.8162 Ho: diff = 0 degrees of freedom = 348 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.2075 Pr(|T| > |t|) = 0.4150 Pr(T > t) = 0.7925 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 197 .1624365 .0263465 .3697906 .1104776 .2143955 better q | 183 .2622951 .0326062 .441089 .1979602 .3266299 ---------+-------------------------------------------------------------------- combined | 380 .2105263 .0209412 .4082199 .1693507 .2517019 ---------+-------------------------------------------------------------------- diff | -.0998585 .0416508 -.1817549 -.0179622 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -2.3975 Ho: diff = 0 degrees of freedom = 378 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0085 Pr(|T| > |t|) = 0.0170 Pr(T > t) = 0.9915 ************************************************ ************************************************ ************** Issue socsec ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 196 .1887755 .0280237 .3923323 .133507 .244044 better i | 152 .2368421 .0345978 .42655 .1684839 .3052004 ---------+-------------------------------------------------------------------- combined | 348 .2097701 .0218567 .4077307 .1667819 .2527583 ---------+-------------------------------------------------------------------- diff | -.0480666 .0440549 -.1347157 .0385825 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -1.0911 Ho: diff = 0 degrees of freedom = 346 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1380 Pr(|T| > |t|) = 0.2760 Pr(T > t) = 0.8620 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 196 .1887755 .0280237 .3923323 .133507 .244044 better q | 184 .2826087 .0332847 .4514963 .2169375 .3482799 ---------+-------------------------------------------------------------------- combined | 380 .2342105 .021754 .4240627 .191437 .2769841 ---------+-------------------------------------------------------------------- diff | -.0938332 .0433191 -.1790099 -.0086565 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -2.1661 Ho: diff = 0 degrees of freedom = 378 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0155 Pr(|T| > |t|) = 0.0309 Pr(T > t) = 0.9845 ************************************************ ************************************************ ************** Issue iraq ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 197 .1725888 .0269922 .3788542 .1193563 .2258214 better i | 153 .1568627 .0294976 .3648656 .0985844 .2151411 ---------+-------------------------------------------------------------------- combined | 350 .1657143 .0199033 .3723563 .1265688 .2048598 ---------+-------------------------------------------------------------------- diff | .0157261 .0401737 -.0632876 .0947398 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = 0.3915 Ho: diff = 0 degrees of freedom = 348 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.6521 Pr(|T| > |t|) = 0.6957 Pr(T > t) = 0.3479 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 197 .1725888 .0269922 .3788542 .1193563 .2258214 better q | 183 .1967213 .0294661 .3986104 .1385822 .2548605 ---------+-------------------------------------------------------------------- combined | 380 .1842105 .0199125 .3881669 .1450576 .2233634 ---------+-------------------------------------------------------------------- diff | -.0241325 .0398855 -.1025577 .0542928 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -0.6050 Ho: diff = 0 degrees of freedom = 378 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.2728 Pr(|T| > |t|) = 0.5455 Pr(T > t) = 0.7272 ************************************************ ************************************************ ************** Issue terror ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 195 .2871795 .0324837 .4536107 .2231129 .3512461 better i | 153 .254902 .0353485 .4372373 .1850641 .3247399 ---------+-------------------------------------------------------------------- combined | 348 .2729885 .0239154 .4461364 .2259511 .3200259 ---------+-------------------------------------------------------------------- diff | .0322775 .0482214 -.0625665 .1271215 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = 0.6694 Ho: diff = 0 degrees of freedom = 346 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.7481 Pr(|T| > |t|) = 0.5037 Pr(T > t) = 0.2519 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 195 .2871795 .0324837 .4536107 .2231129 .3512461 better q | 184 .2771739 .0330878 .4488244 .2118913 .3424565 ---------+-------------------------------------------------------------------- combined | 379 .2823219 .0231521 .4507242 .2367988 .327845 ---------+-------------------------------------------------------------------- diff | .0100056 .0463823 -.0811949 .1012061 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = 0.2157 Ho: diff = 0 degrees of freedom = 377 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.5853 Pr(|T| > |t|) = 0.8293 Pr(T > t) = 0.4147 ************************************************ ************************************************ ************** Issue econ ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 196 .1734694 .0271159 .3796222 .1199914 .2269474 better i | 153 .2352941 .0344057 .4255756 .1673189 .3032693 ---------+-------------------------------------------------------------------- combined | 349 .2005731 .0214653 .4010041 .1583551 .242791 ---------+-------------------------------------------------------------------- diff | -.0618247 .0431951 -.1467819 .0231324 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -1.4313 Ho: diff = 0 degrees of freedom = 347 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0766 Pr(|T| > |t|) = 0.1532 Pr(T > t) = 0.9234 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 196 .1734694 .0271159 .3796222 .1199914 .2269474 better q | 184 .2608696 .0324599 .440307 .1968259 .3249133 ---------+-------------------------------------------------------------------- combined | 380 .2157895 .0211306 .4119113 .1742416 .2573374 ---------+-------------------------------------------------------------------- diff | -.0874002 .0420989 -.1701774 -.0046229 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -2.0761 Ho: diff = 0 degrees of freedom = 378 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0193 Pr(|T| > |t|) = 0.0386 Pr(T > t) = 0.9807 ************************************************ ************************************************ ************** Issue taxes ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 195 .1384615 .0247971 .3462728 .089555 .1873681 better i | 153 .1960784 .0322033 .3983324 .1324546 .2597022 ---------+-------------------------------------------------------------------- combined | 348 .1637931 .0198673 .3706206 .1247175 .2028687 ---------+-------------------------------------------------------------------- diff | -.0576169 .0399652 -.1362223 .0209885 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -1.4417 Ho: diff = 0 degrees of freedom = 346 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0751 Pr(|T| > |t|) = 0.1503 Pr(T > t) = 0.9249 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 195 .1384615 .0247971 .3462728 .089555 .1873681 better q | 183 .2295082 .0311708 .4216703 .1680057 .2910107 ---------+-------------------------------------------------------------------- combined | 378 .1825397 .0198949 .3868007 .1434209 .2216585 ---------+-------------------------------------------------------------------- diff | -.0910467 .0395852 -.1688828 -.0132105 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -2.3000 Ho: diff = 0 degrees of freedom = 376 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0110 Pr(|T| > |t|) = 0.0220 Pr(T > t) = 0.9890 ************************************************ ************************************************ ************** Issue educ ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 196 .2755102 .0319939 .4479151 .2124116 .3386088 better i | 153 .3071895 .0374187 .4628437 .2332617 .3811174 ---------+-------------------------------------------------------------------- combined | 349 .2893983 .0243092 .4541342 .2415868 .3372098 ---------+-------------------------------------------------------------------- diff | -.0316793 .0490328 -.1281183 .0647596 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.6461 Ho: diff = 0 degrees of