AI Dating Lab Study 2
Noah Castelo
5/22/2019
Calculating reliability of the composite DV (interest in dating someone this week, this month, next few months, this year). First for humans:
Reliability analysis
Call: psych::alpha(x = d[, c(72, 73, 74, 75)])
raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
0.84 0.84 0.85 0.57 5.3 0.019 6.5 1.6
lower alpha upper 95% confidence boundaries
0.8 0.84 0.88
Reliability if an item is dropped:
raw_alpha std.alpha G6(smc) average_r S/N
interest_date_this_week 0.80 0.80 0.76 0.57 4.0
interest_date_this_month 0.77 0.77 0.72 0.53 3.3
interest_relationship_months 0.77 0.77 0.75 0.53 3.3
interest_long_relation 0.85 0.85 0.82 0.65 5.6
alpha se
interest_date_this_week 0.025
interest_date_this_month 0.028
interest_relationship_months 0.029
interest_long_relation 0.020
Item statistics
n raw.r std.r r.cor r.drop mean sd
interest_date_this_week 198 0.82 0.82 0.77 0.67 6.2 2.0
interest_date_this_month 198 0.86 0.86 0.83 0.74 6.4 1.9
interest_relationship_months 198 0.87 0.86 0.80 0.74 6.4 2.1
interest_long_relation 198 0.74 0.75 0.63 0.56 6.9 1.9
Non missing response frequency for each item
1 4 5 6 7 8 9 miss
interest_date_this_week 0.06 0.12 0.16 0.15 0.26 0.15 0.11 0
interest_date_this_month 0.04 0.13 0.12 0.17 0.25 0.16 0.13 0
interest_relationship_months 0.06 0.12 0.10 0.13 0.25 0.20 0.14 0
interest_long_relation 0.04 0.08 0.09 0.15 0.23 0.21 0.22 0Then for robots:
Reliability analysis
Call: psych::alpha(x = d[, c(76, 77, 78, 79)])
raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
0.91 0.92 0.94 0.74 11 0.01 2.3 1.9
lower alpha upper 95% confidence boundaries
0.89 0.91 0.93
Reliability if an item is dropped:
raw_alpha std.alpha G6(smc) average_r S/N
interest_dating_robot_week 0.89 0.91 0.90 0.77 10.1
interest_dating_robot_month 0.87 0.89 0.89 0.73 8.0
interest_dating_robot_months 0.88 0.88 0.86 0.71 7.5
long_relation_robot 0.90 0.90 0.88 0.76 9.3
alpha se
interest_dating_robot_week 0.014
interest_dating_robot_month 0.017
interest_dating_robot_months 0.014
long_relation_robot 0.012
Item statistics
n raw.r std.r r.cor r.drop mean sd
interest_dating_robot_week 198 0.90 0.87 0.83 0.80 2.9 2.5
interest_dating_robot_month 198 0.93 0.91 0.88 0.86 2.6 2.4
interest_dating_robot_months 198 0.90 0.92 0.91 0.83 2.0 1.9
long_relation_robot 198 0.86 0.89 0.87 0.77 1.8 1.7
Non missing response frequency for each item
1 4 5 6 7 8 9 miss
interest_dating_robot_week 0.58 0.16 0.10 0.03 0.08 0.03 0.04 0
interest_dating_robot_month 0.65 0.16 0.06 0.03 0.05 0.02 0.04 0
interest_dating_robot_months 0.76 0.13 0.04 0.02 0.03 0.01 0.02 0
long_relation_robot 0.82 0.10 0.03 0.02 0.03 0.02 0.01 0Checking for interaction between condition and prior interest in dating on the interest in dating humans composite DV
Call:
lm(formula = human_DV ~ condition * interest.dating.now, data = d)
Residuals:
Min 1Q Median 3Q Max
-6.6955 -0.6744 0.1468 0.7827 3.0109
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.39689 0.49687 6.837 1.03e-10
conditionhuman 0.08762 0.67821 0.129 0.897
interest.dating.now 0.61409 0.10255 5.988 1.01e-08
conditionhuman:interest.dating.now 0.06249 0.13903 0.449 0.654
(Intercept) ***
conditionhuman
interest.dating.now ***
conditionhuman:interest.dating.now
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.329 on 194 degrees of freedom
Multiple R-squared: 0.3227, Adjusted R-squared: 0.3122
F-statistic: 30.81 on 3 and 194 DF, p-value: 2.464e-16
Attaching package: 'ggplot2'The following objects are masked from 'package:psych':
%+%, alpha
Checking for main effect of condition - reveals a marginal effect, more interest in the human condition.
