User:EpochFail/Journal/2011-12-05
< User:EpochFail | Journal
Tuesday, Dec. 6th.[edit]
I just finished organizing User:Staeiou's dataset and running some logistic regressions.
Scroll to the bottom for my summary
Results[edit]
Result's for 420 'vandal' editors[edit]
Call:
glm(formula = good_outcome ~ is_anon + personal + nodirectives,
data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.57159 0.07250 7.884 2.83e-14 ***
is_anonTRUE 0.05404 0.06858 0.788 0.431
personalTRUE -0.02676 0.06054 -0.442 0.659
nodirectivesTRUE -0.06952 0.05942 -1.170 0.243
Call:
glm(formula = improves ~ is_anon + personal + nodirectives, data = group_codings[group_codings$before_rating <=
4, ])
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.41336 0.09754 4.238 3.26e-05 ***
is_anonTRUE 0.13441 0.08981 1.497 0.136
personalTRUE -0.05580 0.08285 -0.674 0.501
nodirectivesTRUE -0.03947 0.07988 -0.494 0.622
Call:
glm(formula = contact ~ is_anon + personal + nodirectives, data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.10311 0.03194 3.228 0.00134 **
is_anonTRUE -0.07591 0.03021 -2.512 0.01237 *
personalTRUE 0.01779 0.02667 0.667 0.50508
nodirectivesTRUE 0.01673 0.02618 0.639 0.52316
Call:
glm(formula = good_contact ~ is_anon + personal + nodirectives,
data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.03423 0.02552 1.341 0.181
is_anonTRUE -0.02205 0.02414 -0.913 0.362
personalTRUE 0.02014 0.02131 0.945 0.345
nodirectivesTRUE 0.02493 0.02092 1.192 0.234
Call:
glm(formula = stay ~ is_anon + personal + nodirectives, data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.589011 0.072925 8.077 7.33e-15 ***
is_anonTRUE -0.049939 0.068982 -0.724 0.470
personalTRUE 0.006622 0.060896 0.109 0.913
nodirectivesTRUE 0.051001 0.059776 0.853 0.394
Result's for 982 'bad faith' editors[edit]
Call:
glm(formula = good_outcome ~ is_anon + personal + nodirectives,
data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.747150 0.053937 13.852 <2e-16 ***
is_anonTRUE -0.055361 0.052732 -1.050 0.294
personalTRUE 0.031318 0.035503 0.882 0.378
nodirectivesTRUE -0.007934 0.035607 -0.223 0.824
Call:
glm(formula = improves ~ is_anon + personal + nodirectives, data = group_codings[group_codings$before_rating <=
4, ])
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.30488 0.08304 3.671 0.000272 ***
is_anonTRUE -0.03568 0.08039 -0.444 0.657324
personalTRUE 0.08101 0.05457 1.484 0.138416
nodirectivesTRUE 0.01968 0.05381 0.366 0.714758
Call:
glm(formula = contact ~ is_anon + personal + nodirectives, data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.09589 0.02048 4.683 3.23e-06 ***
is_anonTRUE -0.08619 0.02002 -4.305 1.84e-05 ***
personalTRUE 0.01999 0.01348 1.483 0.1385
nodirectivesTRUE 0.02532 0.01352 1.873 0.0613 .