freedom = 347 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.2593 Pr(|T| > |t|) = 0.5187 Pr(T > t) = 0.7407 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 196 .2755102 .0319939 .4479151 .2124116 .3386088 better q | 182 .3021978 .0341328 .4604773 .2348483 .3695473 ---------+-------------------------------------------------------------------- combined | 378 .2883598 .0233307 .4535998 .2424853 .3342343 ---------+-------------------------------------------------------------------- diff | -.0266876 .0467351 -.1185826 .0652074 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -0.5710 Ho: diff = 0 degrees of freedom = 376 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.2842 Pr(|T| > |t|) = 0.5683 Pr(T > t) = 0.7158 ************************************************ ************************************************ ************** Issue energy ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 197 .1979695 .0284621 .3994843 .1418383 .2541008 better i | 153 .2287582 .0340692 .4214126 .1614479 .2960685 ---------+-------------------------------------------------------------------- combined | 350 .2114286 .021857 .4089062 .1684407 .2544165 ---------+-------------------------------------------------------------------- diff | -.0307886 .0440959 -.1175166 .0559393 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.6982 Ho: diff = 0 degrees of freedom = 348 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.2428 Pr(|T| > |t|) = 0.4855 Pr(T > t) = 0.7572 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 197 .1979695 .0284621 .3994843 .1418383 .2541008 better q | 182 .2142857 .0304993 .4114578 .1541058 .2744656 ---------+-------------------------------------------------------------------- combined | 379 .2058047 .0207944 .4048229 .1649176 .2466918 ---------+-------------------------------------------------------------------- diff | -.0163162 .041668 -.098247 .0656147 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -0.3916 Ho: diff = 0 degrees of freedom = 377 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.3478 Pr(|T| > |t|) = 0.6956 Pr(T > t) = 0.6522 ************************************************ ************************************************ ************** Issue immig ************************* ************************************************ ************************************************ Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 197 .3502538 .034075 .4782648 .2830532 .4174544 better i | 152 .375 .0393974 .4857233 .2971587 .4528413 ---------+-------------------------------------------------------------------- combined | 349 .3610315 .0257468 .4809892 .3103927 .4116704 ---------+-------------------------------------------------------------------- diff | -.0247462 .0519848 -.1269911 .0774987 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.4760 Ho: diff = 0 degrees of freedom = 347 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.3172 Pr(|T| > |t|) = 0.6344 Pr(T > t) = 0.6828 Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 197 .3502538 .034075 .4782648 .2830532 .4174544 better q | 183 .4043716 .0363783 .4921164 .3325942 .476149 ---------+-------------------------------------------------------------------- combined | 380 .3763158 .0248851 .4850995 .3273857 .4252459 ---------+-------------------------------------------------------------------- diff | -.0541178 .0497921 -.1520219 .0437863 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -1.0869 Ho: diff = 0 degrees of freedom = 378 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.1389 Pr(|T| > |t|) = 0.2778 Pr(T > t) = 0.8611 . . . #delimit ; delimiter now ; . gen i = _n; . reshape long defect, i(i) j(topic) string; (note: j = _econ _educ _energy _health _immig _iraq _socsec _taxes _terror) Data wide -> long ----------------------------------------------------------------------------- Number of obs. 1000 -> 9000 Number of variables 80 -> 73 j variable (9 values) -> topic xij variables: defect_econ defect_educ ... defect_terror -> defect ----------------------------------------------------------------------------- . ttest defect if splitabc ~= 3, by(splitabc); Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 1766 .216308 .0098002 .4118433 .1970867 .2355293 better i | 1375 .2429091 .0115692 .4289966 .2202139 .2656043 ---------+-------------------------------------------------------------------- combined | 3141 .2279529 .0074865 .4195789 .2132739 .2426318 ---------+-------------------------------------------------------------------- diff | -.0266011 .0150853 -.0561792 .0029771 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -1.7634 Ho: diff = 0 degrees of freedom = 3139 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0390 Pr(|T| > |t|) = 0.0779 Pr(T > t) = 0.9610 . ttest defect if splitabc ~= 2, by(splitabc); Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 1766 .216308 .0098002 .4118433 .1970867 .2355293 better q | 1648 .2700243 .0109398 .4441068 .2485669 .2914816 ---------+-------------------------------------------------------------------- combined | 3414 .2422378 .0073336 .4285002 .2278591 .2566166 ---------+-------------------------------------------------------------------- diff | -.0537162 .0146494 -.0824386 -.0249938 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -3.6668 Ho: diff = 0 degrees of freedom = 3412 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0001 Pr(|T| > |t|) = 0.0002 Pr(T > t) = 0.9999 . ************************************************************************; . * *; . * Creating overall binary eval variables *; . * *; . ************************************************************************; . clear; . use cces_2008_prepped.dta; . fvset base 3 beh_pid_7pt; . fvset base 1 beh_pid_3pt_3way; . fvset base 3 beh_ideology; . fvset base 1 splitabc; . keep beh* dem* con* beval* weight splitabc; . gen i = _n; . reshape long beval, i(i) j(topic) string; (note: j = _econ _educ _energy _health _immig _iraq _socsec _taxes _terror) Data wide -> long ----------------------------------------------------------------------------- Number of obs. 1000 -> 9000 Number of variables 50 -> 43 j variable (9 values) -> topic xij variables: beval_econ beval_educ ... beval_terror -> beval ----------------------------------------------------------------------------- . encode topic, generate(issue); . fvset base 9 issue; . ************************************************************************; . * *; . * Rerunning overall binary tabulations to see sample sizes *; . * *; . ************************************************************************; . table splitabc, contents(mean beval) format(%12.3g); ------------------------------- prompt | randomization | ucb444-452 | mean(beval) -----------------+------------- better job | .395 better ideas | .404 better qualified | .479 ------------------------------- . gen temp_b = (beval ~= .); . table splitabc, contents(sum temp_b) by(issue); ------------------------------ issue and prompt | randomization | ucb444-452 | sum(temp_b) -----------------+------------ _econ | better job | 227 better ideas | 187 better qualified | 208 -----------------+------------ _educ | better job | 202 better ideas | 166 better qualified | 200 -----------------+------------ _energy | better job | 229 better ideas | 189 better qualified | 212 -----------------+------------ _health | better job | 233 better ideas | 197 better qualified | 211 -----------------+------------ _immig | better job | 188 better ideas | 134 better qualified | 179 -----------------+------------ _iraq | better job | 236 better ideas | 207 better qualified | 229 -----------------+------------ _socsec | better job | 219 better ideas | 183 better qualified | 199 -----------------+------------ _taxes | better job | 239 better ideas | 195 better qualified | 217 -----------------+------------ _terror | better job | 211 better ideas | 185 better qualified | 211 ------------------------------ . replace temp_b = (temp_b / 9); (5493 real changes made) . table splitabc, contents(sum temp_b); ------------------------------ prompt | randomization | ucb444-452 | sum(temp_b) -----------------+------------ better job | 220.4444 better ideas | 182.5556 better qualified | 207.3333 ------------------------------ . drop temp_b; . table beh_pid_3pt_3way, contents(mean beval) by(splitabc); ------------------------------ prompt | randomization | ucb444-452 and | beh_pid_3pt_3way | mean(beval) -----------------+------------ better job | 0 | .0789744 1 | .4453441 2 | .9436893 -----------------+------------ better ideas | 0 | .0614934 1 | .4266409 2 | .9049774 -----------------+------------ better qualified | 0 | .0830769 1 | .4283088 2 | .9032738 ------------------------------ . ttest beval if splitabc ~= 3, by(splitabc); Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 1984 .3946573 .0109761 .4889002 .3731313 .4161832 better i | 1643 .4035301 .0121073 .4907547 .3797828 .4272774 ---------+-------------------------------------------------------------------- combined | 3627 .3986766 .0081311 .4896935 .3827346 .4146186 ---------+-------------------------------------------------------------------- diff | -.0088729 .0163362 -.0409019 .0231562 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better i) t = -0.5431 Ho: diff = 0 degrees of freedom = 3625 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.2935 Pr(|T| > |t|) = 0.5871 Pr(T > t) = 0.7065 . ttest beval if splitabc ~= 2, by(splitabc); Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- better j | 1984 .3946573 .0109761 .4889002 .3731313 .4161832 better q | 1866 .4790997 .0115678 .4996969 .4564125 .5017869 ---------+-------------------------------------------------------------------- combined | 3850 .4355844 .0079921 .4958977 .4199152 .4512536 ---------+-------------------------------------------------------------------- diff | -.0844424 .0159358 -.1156858 -.053199 ------------------------------------------------------------------------------ diff = mean(better j) - mean(better q) t = -5.2989 Ho: diff = 0 degrees of freedom = 3848 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(T < t) = 0.0000 Pr(|T| > |t|) = 0.0000 Pr(T > t) = 1.0000 . ************************************************************************; . * *; . * Running tests (all issues, binary, pooled treatments) *; . * *; . ************************************************************************; . ***BASIC SEs***; . logit beval i.splitabc c.beh_ideology c.beh_ideology#i.splitabc > i.beh_pid_3pt_3way i.beh_pid_3pt_3way#i.splitabc > i.issue i.issue#2.splitabc i.issue#3.splitabc; Iteration 0: log likelihood = -3747.0712 Iteration 1: log likelihood = -1660.0938 Iteration 2: log likelihood = -1612.6291 Iteration 3: log likelihood = -1608.8658 Iteration 4: log likelihood = -1608.8617 Iteration 5: log likelihood = -1608.8617 Logistic regression Number of obs = 5493 LR chi2(35) = 4276.42 Prob > chi2 = 0.0000 Log likelihood = -1608.8617 Pseudo R2 = 0.5706 ------------------------------------------------------------------------------------------- beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------------------+---------------------------------------------------------------- splitabc | 2 | -1.83336 .6348448 -2.89 0.004 -3.077633 -.5890873 3 | -.1123515 .5668876 -0.20 0.843 -1.223431 .9987278 | beh_ideology | 1.448863 .1099122 13.18 0.000 1.23344 1.664287 | splitabc#c.beh_ideology | 2 | .5151716 .1768791 2.91 0.004 .168495 .8618483 3 | .0829746 .1557988 0.53 0.594 -.2223854 .3883346 | beh_pid_3pt_3way | 0 | -1.921091 .1709325 -11.24 0.000 -2.256112 -1.586069 2 | 2.144501 .2329242 9.21 0.000 1.687978 2.601024 | beh_pid_3pt_3way#splitabc | 0 2 | -.3685947 .2800886 -1.32 0.188 -.9175582 .1803688 0 3 | -.0723017 .2570982 -0.28 0.779 -.5762048 .4316015 2 2 | -.5189975 .3166214 -1.64 0.101 -1.139564 .101569 2 3 | -.5841068 .2997076 -1.95 0.051 -1.171523 .0033093 | issue | 1 | -1.629768 .327857 -4.97 0.000 -2.272355 -.9871796 2 | -1.628939 .3417837 -4.77 0.000 -2.298823 -.959055 3 | -1.374706 .3238507 -4.24 0.000 -2.009442 -.7399707 4 | -1.671978 .3265424 -5.12 0.000 -2.31199 -1.031967 5 | -.4719742 .326488 -1.45 0.148 -1.111879 .1679306 6 | -.6471018 .3094755 -2.09 0.037 -1.253663 -.0405409 7 | -1.529519 .3298498 -4.64 0.000 -2.176012 -.8830252 8 | -.9681307 .3126229 -3.10 0.002 -1.58086 -.3554012 | issue#splitabc | 1 2 | -.0923282 .4861687 -0.19 0.849 -1.045201 .8605449 1 3 | -.0022736 .4591111 -0.00 0.996 -.9021149 .8975677 2 2 | -.0301375 .5052978 -0.06 0.952 -1.020503 .960228 2 3 | -.3850497 .4740954 -0.81 0.417 -1.31426 .5441602 3 2 | .1662178 .4778145 0.35 0.728 -.7702814 1.102717 3 3 | .021149 .4556212 0.05 0.963 -.8718522 .9141501 4 2 | -.0958833 .4824635 -0.20 0.842 -1.041494 .8497278 4 3 | -.381241 .4606525 -0.83 0.408 -1.284103 .5216214 5 2 | -.2995381 .5140134 -0.58 0.560 -1.306986 .7079097 5 3 | -.0755417 .4589982 -0.16 0.869 -.9751616 .8240782 6 2 | -.0930845 .4596517 -0.20 0.840 -.9939853 .8078163 6 3 | -.0653011 .437227 -0.15 0.881 -.9222504 .7916481 7 2 | -.2069131 .4896369 -0.42 0.673 -1.166584 .7527575 7 3 | -.2905286 .4652619 -0.62 0.532 -1.202425 .6213679 8 2 | -.0169399 .4676343 -0.04 0.971 -.9334863 .8996064 8 3 | .1332045 .4432097 0.30 0.764 -.7354704 1.001879 | _cons | -3.619724 .4019758 -9.00 0.000 -4.407582 -2.831866 ------------------------------------------------------------------------------------------- . testparm i(1/8).issue; ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 chi2( 8) = 52.25 Prob > chi2 = 0.0000 . testparm i(1/8).issue i(1/8).issue#2.splitabc; ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 ( 9) [beval]1.issue#2.splitabc = 0 (10) [beval]2.issue#2.splitabc = 0 (11) [beval]3.issue#2.splitabc = 0 (12) [beval]4.issue#2.splitabc = 0 (13) [beval]5.issue#2.splitabc = 0 (14) [beval]6.issue#2.splitabc = 0 (15) [beval]7.issue#2.splitabc = 0 (16) [beval]8.issue#2.splitabc = 0 chi2( 16) = 96.20 Prob > chi2 = 0.0000 . testparm i(1/8).issue i(1/8).issue#3.splitabc; ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 ( 9) [beval]1.issue#3.splitabc = 0 (10) [beval]2.issue#3.splitabc = 0 (11) [beval]3.issue#3.splitabc = 0 (12) [beval]4.issue#3.splitabc = 0 (13) [beval]5.issue#3.splitabc = 0 (14) [beval]6.issue#3.splitabc = 0 (15) [beval]7.issue#3.splitabc = 0 (16) [beval]8.issue#3.splitabc = 0 chi2( 16) = 126.77 Prob > chi2 = 0.0000 . epcp; Displaying classification results for logit Dependent variable: beval Using cutoff score of .5 Classification table Predicted | Actual values of values of | beval beval | 0 1 | Total -----------+----------------------+---------- 0 | 2,865 323 | 3,188 1 | 288 2,017 | 2,305 -----------+----------------------+---------- Total | 3,153 2,340 | 5,493 Percent Correctly Predicted = 88.88% Percent in Modal Category = 57.40% Proportional Reduction in Error = 73.89% expected PCP = 82.52% expected PMC = 51.10% expected PRE = 64.26% . ***CLUSTERED SEs***; . logit beval i.splitabc c.beh_ideology c.beh_ideology#i.splitabc > i.beh_pid_3pt_3way i.beh_pid_3pt_3way#i.splitabc > i.issue i.issue#2.splitabc i.issue#3.splitabc > , vce(cluster i); Iteration 0: log pseudolikelihood = -3747.0712 Iteration 1: log pseudolikelihood = -1660.0938 Iteration 2: log pseudolikelihood = -1612.6291 Iteration 3: log pseudolikelihood = -1608.8658 Iteration 4: log pseudolikelihood = -1608.8617 Iteration 5: log pseudolikelihood = -1608.8617 Logistic regression Number of obs = 5493 Wald chi2(35) = 539.42 Prob > chi2 = 0.0000 Log pseudolikelihood = -1608.8617 Pseudo R2 = 0.5706 (Std. Err. adjusted for 753 clusters in i) ------------------------------------------------------------------------------------------- | Robust beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------------------+---------------------------------------------------------------- splitabc | 2 | -1.83336 1.333946 -1.37 0.169 -4.447847 .7811263 3 | -.1123515 1.105292 -0.10 0.919 -2.278685 2.053982 | beh_ideology | 1.448863 .2296045 6.31 0.000 .9988468 1.89888 | splitabc#c.beh_ideology | 2 | .5151716 .4016599 1.28 0.200 -.2720674 1.302411 3 | .0829746 .3446512 0.24 0.810 -.5925294 .7584785 | beh_pid_3pt_3way | 0 | -1.921091 .3786549 -5.07 0.000 -2.66324 -1.178941 2 | 2.144501 .5013057 4.28 0.000 1.16196 3.127042 | beh_pid_3pt_3way#splitabc | 0 2 | -.3685947 .5738097 -0.64 0.521 -1.493241 .7560517 0 3 | -.0723017 .546459 -0.13 0.895 -1.143342 .9987382 2 2 | -.5189975 .6824956 -0.76 0.447 -1.856664 .8186692 2 3 | -.5841068 .6575593 -0.89 0.374 -1.872899 .7046858 | issue | 1 | -1.629768 .211244 -7.72 0.000 -2.043798 -1.215737 2 | -1.628939 .2512273 -6.48 0.000 -2.121335 -1.136542 3 | -1.374706 .2378615 -5.78 0.000 -1.840906 -.9085063 4 | -1.671978 .254264 -6.58 0.000 -2.170326 -1.17363 5 | -.4719742 .2472863 -1.91 0.056 -.9566464 .012698 6 | -.6471018 .1805305 -3.58 0.000 -1.000935 -.2932685 7 | -1.529519 .2300259 -6.65 0.000 -1.980361 -1.078676 8 | -.9681307 .2073441 -4.67 0.000 -1.374518 -.5617437 | issue#splitabc | 1 2 | -.0923282 .3334693 -0.28 0.782 -.7459161 .5612597 1 3 | -.0022736 .3034789 -0.01 0.994 -.5970814 .5925342 2 2 | -.0301375 .3934158 -0.08 0.939 -.8012182 .7409432 2 3 | -.3850497 .3647697 -1.06 0.291 -1.099985 .3298858 3 2 | .1662178 .3602822 0.46 0.645 -.5399224 .872358 3 3 | .021149 .3331434 0.06 0.949 -.6318002 .6740981 4 2 | -.0958833 .382326 -0.25 0.802 -.8452286 .653462 4 3 | -.381241 .3534978 -1.08 0.281 -1.074084 .311602 5 2 | -.2995381 .4446874 -0.67 0.501 -1.171109 .5720332 5 3 | -.0755417 .3609636 -0.21 0.834 -.7830173 .6319339 6 2 | -.0930845 .2656397 -0.35 0.726 -.6137286 .4275597 6 3 | -.0653011 .2543235 -0.26 0.797 -.5637661 .4331638 7 2 | -.2069131 .3649583 -0.57 0.571 -.9222182 .508392 7 3 | -.2905286 .3382317 -0.86 0.390 -.9534505 .3723933 8 2 | -.0169399 .3449457 -0.05 0.961 -.693021 .6591412 8 3 | .1332045 .3101526 0.43 0.668 -.4746834 .7410925 | _cons | -3.619724 .7239431 -5.00 0.000 -5.038627 -2.200822 ------------------------------------------------------------------------------------------- . testparm i(1/8).issue; ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 chi2( 8) = 81.39 Prob > chi2 = 0.0000 . testparm i(1/8).issue i(1/8).issue#2.splitabc; ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 ( 9) [beval]1.issue#2.splitabc = 0 (10) [beval]2.issue#2.splitabc = 0 (11) [beval]3.issue#2.splitabc = 0 (12) [beval]4.issue#2.splitabc = 0 (13) [beval]5.issue#2.splitabc = 0 (14) [beval]6.issue#2.splitabc = 0 (15) [beval]7.issue#2.splitabc = 0 (16) [beval]8.issue#2.splitabc = 0 chi2( 16) = 151.17 Prob > chi2 = 0.0000 . testparm i(1/8).issue i(1/8).issue#3.splitabc; ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 ( 9) [beval]1.issue#3.splitabc = 0 (10) [beval]2.issue#3.splitabc = 0 (11) [beval]3.issue#3.splitabc = 0 (12) [beval]4.issue#3.splitabc = 0 (13) [beval]5.issue#3.splitabc = 0 (14) [beval]6.issue#3.splitabc = 0 (15) [beval]7.issue#3.splitabc = 0 (16) [beval]8.issue#3.splitabc = 0 chi2( 16) = 189.75 Prob > chi2 = 0.0000 . epcp; Displaying classification results for logit Dependent variable: beval Using cutoff score of .5 Classification table Predicted | Actual values of values of | beval beval | 0 1 | Total -----------+----------------------+---------- 0 | 2,865 323 | 3,188 1 | 288 2,017 | 2,305 -----------+----------------------+---------- Total | 3,153 2,340 | 5,493 Percent Correctly Predicted = 88.88% Percent in Modal Category = 57.40% Proportional Reduction in Error = 73.89% expected PCP = 82.52% expected PMC = 51.10% expected PRE = 64.26% . ************************************************************************; . * *; . * Running tests (all issues, binary, by treatment) *; . * *; . ************************************************************************; . #delimit cr delimiter now cr . . local a 1 . . while `a' <= 3 { 2. . di "" 3. di "" 4. di "" 5. di "************************************************" 6. di "************************************************" 7. di "************** TREATMENT `a' *********************" 8. di "************************************************" 9. di "************************************************" 10. di "" 11. di "************************************************" 12. di "************** BASIC SEs ***********************" 13. di "************************************************" 14. di "" 15. di "************************************************" 16. di "************** No FEs **************************" 17. di "************************************************" 18. . logit beval beh_ideology i.beh_pid_3pt_3way /// > if splitabc == `a' 19. qui estimates store treatment`a'_nofixed 20. . di "" 21. di "************************************************" 22. di "************** With FEs **************************" 23. di "************************************************" 24. . logit beval beh_ideology i.beh_pid_3pt_3way /// > i.issue /// > if splitabc == `a' 25. qui estimates store treatment`a'_withfixed 26. epcp 27. . lrtest treatment`a'_nofixed treatment`a'_withfixed, force 28. . testparm i(1/8).issue 29. . margins, at(beh_ideology = (1(1)5) beh_pid_3pt_3way = 1) 30. margins, at(issue = (1(1)9) beh_ideology = 3 beh_pid_3pt_3way = 1) 31. margins, at(beh_pid_3pt_3way = (0 1 2) beh_ideology = 3) 32. . di "" 33. di "************************************************" 34. di "************** CLUSTERED SEs *******************" 35. di "************************************************" 36. di "" 37. di "************************************************" 38. di "************** No FEs **************************" 39. di "************************************************" 40. . logit beval beh_ideology i.beh_pid_3pt_3way /// > if splitabc == `a' /// > , vce(cluster i) 41. qui estimates store treatment`a'_nofixed 42. . di "" 43. di "************************************************" 44. di "************** With FEs **************************" 45. di "************************************************" 46. . logit beval beh_ideology i.beh_pid_3pt_3way /// > i.issue /// > if splitabc == `a' /// > , vce(cluster i) 47. qui estimates store treatment`a'_withfixed 48. epcp 49. . lrtest treatment`a'_nofixed treatment`a'_withfixed, force 50. . testparm i(1/8).issue 51. . margins, at(beh_ideology = (1(1)5) beh_pid_3pt_3way = 1) 52. margins, at(issue = (1(1)9) beh_ideology = 3 beh_pid_3pt_3way = 1) 53. margins, at(beh_pid_3pt_3way = (0 1 2) beh_ideology = 3) 54. . local ++a 55. . } ************************************************ ************************************************ ************** TREATMENT 1 ********************* ************************************************ ************************************************ ************************************************ ************** BASIC SEs *********************** ************************************************ ************************************************ ************** No FEs ************************** ************************************************ Iteration 0: log likelihood = -1330.8391 Iteration 1: log likelihood = -608.56633 Iteration 2: log likelihood = -597.70808 Iteration 3: log likelihood = -595.09192 Iteration 4: log likelihood = -595.08793 Iteration 5: log likelihood = -595.08793 Logistic regression Number of obs = 1984 LR chi2(3) = 1471.50 Prob > chi2 = 0.0000 Log likelihood = -595.08793 Pseudo R2 = 0.5528 ---------------------------------------------------------------------------------- beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.409311 .1069317 13.18 0.000 1.199729 1.618893 | beh_pid_3pt_3way | 0 | -1.838495 .1646668 -11.16 0.000 -2.161236 -1.515754 2 | 2.071391 .2281856 9.08 0.000 1.624156 2.518627 | _cons | -4.611814 .3491738 -13.21 0.000 -5.296182 -3.927446 ---------------------------------------------------------------------------------- ************************************************ ************** With FEs ************************** ************************************************ Iteration 0: log likelihood = -1330.8391 Iteration 1: log likelihood = -582.35622 Iteration 2: log likelihood = -569.23932 Iteration 3: log likelihood = -567.58234 Iteration 4: log likelihood = -567.5818 Iteration 5: log likelihood = -567.5818 Logistic regression Number of obs = 1984 LR chi2(11) = 1526.51 Prob > chi2 = 0.0000 Log likelihood = -567.5818 Pseudo R2 = 0.5735 ---------------------------------------------------------------------------------- beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.448864 .1099122 13.18 0.000 1.23344 1.664288 | beh_pid_3pt_3way | 0 | -1.921091 .1709325 -11.24 0.000 -2.256112 -1.586069 2 | 2.144501 .2329242 9.21 0.000 1.687978 2.601024 | issue | 1 | -1.629768 .327857 -4.97 0.000 -2.272356 -.9871797 2 | -1.628939 .3417838 -4.77 0.000 -2.298823 -.9590551 3 | -1.374706 .3238507 -4.24 0.000 -2.009442 -.7399708 4 | -1.671978 .3265425 -5.12 