Call:
lm(formula = human_DV ~ condition, data = d)
Residuals:
Min 1Q Median 3Q Max
-5.2626 -0.9192 0.0808 1.3074 2.7374
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.2626 0.1602 39.096 <2e-16 ***
conditionhuman 0.4066 0.2265 1.795 0.0742 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.594 on 196 degrees of freedom
Multiple R-squared: 0.01617, Adjusted R-squared: 0.01115
F-statistic: 3.221 on 1 and 196 DF, p-value: 0.07424Checking for interaction between condition and prior interest in dating on the interest in dating robot composite DV
Call:
lm(formula = robot_DV ~ condition * interest.dating.now, data = d)
Residuals:
Min 1Q Median 3Q Max
-2.082 -1.270 -1.009 1.133 6.562
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2529 0.7153 1.752 0.0814 .
conditionhuman 2.0077 0.9763 2.056 0.0411 *
interest.dating.now 0.2034 0.1476 1.378 0.1699
conditionhuman:interest.dating.now -0.3821 0.2001 -1.909 0.0577 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.914 on 194 degrees of freedom
Multiple R-squared: 0.02161, Adjusted R-squared: 0.006482
F-statistic: 1.428 on 3 and 194 DF, p-value: 0.2357
Breaking down the interaction shown above
Call:
lm(formula = robot_DV ~ condition, data = d)
Residuals:
Min 1Q Median 3Q Max
-1.419 -1.419 -1.202 1.081 6.331
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.2020 0.1931 11.402 <2e-16 ***
conditionhuman 0.2172 0.2731 0.795 0.427
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.922 on 196 degrees of freedom
Multiple R-squared: 0.003216, Adjusted R-squared: -0.00187
F-statistic: 0.6323 on 1 and 196 DF, p-value: 0.4275
Call:
lm(formula = robot_DV ~ interest.dating.now, data = AI)
Residuals:
Min 1Q Median 3Q Max
-1.6766 -1.2698 -0.8631 0.6052 5.9336
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2529 0.6788 1.846 0.068 .
interest.dating.now 0.2034 0.1401 1.452 0.150
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.816 on 97 degrees of freedom
Multiple R-squared: 0.02126, Adjusted R-squared: 0.01117
F-statistic: 2.107 on 1 and 97 DF, p-value: 0.1498
Call:
lm(formula = robot_DV ~ interest.dating.now, data = human)
Residuals:
Min 1Q Median 3Q Max
-2.082 -1.367 -1.009 1.419 6.562
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.2607 0.6967 4.680 9.31e-06 ***
interest.dating.now -0.1788 0.1417 -1.262 0.21
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.006 on 97 degrees of freedom
Multiple R-squared: 0.01615, Adjusted R-squared: 0.006003
F-statistic: 1.592 on 1 and 97 DF, p-value: 0.2101Checking whether the interaction is significant for any of the individual DVs (not the composite). Not significant for any of the dating human DVs.