Call:
glm(formula = good_contact ~ is_anon + personal + nodirectives,
data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.055407 0.015358 3.608 0.000325 ***
is_anonTRUE -0.048091 0.015015 -3.203 0.001405 **
personalTRUE 0.007076 0.010109 0.700 0.484101
nodirectivesTRUE 0.011429 0.010139 1.127 0.259912
Call:
glm(formula = stay ~ is_anon + personal + nodirectives, data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.427547 0.058728 7.280 6.85e-13 ***
is_anonTRUE 0.001126 0.057417 0.020 0.984
personalTRUE 0.005804 0.038657 0.150 0.881
nodirectivesTRUE 0.035127 0.038770 0.906 0.365
Result's for 702 'test' editors[edit]
Call:
glm(formula = good_outcome ~ is_anon + personal + nodirectives,
data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.32041 0.05007 6.400 2.86e-10 ***
is_anonTRUE -0.19191 0.04950 -3.877 0.000116 ***
personalTRUE -0.06622 0.02799 -2.366 0.018269 *
nodirectivesTRUE -0.02851 0.02860 -0.997 0.319314
Call:
glm(formula = improves ~ is_anon + personal + nodirectives, data = group_codings[group_codings$before_rating <=
4, ])
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.51628 0.08999 5.737 2.71e-08 ***
is_anonTRUE -0.20262 0.09047 -2.240 0.0260 *
personalTRUE -0.08154 0.06867 -1.187 0.2362
nodirectivesTRUE -0.13342 0.06321 -2.111 0.0357 *
Call:
glm(formula = contact ~ is_anon + personal + nodirectives, data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.102792 0.029816 3.448 0.000600 ***
is_anonTRUE -0.092998 0.029477 -3.155 0.001674 **
personalTRUE 0.006664 0.016669 0.400 0.689437
nodirectivesTRUE 0.058796 0.017034 3.452 0.000591 ***
Call:
glm(formula = good_contact ~ is_anon + personal + nodirectives,
data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.08055 0.02552 3.156 0.00167 **
is_anonTRUE -0.07780 0.02523 -3.083 0.00213 **
personalTRUE 0.01449 0.01427 1.015 0.31031
nodirectivesTRUE 0.04365 0.01458 2.993 0.00286 **
Call:
glm(formula = stay ~ is_anon + personal + nodirectives, data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.66019 0.07762 8.505 < 2e-16 ***
is_anonTRUE -0.27420 0.07674 -3.573 0.000377 ***
personalTRUE -0.11179 0.04339 -2.576 0.010195 *
nodirectivesTRUE 0.02281 0.04435 0.514 0.607095
Result's for 347 'good faith' editors[edit]
Call:
glm(formula = good_outcome ~ is_anon + personal + nodirectives,
data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.54997 0.06567 8.375 1.45e-15 ***
is_anonTRUE -0.22114 0.06548 -3.377 0.000817 ***
personalTRUE 0.02469 0.06455 0.382 0.702373
nodirectivesTRUE 0.03012 0.06340 0.475 0.635100
Call:
glm(formula = improves ~ is_anon + personal + nodirectives, data = group_codings[group_codings$before_rating <=
4, ])
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.41501 0.09437 4.398 2.63e-05 ***
is_anonTRUE -0.09934 0.10140 -0.980 0.330
personalTRUE -0.13024 0.11243 -1.158 0.249
nodirectivesTRUE 0.07892 0.11049 0.714 0.477
Call:
glm(formula = contact ~ is_anon + personal + nodirectives, data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.174159 0.040362 4.315 2.09e-05 ***
is_anonTRUE -0.131793 0.040247 -3.275 0.00117 **
personalTRUE 0.009522 0.039672 0.240 0.81046
nodirectivesTRUE 0.086722 0.038970 2.225 0.02671 *
Call:
glm(formula = good_contact ~ is_anon + personal + nodirectives,
data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.14026 0.03906 3.591 0.000378 ***
is_anonTRUE -0.08553 0.03895 -2.196 0.028781 *
personalTRUE -0.01377 0.03839 -0.359 0.719990
nodirectivesTRUE 0.07412 0.03771 1.965 0.050198 .
Call:
glm(formula = stay ~ is_anon + personal + nodirectives, data = group_codings)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.70754 0.06670 10.608 < 2e-16 ***
is_anonTRUE -0.24794 0.06651 -3.728 0.000226 ***
personalTRUE 0.06999 0.06556 1.068 0.286437
nodirectivesTRUE 0.04712 0.06440 0.732 0.464845
Summary[edit]
- Vandals (blatant)
- No interesting significant effects here
- Bad faith
- No directive increases the probability of making contact (marginal significance)
- Testers
- Personal messages reduce the probability of a good outcome
- No directives messages reduce the probability that an editor will improve.
- No directives increases the probability of contact and good contact
- Personal messages reduce the probability of staying to make more edits
- Good faith
- No directives increases the probability of contact and good contact.