0.000 -2.31199 -1.031967 5 | -.4719743 .3264881 -1.45 0.148 -1.111879 .1679305 6 | -.6471019 .3094755 -2.09 0.037 -1.253663 -.040541 7 | -1.529519 .3298498 -4.64 0.000 -2.176013 -.8830253 8 | -.9681309 .3126229 -3.10 0.002 -1.58086 -.3554013 | _cons | -3.619725 .4019758 -9.00 0.000 -4.407583 -2.831867 ---------------------------------------------------------------------------------- Displaying classification results for logit Dependent variable: beval Using cutoff score of .5 Classification table Predicted | Actual values of values of | beval beval | 0 1 | Total -----------+----------------------+---------- 0 | 1,116 135 | 1,251 1 | 85 648 | 733 -----------+----------------------+---------- Total | 1,201 783 | 1,984 Percent Correctly Predicted = 88.91% Percent in Modal Category = 60.53% Proportional Reduction in Error = 71.90% expected PCP = 82.99% expected PMC = 52.22% expected PRE = 64.40% Likelihood-ratio test LR chi2(8) = 55.01 (Assumption: treatment1_n~d nested in treatment1_w~d) Prob > chi2 = 0.0000 ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 chi2( 8) = 52.25 Prob > chi2 = 0.0000 Predictive margins Number of obs = 1984 Model VCE : OIM Expression : Pr(beval), predict() 1._at : beh_ideology = 1 beh_pid_3pt_3way= 1 2._at : beh_ideology = 2 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 4._at : beh_ideology = 4 beh_pid_3pt_3way= 1 5._at : beh_ideology = 5 beh_pid_3pt_3way= 1 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0419854 .0100667 4.17 0.000 .0222551 .0617157 2 | .1514546 .0199894 7.58 0.000 .1122762 .190633 3 | .4089869 .0246853 16.57 0.000 .3606046 .4573692 4 | .7284065 .0276399 26.35 0.000 .6742334 .7825797 5 | .9161092 .0176645 51.86 0.000 .8814874 .950731 ------------------------------------------------------------------------------ Adjusted predictions Number of obs = 1984 Model VCE : OIM Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 1 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 2 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 3 4._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 4 5._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 5 6._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 6 7._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 7 8._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 8 9._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 9 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .2884546 .0506553 5.69 0.000 .1891721 .3877371 2 | .2886247 .0545076 5.30 0.000 .1817919 .3954576 3 | .3434764 .0547223 6.28 0.000 .2362226 .4507301 4 | .2798688 .0494231 5.66 0.000 .1830014 .3767363 5 | .5633802 .063175 8.92 0.000 .4395594 .6872009 6 | .5199305 .0571993 9.09 0.000 .407822 .632039 7 | .3094583 .0537917 5.75 0.000 .2040284 .4148882 8 | .4399747 .0566423 7.77 0.000 .3289578 .5509915 9 | .6741172 .0529686 12.73 0.000 .5703007 .7779337 ------------------------------------------------------------------------------ Predictive margins Number of obs = 1984 Model VCE : OIM Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 0 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 2 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1019592 .0113874 8.95 0.000 .0796404 .1242781 2 | .4089869 .0246853 16.57 0.000 .3606046 .4573692 3 | .8395394 .0271245 30.95 0.000 .7863765 .8927024 ------------------------------------------------------------------------------ ************************************************ ************** CLUSTERED SEs ******************* ************************************************ ************************************************ ************** No FEs ************************** ************************************************ Iteration 0: log pseudolikelihood = -1330.8391 Iteration 1: log pseudolikelihood = -608.56633 Iteration 2: log pseudolikelihood = -597.70808 Iteration 3: log pseudolikelihood = -595.09192 Iteration 4: log pseudolikelihood = -595.08793 Iteration 5: log pseudolikelihood = -595.08793 Logistic regression Number of obs = 1984 Wald chi2(3) = 130.80 Prob > chi2 = 0.0000 Log pseudolikelihood = -595.08793 Pseudo R2 = 0.5528 (Std. Err. adjusted for 269 clusters in i) ---------------------------------------------------------------------------------- | Robust beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.409311 .2205154 6.39 0.000 .9771085 1.841513 | beh_pid_3pt_3way | 0 | -1.838495 .3668394 -5.01 0.000 -2.557487 -1.119503 2 | 2.071391 .4963049 4.17 0.000 1.098652 3.044131 | _cons | -4.611814 .7110353 -6.49 0.000 -6.005417 -3.21821 ---------------------------------------------------------------------------------- ************************************************ ************** With FEs ************************** ************************************************ Iteration 0: log pseudolikelihood = -1330.8391 Iteration 1: log pseudolikelihood = -582.35622 Iteration 2: log pseudolikelihood = -569.23932 Iteration 3: log pseudolikelihood = -567.58234 Iteration 4: log pseudolikelihood = -567.5818 Iteration 5: log pseudolikelihood = -567.5818 Logistic regression Number of obs = 1984 Wald chi2(11) = 188.93 Prob > chi2 = 0.0000 Log pseudolikelihood = -567.5818 Pseudo R2 = 0.5735 (Std. Err. adjusted for 269 clusters in i) ---------------------------------------------------------------------------------- | Robust beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.448864 .2298798 6.30 0.000 .9983076 1.89942 | beh_pid_3pt_3way | 0 | -1.921091 .3791087 -5.07 0.000 -2.66413 -1.178051 2 | 2.144501 .5019066 4.27 0.000 1.160782 3.12822 | issue | 1 | -1.629768 .2114972 -7.71 0.000 -2.044295 -1.215241 2 | -1.628939 .2515284 -6.48 0.000 -2.121926 -1.135952 3 | -1.374706 .2381466 -5.77 0.000 -1.841465 -.9079477 4 | -1.671978 .2545687 -6.57 0.000 -2.170924 -1.173033 5 | -.4719743 .2475827 -1.91 0.057 -.9572275 .0132788 6 | -.6471019 .1807469 -3.58 0.000 -1.001359 -.2928445 7 | -1.529519 .2303016 -6.64 0.000 -1.980902 -1.078136 8 | -.9681309 .2075926 -4.66 0.000 -1.375005 -.5612567 | _cons | -3.619725 .7248108 -4.99 0.000 -5.040328 -2.199122 ---------------------------------------------------------------------------------- Displaying classification results for logit Dependent variable: beval Using cutoff score of .5 Classification table Predicted | Actual values of values of | beval beval | 0 1 | Total -----------+----------------------+---------- 0 | 1,116 135 | 1,251 1 | 85 648 | 733 -----------+----------------------+---------- Total | 1,201 783 | 1,984 Percent Correctly Predicted = 88.91% Percent in Modal Category = 60.53% Proportional Reduction in Error = 71.90% expected PCP = 82.99% expected PMC = 52.22% expected PRE = 64.40% Likelihood-ratio test LR chi2(8) = 55.01 (Assumption: treatment1_n~d nested in treatment1_w~d) Prob > chi2 = 0.0000 ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 chi2( 8) = 81.19 Prob > chi2 = 0.0000 Predictive margins Number of obs = 1984 Model VCE : Robust Expression : Pr(beval), predict() 1._at : beh_ideology = 1 beh_pid_3pt_3way= 1 2._at : beh_ideology = 2 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 4._at : beh_ideology = 4 beh_pid_3pt_3way= 1 5._at : beh_ideology = 5 beh_pid_3pt_3way= 1 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0419854 .0205121 2.05 0.041 .0017823 .0821884 2 | .1514546 .0404572 3.74 0.000 .0721599 .2307492 3 | .4089869 .0516942 7.91 0.000 .3076682 .5103056 4 | .7284065 .0590986 12.33 0.000 .6125753 .8442377 5 | .9161092 .0375476 24.40 0.000 .8425172 .9897012 ------------------------------------------------------------------------------ Adjusted predictions Number of obs = 1984 Model VCE : Robust Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 1 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 2 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 3 4._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 4 5._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 5 6._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 6 7._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 7 8._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 8 9._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 9 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .2884546 .0571118 5.05 0.000 .1765175 .4003917 2 | .2886247 .0579629 4.98 0.000 .1750196 .4022298 3 | .3434764 .0617607 5.56 0.000 .2224276 .4645252 4 | .2798688 .0582094 4.81 0.000 .1657806 .3939571 5 | .5633802 .0763274 7.38 0.000 .4137812 .7129791 6 | .5199305 .0638027 8.15 0.000 .3948794 .6449815 7 | .3094583 .0601861 5.14 0.000 .1914958 .4274208 8 | .4399747 .0640515 6.87 0.000 .314436 .5655134 9 | .6741172 .0596364 11.30 0.000 .557232 .7910024 ------------------------------------------------------------------------------ Predictive margins Number of obs = 1984 Model VCE : Robust Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 0 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 2 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1019592 .0265264 3.84 0.000 .0499685 .1539499 2 | .4089869 .0516942 7.91 0.000 .3076682 .5103056 3 | .8395394 .0589623 14.24 0.000 .7239754 .9551035 ------------------------------------------------------------------------------ ************************************************ ************************************************ ************** TREATMENT 2 ********************* ************************************************ ************************************************ ************************************************ ************** BASIC SEs *********************** ************************************************ ************************************************ ************** No FEs ************************** ************************************************ Iteration 0: log likelihood = -1108.0673 Iteration 1: log likelihood = -505.07236 Iteration 2: log likelihood = -485.58831 Iteration 3: log likelihood = -481.13606 Iteration 4: log likelihood = -481.13017 Iteration 5: log likelihood = -481.13017 Logistic regression Number of obs = 1643 LR chi2(3) = 1253.87 Prob > chi2 = 0.0000 Log likelihood = -481.13017 Pseudo R2 = 0.5658 ---------------------------------------------------------------------------------- beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.907898 .134088 14.23 0.000 1.645091 2.170706 | beh_pid_3pt_3way | 0 | -2.197697 .2159659 -10.18 0.000 -2.620982 -1.774411 2 | 1.574168 .2079811 7.57 0.000 1.166533 1.981804 | _cons | -6.449303 .4434398 -14.54 0.000 -7.318429 -5.580177 ---------------------------------------------------------------------------------- ************************************************ ************** With FEs ************************** ************************************************ Iteration 0: log likelihood = -1108.0673 Iteration 1: log likelihood = -483.45504 Iteration 2: log likelihood = -460.77994 Iteration 3: log likelihood = -457.63699 Iteration 4: log likelihood = -457.635 Iteration 5: log likelihood = -457.635 Logistic regression Number of obs = 1643 LR chi2(11) = 1300.86 Prob > chi2 = 0.0000 Log likelihood = -457.635 Pseudo R2 = 0.5870 ---------------------------------------------------------------------------------- beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.964035 .138584 14.17 0.000 1.692415 2.235655 | beh_pid_3pt_3way | 0 | -2.289685 .2218821 -10.32 0.000 -2.724566 -1.854804 2 | 1.625503 .2144655 7.58 0.000 1.205159 2.045848 | issue | 1 | -1.722096 .3589843 -4.80 0.000 -2.425692 -1.018499 2 | -1.659076 .372169 -4.46 0.000 -2.388514 -.9296385 3 | -1.208489 .3513224 -3.44 0.001 -1.897068 -.5199092 4 | -1.767862 .3551634 -4.98 0.000 -2.463969 -1.071754 5 | -.7715124 .397008 -1.94 0.052 -1.549634 .0066091 6 | -.7401863 .3398597 -2.18 0.029 -1.406299 -.0740736 7 | -1.736432 .3618611 -4.80 0.000 -2.445667 -1.027197 8 | -.9850707 .3477769 -2.83 0.005 -1.666701 -.3034405 | _cons | -5.453084 .4913688 -11.10 0.000 -6.41615 -4.490019 ---------------------------------------------------------------------------------- Displaying classification results for logit Dependent variable: beval Using cutoff score of .5 Classification table Predicted | Actual values of values of | beval beval | 0 1 | Total -----------+----------------------+---------- 0 | 909 91 | 1,000 1 | 71 572 | 643 -----------+----------------------+---------- Total | 980 663 | 1,643 Percent Correctly Predicted = 90.14% Percent in Modal Category = 59.65% Proportional Reduction in Error = 75.57% expected PCP = 83.52% expected PMC = 51.86% expected PRE = 65.77% Likelihood-ratio test LR chi2(8) = 46.99 (Assumption: treatment2_n~d nested in treatment2_w~d) Prob > chi2 = 0.0000 ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 chi2( 8) = 43.95 Prob > chi2 = 0.0000 Predictive margins Number of obs = 1643 Model VCE : OIM Expression : Pr(beval), predict() 1._at : beh_ideology = 1 beh_pid_3pt_3way= 1 2._at : beh_ideology = 2 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 4._at : beh_ideology = 4 beh_pid_3pt_3way= 1 5._at : beh_ideology = 5 beh_pid_3pt_3way= 1 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0110752 .0035215 3.14 0.002 .0041731 .0179772 2 | .0720526 .0130759 5.51 0.000 .0464244 .0976808 3 | .3327888 .024032 13.85 0.000 .2856869 .3798907 4 | .7572499 .0274704 27.57 0.000 .7034089 .8110909 5 | .9549178 .0115105 82.96 0.000 .9323576 .9774779 ------------------------------------------------------------------------------ Adjusted predictions Number of obs = 1643 Model VCE : OIM Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 1 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 2 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 3 4._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 4 5._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 5 6._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 6 7._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 7 8._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 8 9._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 9 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .2170273 .0453765 4.78 0.000 .128091 .3059635 2 | .2279267 .0503597 4.53 0.000 .1292236 .3266298 3 | .3165943 .0560281 5.65 0.000 .2067812 .4264074 4 | .2093512 .0435918 4.80 0.000 .1239128 .2947896 5 | .4176345 .0782484 5.34 0.000 .2642704 .5709986 6 | .4252726 .059548 7.14 0.000 .3085606 .5419846 7 | .214601 .0459243 4.67 0.000 .1245911 .304611 8 | .3667814 .0590828 6.21 0.000 .2509811 .4825816 9 | .6080257 .0620238 9.80 0.000 .4864613 .7295901 ------------------------------------------------------------------------------ Predictive margins Number of obs = 1643 Model VCE : OIM Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 0 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 2 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0535073 .0096685 5.53 0.000 .0345574 .0724572 2 | .3327888 .024032 13.85 0.000 .2856869 .3798907 3 | .6934578 .0380718 18.21 0.000 .6188385 .768077 ------------------------------------------------------------------------------ ************************************************ ************** CLUSTERED SEs ******************* ************************************************ ************************************************ ************** No FEs ************************** ************************************************ Iteration 0: log pseudolikelihood = -1108.0673 Iteration 1: log pseudolikelihood = -505.07236 Iteration 2: log pseudolikelihood = -485.58831 Iteration 3: log pseudolikelihood = -481.13606 Iteration 4: log pseudolikelihood = -481.13017 Iteration 5: log pseudolikelihood = -481.13017 Logistic regression Number of obs = 1643 Wald chi2(3) = 98.62 Prob > chi2 = 0.0000 Log pseudolikelihood = -481.13017 Pseudo R2 = 0.5658 (Std. Err. adjusted for 223 clusters in i) ---------------------------------------------------------------------------------- | Robust beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.907898 .3199486 5.96 0.000 1.280811 2.534986 | beh_pid_3pt_3way | 0 | -2.197697 .4276064 -5.14 0.000 -3.03579 -1.359604 2 | 1.574168 .4424893 3.56 0.000 .7069053 2.441431 | _cons | -6.449303 1.102478 -5.85 0.000 -8.610121 -4.288485 ---------------------------------------------------------------------------------- ************************************************ ************** With FEs ************************** ************************************************ Iteration 0: log pseudolikelihood = -1108.0673 Iteration 1: log pseudolikelihood = -483.45504 Iteration 2: log pseudolikelihood = -460.77994 Iteration 3: log pseudolikelihood = -457.63699 Iteration 4: log pseudolikelihood = -457.635 Iteration 5: log pseudolikelihood = -457.635 Logistic regression Number of obs = 1643 Wald chi2(11) = 166.99 Prob > chi2 = 0.0000 Log pseudolikelihood = -457.635 Pseudo R2 = 0.5870 (Std. Err. adjusted for 223 clusters in i) ---------------------------------------------------------------------------------- | Robust beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.964035 .3300861 5.95 0.000 1.317078 2.610992 | beh_pid_3pt_3way | 0 | -2.289685 .4318187 -5.30 0.000 -3.136034 -1.443336 2 | 1.625503 .4638673 3.50 0.000 .7163403 2.534667 | issue | 1 | -1.722096 .2584354 -6.66 0.000 -2.22862 -1.215572 2 | -1.659076 .303235 -5.47 0.000 -2.253406 -1.064747 3 | -1.208489 .2710302 -4.46 0.000 -1.739698 -.6772792 4 | -1.767862 .2859746 -6.18 0.000 -2.328362 -1.207362 5 | -.7715124 .3701749 -2.08 0.037 -1.497042 -.0459828 6 | -.7401863 .1951757 -3.79 0.000 -1.122724 -.3576489 7 | -1.736432 .2837907 -6.12 0.000 -2.292652 -1.180212 8 | -.9850707 .2761103 -3.57 0.000 -1.526237 -.4439045 | _cons | -5.453084 1.122185 -4.86 0.000 -7.652526 -3.253643 ---------------------------------------------------------------------------------- Displaying classification results for logit Dependent variable: beval Using cutoff score of .5 Classification table Predicted | Actual values of values of | beval beval | 0 1 | Total -----------+----------------------+---------- 0 | 909 91 | 1,000 1 | 71 572 | 643 -----------+----------------------+---------- Total | 980 663 | 1,643 Percent Correctly Predicted = 90.14% Percent in Modal Category = 59.65% Proportional Reduction in Error = 75.57% expected PCP = 83.52% expected PMC = 51.86% expected PRE = 65.77% Likelihood-ratio test LR chi2(8) = 46.99 (Assumption: treatment2_n~d nested in treatment2_w~d) Prob > chi2 = 0.0000 ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 chi2( 8) = 69.56 Prob > chi2 = 0.0000 Predictive margins Number of obs = 1643 Model VCE : Robust Expression : Pr(beval), predict() 1._at : beh_ideology = 1 beh_pid_3pt_3way= 1 2._at : beh_ideology = 2 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 4._at : beh_ideology = 4 beh_pid_3pt_3way= 1 5._at : beh_ideology = 5 beh_pid_3pt_3way= 1 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0110752 .0088786 1.25 0.212 -.0063266 .0284769 2 | .0720526 .0329092 2.19 0.029 .0075517 .1365536 3 | .3327888 .052647 6.32 0.000 .2296025 .4359751 4 | .7572499 .0518863 14.59 0.000 .6555547 .8589451 5 | .9549178 .0243464 39.22 0.000 .9071997 1.002636 ------------------------------------------------------------------------------ Adjusted predictions Number of obs = 1643 Model VCE : Robust Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 1 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 2 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 3 4._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 4 5._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 5 6._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 6 7._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 7 8._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 8 9._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 9 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .2170273 .0484811 4.48 0.000 .122006 .3120485 2 | .2279267 .0566536 4.02 0.000 .1168878 .3389656 3 | .3165943 .0623178 5.08 0.000 .1944537 .4387348 4 | .2093512 .0519227 4.03 0.000 .1075846 .3111178 5 | .4176345 .0904003 4.62 0.000 .2404531 .5948159 6 | .4252726 .0707104 6.01 0.000 .2866828 .5638624 7 | .214601 .0511708 4.19 0.000 .1143082 .3148939 8 | .3667814 .0729888 5.03 0.000 .223726 .5098367 9 | .6080257 .07335 8.29 0.000 .4642623 .7517891 ------------------------------------------------------------------------------ Predictive margins Number of obs = 1643 Model VCE : Robust Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 0 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 2 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0535073 .0196206 2.73 0.006 .0150516 .091963 2 | .3327888 .052647 6.32 0.000 .2296025 .4359751 3 | .6934578 .0889083 7.80 0.000 .5192008 .8677148 ------------------------------------------------------------------------------ ************************************************ ************************************************ ************** TREATMENT 3 ********************* ************************************************ ************************************************ ************************************************ ************** BASIC SEs *********************** ************************************************ ************************************************ ************** No FEs ************************** ************************************************ Iteration 0: log likelihood = -1291.7819 Iteration 1: log likelihood = -636.03863 Iteration 2: log likelihood = -625.36655 Iteration 3: log likelihood = -624.81579 Iteration 4: log likelihood = -624.8144 Iteration 5: log likelihood = -624.8144 Logistic regression Number of obs = 1866 LR chi2(3) = 1333.94 Prob > chi2 = 0.0000 Log likelihood = -624.8144 Pseudo R2 = 0.5163 ---------------------------------------------------------------------------------- beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.470709 .1066897 13.78 0.000 1.261601 1.679817 | beh_pid_3pt_3way | 0 | -1.888672 .1829136 -10.33 0.000 -2.247176 -1.530168 2 | 1.4509 .1815214 7.99 0.000 1.095125 1.806676 | _cons | -4.732037 .3445155 -13.74 0.000 -5.407275 -4.056799 ---------------------------------------------------------------------------------- ************************************************ ************** With FEs ************************** ************************************************ Iteration 0: log likelihood = -1291.7819 Iteration 1: log likelihood = -595.03047 Iteration 2: log likelihood = -583.91375 Iteration 3: log likelihood = -583.64522 Iteration 4: log likelihood = -583.64488 Iteration 5: log likelihood = -583.64488 Logistic regression Number of obs = 1866 LR chi2(11) = 1416.27 Prob > chi2 = 0.0000 Log likelihood = -583.64488 Pseudo R2 = 0.5482 ---------------------------------------------------------------------------------- beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.531838 .11042 13.87 0.000 1.315419 1.748257 | beh_pid_3pt_3way | 0 | -1.993392 .1920457 -10.38 0.000 -2.369795 -1.61699 2 | 1.560394 .1886027 8.27 0.000 1.19074 1.930049 | issue | 1 | -1.632041 .321392 -5.08 0.000 -2.261958 -1.002124 2 | -2.013989 .328558 -6.13 0.000 -2.65795 -1.370027 3 | -1.353557 .3204863 -4.22 0.000 -1.981699 -.7254158 4 | -2.053219 .3249166 -6.32 0.000 -2.690044 -1.416394 5 | -.5475159 .3226219 -1.70 0.090 -1.179843 .0848114 6 | -.7124029 .3088566 -2.31 0.021 -1.317751 -.1070551 7 | -1.820047 .3281276 -5.55 0.000 -2.463166 -1.176929 8 | -.8349262 .3141683 -2.66 0.008 -1.450685 -.2191676 | _cons | -3.732076 .3997212 -9.34 0.000 -4.515515 -2.948637 ---------------------------------------------------------------------------------- Displaying classification results for logit Dependent variable: beval Using cutoff score of .5 Classification table Predicted | Actual values of values of | beval beval | 0 1 | Total -----------+----------------------+---------- 0 | 840 97 | 937 1 | 132 797 | 929 -----------+----------------------+---------- Total | 972 894 | 1,866 Percent Correctly Predicted = 87.73% Percent in Modal Category = 52.09% Proportional Reduction in Error = 74.38% expected PCP = 81.15% expected PMC = 50.09% expected PRE = 62.23% Likelihood-ratio test LR chi2(8) = 82.34 (Assumption: treatment3_n~d nested in treatment3_w~d) Prob > chi2 = 0.0000 ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 chi2( 8) = 74.52 Prob > chi2 = 0.0000 Predictive margins Number of obs = 1866 Model VCE : OIM Expression : Pr(beval), predict() 1._at : beh_ideology = 1 beh_pid_3pt_3way= 1 2._at : beh_ideology = 2 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 4._at : beh_ideology = 4 beh_pid_3pt_3way= 1 5._at : beh_ideology = 5 beh_pid_3pt_3way= 1 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0392612 .0092994 4.22 0.000 .0210346 .0574877 2 | .151513 .0192498 7.87 0.000 .113784 .1892419 3 | .4201879 .0233286 18.01 0.000 .3744647 .4659111 4 | .744149 .0258632 28.77 