Call:
lm(formula = interest_date_this_week ~ condition * interest.dating.now,
data = d)
Residuals:
Min 1Q Median 3Q Max
-6.7605 -0.7715 0.3268 1.1691 4.1926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.3577 0.6414 3.676 0.000307
conditionhuman 1.2625 0.8755 1.442 0.150885
interest.dating.now 0.7718 0.1324 5.830 2.28e-08
conditionhuman:interest.dating.now -0.1783 0.1795 -0.993 0.321765
(Intercept) ***
conditionhuman
interest.dating.now ***
conditionhuman:interest.dating.now
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.716 on 194 degrees of freedom
Multiple R-squared: 0.2406, Adjusted R-squared: 0.2288
F-statistic: 20.49 on 3 and 194 DF, p-value: 1.416e-11
Call:
lm(formula = interest_date_this_month ~ condition * interest.dating.now,
data = d)
Residuals:
Min 1Q Median 3Q Max
-6.5678 -0.5583 0.1777 1.0081 3.2009
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.80387 0.59416 4.719 4.53e-06
conditionhuman 0.53336 0.81101 0.658 0.512
interest.dating.now 0.68056 0.12263 5.550 9.31e-08
conditionhuman:interest.dating.now 0.05038 0.16625 0.303 0.762
(Intercept) ***
conditionhuman
interest.dating.now ***
conditionhuman:interest.dating.now
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.589 on 194 degrees of freedom
Multiple R-squared: 0.3063, Adjusted R-squared: 0.2956
F-statistic: 28.56 on 3 and 194 DF, p-value: 2.439e-15
Call:
lm(formula = interest_relationship_months ~ condition * interest.dating.now,
data = d)
Residuals:
Min 1Q Median 3Q Max
-6.7121 -0.7657 0.3893 1.1143 3.8593
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.2121 0.6661 4.822 2.86e-06
conditionhuman -0.4098 0.9092 -0.451 0.653
interest.dating.now 0.6429 0.1375 4.676 5.47e-06
conditionhuman:interest.dating.now 0.1738 0.1864 0.933 0.352
(Intercept) ***
conditionhuman
interest.dating.now ***
conditionhuman:interest.dating.now
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.782 on 194 degrees of freedom
Multiple R-squared: 0.2565, Adjusted R-squared: 0.245
F-statistic: 22.31 on 3 and 194 DF, p-value: 1.878e-12
Call:
lm(formula = interest_long_relation ~ condition * interest.dating.now,
data = d)
Residuals:
Min 1Q Median 3Q Max
-6.7416 -1.0039 0.3418 1.2254 4.2566
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.2138 0.6663 7.825 3.21e-13
conditionhuman -1.0356 0.9095 -1.139 0.25629
interest.dating.now 0.3611 0.1375 2.626 0.00934
conditionhuman:interest.dating.now 0.2040 0.1864 1.094 0.27518
(Intercept) ***
conditionhuman
interest.dating.now **
conditionhuman:interest.dating.now
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.783 on 194 degrees of freedom
Multiple R-squared: 0.1226, Adjusted R-squared: 0.109
F-statistic: 9.035 on 3 and 194 DF, p-value: 1.252e-05Significant for the 2 “short term” robot DVs (interest in dating a robot this week / this month) - not significant for the longer term DV’s (interest in dating robot over months / developing long term relationship with robot).
Call:
lm(formula = interest_dating_robot_week ~ condition * interest.dating.now,
data = d)
Residuals:
Min 1Q Median 3Q Max
-2.601 -1.967 -1.621 1.728 6.378
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.3199 0.9315 1.417 0.1581
conditionhuman 2.4378 1.2714 1.917 0.0567 .
interest.dating.now 0.3254 0.1923 1.693 0.0922 .
conditionhuman:interest.dating.now -0.4821 0.2606 -1.850 0.0659 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.492 on 194 degrees of freedom
Multiple R-squared: 0.01981, Adjusted R-squared: 0.004652
F-statistic: 1.307 on 3 and 194 DF, p-value: 0.2734
Call:
lm(formula = interest_dating_robot_month ~ condition * interest.dating.now,
data = d)
Residuals:
Min 1Q Median 3Q Max
-2.530 -1.692 -1.184 1.471 6.816
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1684 0.8870 1.317 0.1893
conditionhuman 2.5621 1.2107 2.116 0.0356 *
interest.dating.now 0.2540 0.1831 1.387 0.1669
conditionhuman:interest.dating.now -0.4542 0.2482 -1.830 0.0688 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.373 on 194 degrees of freedom
Multiple R-squared: 0.02518, Adjusted R-squared: 0.01011
F-statistic: 1.67 on 3 and 194 DF, p-value: 0.1747
Call:
lm(formula = interest_dating_robot_months ~ condition * interest.dating.now,
data = d)
Residuals:
Min 1Q Median 3Q Max
-1.7442 -0.9978 -0.8529 -0.5560 7.2459
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2736 0.7143 1.783 0.0761 .
conditionhuman 1.6687 0.9750 1.712 0.0886 .
interest.dating.now 0.1448 0.1474 0.982 0.3271
conditionhuman:interest.dating.now -0.3429 0.1999 -1.716 0.0878 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.911 on 194 degrees of freedom
Multiple R-squared: 0.01607, Adjusted R-squared: 0.0008554
F-statistic: 1.056 on 3 and 194 DF, p-value: 0.3689
Call:
lm(formula = long_relation_robot ~ condition * interest.dating.now,
data = d)
Residuals:
Min 1Q Median 3Q Max
-1.4521 -0.8117 -0.6964 -0.4915 7.3484
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.25000 0.65384 1.912 0.0574 .
conditionhuman 1.36219 0.89246 1.526 0.1286
interest.dating.now 0.08929 0.13495 0.662 0.5090
conditionhuman:interest.dating.now -0.24939 0.18295 -1.363 0.1744
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.749 on 194 degrees of freedom
Multiple R-squared: 0.01379, Adjusted R-squared: -0.001456
F-statistic: 0.9045 on 3 and 194 DF, p-value: 0.44Other non-significant moderators of condition on the “interest in dating humans” composite DV
#Wanting children
summary(lm(human_DV ~ condition * want.children, d))##
## Call:
## lm(formula = human_DV ~ condition * want.children, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6407 -0.8566 0.1093 1.1434 3.3853
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.15203 0.45723 11.268 <2e-16 ***
## conditionhuman 0.74977 0.70496 1.064 0.2888
## want.children 0.21267 0.08220 2.587 0.0104 *
## conditionhuman:want.children -0.07627 0.12276 -0.621 0.5351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.566 on 194 degrees of freedom
## Multiple R-squared: 0.05947, Adjusted R-squared: 0.04492
## F-statistic: 4.089 on 3 and 194 DF, p-value: 0.007646#Age
summary(lm(human_DV ~ condition * Age, d))##
## Call:
## lm(formula = human_DV ~ condition * Age, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3315 -0.9393 0.1237 1.2883 2.8027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.660278 0.611040 9.263 <2e-16 ***
## conditionhuman 0.315211 0.940633 0.335 0.738
## Age 0.026849 0.026284 1.021 0.308
## conditionhuman:Age 0.004184 0.040780 0.103 0.918
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.594 on 194 degrees of freedom
## Multiple R-squared: 0.02638, Adjusted R-squared: 0.01132
## F-statistic: 1.752 on 3 and 194 DF, p-value: 0.1578#How long ago was last relationship
summary(lm(human_DV ~ condition * last.relationship.months, d))##
## Call:
## lm(formula = human_DV ~ condition * last.relationship.months,
## data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2652 -0.9244 0.0838 1.2994 2.7371
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 6.2652465 0.2097459 29.871
## conditionhuman 0.4159900 0.2807628 1.482
## last.relationship.months -0.0001769 0.0090781 -0.019
## conditionhuman:last.relationship.months -0.0007057 0.0114136 -0.062
## Pr(>|t|)
## (Intercept) <2e-16 ***
## conditionhuman 0.140
## last.relationship.months 0.984
## conditionhuman:last.relationship.months 0.951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.602 on 194 degrees of freedom
## Multiple R-squared: 0.01625, Adjusted R-squared: 0.001039
## F-statistic: 1.068 on 3 and 194 DF, p-value: 0.3637#Length of last relationship
summary(lm(human_DV ~ condition * length.months, d))##
## Call:
## lm(formula = human_DV ~ condition * length.months, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2983 -0.9412 0.1517 1.2607 2.7123
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.2982533 0.2068268 30.452 <2e-16 ***
## conditionhuman 0.4292760 0.2774542 1.547 0.123
## length.months -0.0035130 0.0128254 -0.274 0.784
## conditionhuman:length.months -0.0008856 0.0145576 -0.061 0.952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.6 on 194 degrees of freedom
## Multiple R-squared: 0.01861, Adjusted R-squared: 0.003434
## F-statistic: 1.226 on 3 and 194 DF, p-value: 0.3014#loneliness
d$loneliness<-(d$lack.companionship+d$left.out+d$isolated)/3
summary(lm(human_DV ~ condition * loneliness, d))##
## Call:
## lm(formula = human_DV ~ condition * loneliness, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6679 -0.8492 0.1082 1.2209 3.1097
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5015 0.4628 11.887 <2e-16 ***