0.000 .693458 .79484 5 | .9262147 .0156318 59.25 0.000 .8955769 .9568526 ------------------------------------------------------------------------------ Adjusted predictions Number of obs = 1866 Model VCE : OIM Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 1 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 2 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 3 4._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 4 5._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 5 6._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 6 7._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 7 8._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 8 9._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 9 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .3167814 .0515486 6.15 0.000 .2157481 .4178147 2 | .2403886 .0451099 5.33 0.000 .1519748 .3288024 3 | .3798655 .0563397 6.74 0.000 .2694417 .4902893 4 | .2332981 .0433593 5.38 0.000 .1483155 .3182807 5 | .5783302 .0595787 9.71 0.000 .461558 .6951023 6 | .5376872 .0557434 9.65 0.000 .4284321 .6469424 7 | .2775576 .0494545 5.61 0.000 .1806287 .3744866 8 | .5071275 .0577133 8.79 0.000 .3940115 .6202436 9 | .7033785 .049794 14.13 0.000 .6057841 .800973 ------------------------------------------------------------------------------ Predictive margins Number of obs = 1866 Model VCE : OIM Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 0 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 2 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1034018 .0139911 7.39 0.000 .0759797 .1308238 2 | .4201879 .0233286 18.01 0.000 .3744647 .4659111 3 | .7491675 .0275204 27.22 0.000 .6952286 .8031065 ------------------------------------------------------------------------------ ************************************************ ************** CLUSTERED SEs ******************* ************************************************ ************************************************ ************** No FEs ************************** ************************************************ Iteration 0: log pseudolikelihood = -1291.7819 Iteration 1: log pseudolikelihood = -636.03863 Iteration 2: log pseudolikelihood = -625.36655 Iteration 3: log pseudolikelihood = -624.81579 Iteration 4: log pseudolikelihood = -624.8144 Iteration 5: log pseudolikelihood = -624.8144 Logistic regression Number of obs = 1866 Wald chi2(3) = 110.45 Prob > chi2 = 0.0000 Log pseudolikelihood = -624.8144 Pseudo R2 = 0.5163 (Std. Err. adjusted for 261 clusters in i) ---------------------------------------------------------------------------------- | Robust beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.470709 .2522155 5.83 0.000 .9763757 1.965042 | beh_pid_3pt_3way | 0 | -1.888672 .3742488 -5.05 0.000 -2.622186 -1.155158 2 | 1.4509 .410296 3.54 0.000 .6467351 2.255066 | _cons | -4.732037 .8077787 -5.86 0.000 -6.315254 -3.14882 ---------------------------------------------------------------------------------- ************************************************ ************** With FEs ************************** ************************************************ Iteration 0: log pseudolikelihood = -1291.7819 Iteration 1: log pseudolikelihood = -595.03047 Iteration 2: log pseudolikelihood = -583.91375 Iteration 3: log pseudolikelihood = -583.64522 Iteration 4: log pseudolikelihood = -583.64488 Iteration 5: log pseudolikelihood = -583.64488 Logistic regression Number of obs = 1866 Wald chi2(11) = 181.96 Prob > chi2 = 0.0000 Log pseudolikelihood = -583.64488 Pseudo R2 = 0.5482 (Std. Err. adjusted for 261 clusters in i) ---------------------------------------------------------------------------------- | Robust beval | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------+---------------------------------------------------------------- beh_ideology | 1.531838 .2573562 5.95 0.000 1.027429 2.036247 | beh_pid_3pt_3way | 0 | -1.993392 .3944972 -5.05 0.000 -2.766592 -1.220192 2 | 1.560394 .4260656 3.66 0.000 .7253209 2.395467 | issue | 1 | -1.632041 .2181622 -7.48 0.000 -2.059631 -1.204451 2 | -2.013989 .2647972 -7.61 0.000 -2.532982 -1.494996 3 | -1.353557 .2335448 -5.80 0.000 -1.811297 -.895818 4 | -2.053219 .2458904 -8.35 0.000 -2.535156 -1.571283 5 | -.5475159 .263283 -2.08 0.038 -1.063541 -.0314907 6 | -.7124029 .1793595 -3.97 0.000 -1.063941 -.3608647 7 | -1.820047 .2482806 -7.33 0.000 -2.306669 -1.333426 8 | -.8349262 .2309475 -3.62 0.000 -1.287575 -.3822774 | _cons | -3.732076 .8362598 -4.46 0.000 -5.371115 -2.093037 ---------------------------------------------------------------------------------- Displaying classification results for logit Dependent variable: beval Using cutoff score of .5 Classification table Predicted | Actual values of values of | beval beval | 0 1 | Total -----------+----------------------+---------- 0 | 840 97 | 937 1 | 132 797 | 929 -----------+----------------------+---------- Total | 972 894 | 1,866 Percent Correctly Predicted = 87.73% Percent in Modal Category = 52.09% Proportional Reduction in Error = 74.38% expected PCP = 81.15% expected PMC = 50.09% expected PRE = 62.23% Likelihood-ratio test LR chi2(8) = 82.34 (Assumption: treatment3_n~d nested in treatment3_w~d) Prob > chi2 = 0.0000 ( 1) [beval]1.issue = 0 ( 2) [beval]2.issue = 0 ( 3) [beval]3.issue = 0 ( 4) [beval]4.issue = 0 ( 5) [beval]5.issue = 0 ( 6) [beval]6.issue = 0 ( 7) [beval]7.issue = 0 ( 8) [beval]8.issue = 0 chi2( 8) = 108.10 Prob > chi2 = 0.0000 Predictive margins Number of obs = 1866 Model VCE : Robust Expression : Pr(beval), predict() 1._at : beh_ideology = 1 beh_pid_3pt_3way= 1 2._at : beh_ideology = 2 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 4._at : beh_ideology = 4 beh_pid_3pt_3way= 1 5._at : beh_ideology = 5 beh_pid_3pt_3way= 1 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .0392612 .0213432 1.84 0.066 -.0025707 .081093 2 | .151513 .0422559 3.59 0.000 .0686929 .234333 3 | .4201879 .0459649 9.14 0.000 .3300984 .5102774 4 | .744149 .0554169 13.43 0.000 .6355339 .8527641 5 | .9262147 .0355886 26.03 0.000 .8564624 .9959671 ------------------------------------------------------------------------------ Adjusted predictions Number of obs = 1866 Model VCE : Robust Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 1 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 2 3._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 3 4._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 4 5._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 5 6._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 6 7._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 7 8._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 8 9._at : beh_ideology = 3 beh_pid_3pt_3way= 1 issue = 9 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .3167814 .0552273 5.74 0.000 .2085378 .425025 2 | .2403886 .0471448 5.10 0.000 .1479864 .3327908 3 | .3798655 .0628173 6.05 0.000 .2567459 .5029852 4 | .2332981 .045105 5.17 0.000 .1448939 .3217024 5 | .5783302 .068251 8.47 0.000 .4445607 .7120996 6 | .5376872 .0613763 8.76 0.000 .4173918 .6579826 7 | .2775576 .0527691 5.26 0.000 .1741322 .3809831 8 | .5071275 .0634548 7.99 0.000 .3827584 .6314967 9 | .7033785 .0551437 12.76 0.000 .5952988 .8114582 ------------------------------------------------------------------------------ Predictive margins Number of obs = 1866 Model VCE : Robust Expression : Pr(beval), predict() 1._at : beh_ideology = 3 beh_pid_3pt_3way= 0 2._at : beh_ideology = 3 beh_pid_3pt_3way= 1 3._at : beh_ideology = 3 beh_pid_3pt_3way= 2 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1034018 .0295381 3.50 0.000 .0455082 .1612953 2 | .4201879 .0459649 9.14 0.000 .3300984 .5102774 3 | .7491675 .065481 11.44 0.000 .620827 .877508 ------------------------------------------------------------------------------ . . #delimit ; delimiter now ; . ************************************************************************; . * *; . * closing *; . * *; . ************************************************************************; . log close; name: log: C:\Users\therriault\Desktop\Research\projects_current\QP\QPv7\bjps\therriault_bjps_final\02_cces08_analysis.log log type: text closed on: 22 Nov 2014, 16:49:29 ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------