## conditionhuman 0.7617 0.6551 1.163 0.2464
## loneliness 0.2333 0.1332 1.752 0.0814 .
## conditionhuman:loneliness -0.1073 0.1897 -0.565 0.5724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.586 on 194 degrees of freedom
## Multiple R-squared: 0.03574, Adjusted R-squared: 0.02083
## F-statistic: 2.397 on 3 and 194 DF, p-value: 0.06942Moderation by culture - main effect is significant among Americans
summary(lm(human_DV ~ condition * Culture, d))##
## Call:
## lm(formula = human_DV ~ condition * Culture, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0438 -0.9421 0.0948 1.2062 2.9562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.785941 0.598995 12.998 < 2e-16 ***
## conditionhuman -1.044158 0.787235 -1.326 0.18628
## Culture -0.006476 0.002456 -2.637 0.00905 **
## conditionhuman:Culture 0.006154 0.003267 1.884 0.06110 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.574 on 194 degrees of freedom
## Multiple R-squared: 0.05031, Adjusted R-squared: 0.03562
## F-statistic: 3.426 on 3 and 194 DF, p-value: 0.01823nonUS<-subset(d, Culture!=269)
US<-subset(d, Culture==269)
summary(lm(human_DV ~ condition, nonUS))##
## Call:
## lm(formula = human_DV ~ condition, data = nonUS)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.231 -0.750 0.250 1.134 2.250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.73148 0.26941 24.986 <2e-16 ***
## conditionhuman 0.01852 0.37759 0.049 0.961
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.4 on 53 degrees of freedom
## Multiple R-squared: 4.538e-05, Adjusted R-squared: -0.01882
## F-statistic: 0.002405 on 1 and 53 DF, p-value: 0.9611summary(lm(human_DV ~ condition, US))##
## Call:
## lm(formula = human_DV ~ condition, data = US)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0868 -0.8873 0.1127 1.1632 2.9132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.0868 0.1949 31.23 <2e-16 ***
## conditionhuman 0.5505 0.2766 1.99 0.0485 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.654 on 141 degrees of freedom
## Multiple R-squared: 0.02733, Adjusted R-squared: 0.02043
## F-statistic: 3.961 on 1 and 141 DF, p-value: 0.04849#Key interaction is still not significant among Americans though
summary(lm(human_DV ~ condition * interest.dating.now, US))##
## Call:
## lm(formula = human_DV ~ condition * interest.dating.now, data = US)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5298 -0.6004 0.1123 0.8942 3.0681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.42092 0.60734 5.633 9.49e-08
## conditionhuman 0.03875 0.82994 0.047 0.963
## interest.dating.now 0.58698 0.12864 4.563 1.10e-05
## conditionhuman:interest.dating.now 0.10935 0.17484 0.625 0.533
##
## (Intercept) ***
## conditionhuman
## interest.dating.now ***
## conditionhuman:interest.dating.now
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.408 on 139 degrees of freedom
## Multiple R-squared: 0.3045, Adjusted R-squared: 0.2895
## F-statistic: 20.29 on 3 and 139 DF, p-value: 5.793e-11Checking for effects of condition on the other DV’s / potential mechanisms.
summary(lm(anxious ~ condition, d))##
## Call:
## lm(formula = anxious ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6970 -1.5556 -0.5556 1.3030 4.4444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.5556 0.1680 15.216 <2e-16 ***
## conditionhuman 0.1414 0.2375 0.595 0.552
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.671 on 196 degrees of freedom
## Multiple R-squared: 0.001805, Adjusted R-squared: -0.003288
## F-statistic: 0.3545 on 1 and 196 DF, p-value: 0.5523summary(lm(stressed ~ condition, d))##
## Call:
## lm(formula = stressed ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5253 -1.4949 -0.4949 1.2323 4.5051
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.4949 0.1633 15.278 <2e-16 ***
## conditionhuman 0.0303 0.2309 0.131 0.896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.625 on 196 degrees of freedom
## Multiple R-squared: 8.784e-05, Adjusted R-squared: -0.005014
## F-statistic: 0.01722 on 1 and 196 DF, p-value: 0.8957summary(lm(confident ~ condition, d))##
## Call:
## lm(formula = confident ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7778 -1.3460 0.2222 1.2222 3.2222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5354 0.1543 22.918 <2e-16 ***
## conditionhuman 0.2424 0.2182 1.111 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.535 on 196 degrees of freedom
## Multiple R-squared: 0.006261, Adjusted R-squared: 0.001191
## F-statistic: 1.235 on 1 and 196 DF, p-value: 0.2678summary(lm(calm ~ condition, d))##
## Call:
## lm(formula = calm ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9596 -0.9596 0.0404 1.0404 3.3131
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6869 0.1607 22.94 <2e-16 ***
## conditionhuman 0.2727 0.2273 1.20 0.232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.599 on 196 degrees of freedom
## Multiple R-squared: 0.007293, Adjusted R-squared: 0.002229
## F-statistic: 1.44 on 1 and 196 DF, p-value: 0.2316summary(lm(comfortable ~ condition, d))##
## Call:
## lm(formula = comfortable ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1111 -1.1111 0.2323 1.2323 3.2323
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7677 0.1604 23.484 <2e-16 ***
## conditionhuman 0.3434 0.2269 1.514 0.132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.596 on 196 degrees of freedom
## Multiple R-squared: 0.01155, Adjusted R-squared: 0.006512
## F-statistic: 2.291 on 1 and 196 DF, p-value: 0.1317summary(lm(insecure ~ condition, d))##
## Call:
## lm(formula = insecure ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.697 -1.404 -0.404 1.303 4.596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6970 0.1541 17.502 <2e-16 ***
## conditionhuman -0.2929 0.2179 -1.344 0.18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.533 on 196 degrees of freedom
## Multiple R-squared: 0.009134, Adjusted R-squared: 0.004079
## F-statistic: 1.807 on 1 and 196 DF, p-value: 0.1804#Felt more emotionally satisfied after talking to a human
summary(lm(feel_emotionally ~ condition, d))##
## Call:
## lm(formula = feel_emotionally ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4242 -1.0808 -0.0808 0.9192 3.9192
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0808 0.1463 21.053 <2e-16 ***
## conditionhuman 0.3434 0.2070 1.659 0.0986 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.456 on 196 degrees of freedom
## Multiple R-squared: 0.01386, Adjusted R-squared: 0.008824
## F-statistic: 2.754 on 1 and 196 DF, p-value: 0.09862summary(lm(feel_cognitively ~ condition, d))##
## Call:
## lm(formula = feel_cognitively ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6364 -1.3838 0.3636 1.3636 3.6162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3838 0.1461 23.157 <2e-16 ***
## conditionhuman 0.2525 0.2067 1.222 0.223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.454 on 196 degrees of freedom
## Multiple R-squared: 0.007561, Adjusted R-squared: 0.002498
## F-statistic: 1.493 on 1 and 196 DF, p-value: 0.2232summary(lm(feel_physically ~ condition, d))##
## Call:
## lm(formula = feel_physically ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1717 -1.0960 0.1313 0.8283 4.1313
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.8687 0.1326 21.628 <2e-16 ***
## conditionhuman 0.3030 0.1876 1.615 0.108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.32 on 196 degrees of freedom
## Multiple R-squared: 0.01314, Adjusted R-squared: 0.008105
## F-statistic: 2.61 on 1 and 196 DF, p-value: 0.1078summary(lm(would_like_relationship ~ condition, d))##
## Call:
## lm(formula = would_like_relationship ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.798 -1.485 0.202 1.202 3.515
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4848 0.1675 20.800 <2e-16 ***
## conditionhuman 0.3131 0.2369 1.322 0.188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.667 on 196 degrees of freedom
## Multiple R-squared: 0.008832, Adjusted R-squared: 0.003775
## F-statistic: 1.746 on 1 and 196 DF, p-value: 0.1879summary(lm(good_to_be_in_relationship ~ condition, d))##
## Call:
## lm(formula = good_to_be_in_relationship ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1515 -1.1515 0.2121 1.2121 3.2121
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7879 0.1737 21.810 <2e-16 ***
## conditionhuman 0.3636 0.2456 1.481 0.14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.728 on 196 degrees of freedom
## Multiple R-squared: 0.01106, Adjusted R-squared: 0.006014
## F-statistic: 2.192 on 1 and 196 DF, p-value: 0.1403summary(lm(good.looks ~ condition, d))##
## Call:
## lm(formula = good.looks ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.6364 -0.9192 0.0808 1.0808 2.3636
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.6364 0.1656 46.107 <2e-16 ***
## conditionhuman 0.2828 0.2342 1.208 0.229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.648 on 196 degrees of freedom
## Multiple R-squared: 0.007384, Adjusted R-squared: 0.00232
## F-statistic: 1.458 on 1 and 196 DF, p-value: 0.2287summary(lm(patience ~ condition, d))##
## Call:
## lm(formula = patience ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5152 -0.5152 0.4848 1.4848 1.5455
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.45455 0.14236 59.387 <2e-16 ***
## conditionhuman 0.06061 0.20133 0.301 0.764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.417 on 196 degrees of freedom
## Multiple R-squared: 0.0004621, Adjusted R-squared: -0.004638
## F-statistic: 0.09061 on 1 and 196 DF, p-value: 0.7637summary(lm(honesty ~ condition, d))##
## Call:
## lm(formula = honesty ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4141 -0.3232 0.5859 0.6768 0.6768
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.41414 0.10592 88.883 <2e-16 ***
## conditionhuman -0.09091 0.14979 -0.607 0.545
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.054 on 196 degrees of freedom
## Multiple R-squared: 0.001876, Adjusted R-squared: -0.003217
## F-statistic: 0.3684 on 1 and 196 DF, p-value: 0.5446summary(lm(warmth ~ condition, d))##
## Call:
## lm(formula = warmth ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8586 -0.8586 0.1414 0.9394 1.1414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.8586 0.1188 74.595 <2e-16 ***
## conditionhuman 0.2020 0.1679 1.203 0.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.182 on 196 degrees of freedom
## Multiple R-squared: 0.007328, Adjusted R-squared: 0.002264
## F-statistic: 1.447 on 1 and 196 DF, p-value: 0.2305summary(lm(humor ~ condition, d))##
## Call:
## lm(formula = humor ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.6061 -0.6162 0.3939 1.3838 1.3939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6061 0.1597 53.880 <2e-16 ***
## conditionhuman 0.0101 0.2259 0.045 0.964
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.589 on 196 degrees of freedom
## Multiple R-squared: 1.02e-05, Adjusted R-squared: -0.005092
## F-statistic: 0.002 on 1 and 196 DF, p-value: 0.9644summary(lm(fitness ~ condition, d))##
## Call:
## lm(formula = fitness ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0606 -1.0606 -0.0606 1.6768 2.9394
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.0606 0.1994 35.414 <2e-16 ***
## conditionhuman 0.2626 0.2820 0.931 0.353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.984 on 196 degrees of freedom
## Multiple R-squared: 0.004407, Adjusted R-squared: -0.0006725
## F-statistic: 0.8676 on 1 and 196 DF, p-value: 0.3528summary(lm(comm_skill ~ condition, d))##
## Call:
## lm(formula = comm_skill ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.697 -0.697 0.303 1.151 1.303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.6970 0.1559 55.784 <2e-16 ***
## conditionhuman 0.1515 0.2205 0.687 0.493
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.551 on 196 degrees of freedom
## Multiple R-squared: 0.002404, Adjusted R-squared: -0.002686
## F-statistic: 0.4722 on 1 and 196 DF, p-value: 0.4928summary(lm(kindness ~ condition, d))##
## Call:
## lm(formula = kindness ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2222 -0.2222 0.7778 0.8081 0.8081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.1919 0.1076 85.463 <2e-16 ***
## conditionhuman 0.0303 0.1521 0.199 0.842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.07 on 196 degrees of freedom
## Multiple R-squared: 0.0002025, Adjusted R-squared: -0.004899
## F-statistic: 0.03969 on 1 and 196 DF, p-value: 0.8423summary(lm(tolerance ~ condition, d))##
## Call:
## lm(formula = tolerance ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.2222 -1.2222 -0.2222 1.7475 1.7778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.2525 0.1573 52.481 <2e-16 ***
## conditionhuman -0.0303 0.2224 -0.136 0.892
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.565 on 196 degrees of freedom
## Multiple R-squared: 9.473e-05, Adjusted R-squared: -0.005007
## F-statistic: 0.01857 on 1 and 196 DF, p-value: 0.8918summary(lm(loyalty ~ condition, d))##
## Call:
## lm(formula = loyalty ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2222 -0.2424 0.7576 0.7778 0.7778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.2424 0.1311 70.521 <2e-16 ***
## conditionhuman -0.0202 0.1853 -0.109 0.913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.304 on 196 degrees of freedom
## Multiple R-squared: 6.061e-05, Adjusted R-squared: -0.005041
## F-statistic: 0.01188 on 1 and 196 DF, p-value: 0.9133summary(lm(adaptability ~ condition, d))##
## Call:
## lm(formula = adaptability ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.2323 -1.1111 -0.1111 0.8889 1.8889
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.2323 0.1658 49.651 <2e-16 ***
## conditionhuman -0.1212 0.2345 -0.517 0.606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.65 on 196 degrees of freedom
## Multiple R-squared: 0.001362, Adjusted R-squared: -0.003734
## F-statistic: 0.2672 on 1 and 196 DF, p-value: 0.6058summary(lm(ambition ~ condition, d))##
## Call:
## lm(formula = ambition ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3535 -1.1919 0.2273 1.6465 1.8081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.3535 0.1579 52.894 <2e-16 ***
## conditionhuman -0.1616 0.2233 -0.724 0.47
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.571 on 196 degrees of freedom
## Multiple R-squared: 0.002664, Adjusted R-squared: -0.002424
## F-statistic: 0.5236 on 1 and 196 DF, p-value: 0.4702summary(lm(openness ~ condition, d))##
## Call:
## lm(formula = openness ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.8889 -0.8182 0.1818 1.1111 1.1818
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.88889 0.14580 60.965 <2e-16 ***
## conditionhuman -0.07071 0.20620 -0.343 0.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.451 on 196 degrees of freedom
## Multiple R-squared: 0.0005996, Adjusted R-squared: -0.004499
## F-statistic: 0.1176 on 1 and 196 DF, p-value: 0.732summary(lm(respect ~ condition, d))##
## Call:
## lm(formula = respect ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2121 -0.2121 0.7879 0.8182 0.8182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.2121 0.1187 77.626 <2e-16 ***
## conditionhuman -0.0303 0.1678 -0.181 0.857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.181 on 196 degrees of freedom
## Multiple R-squared: 0.0001663, Adjusted R-squared: -0.004935
## F-statistic: 0.0326 on 1 and 196 DF, p-value: 0.8569summary(lm(supportive ~ condition, d))##
## Call:
## lm(formula = supportive ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1818 -0.1818 -0.0505 0.8182 0.9495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.0505 0.1129 80.141 <2e-16 ***
## conditionhuman 0.1313 0.1597 0.822 0.412
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.124 on 196 degrees of freedom
## Multiple R-squared: 0.003437, Adjusted R-squared: -0.001647
## F-statistic: 0.676 on 1 and 196 DF, p-value: 0.412summary(lm(reliable ~ condition, d))##
## Call:
## lm(formula = reliable ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1818 -0.1818 0.0101 0.8182 1.0101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.9899 0.1125 79.888 <2e-16 ***
## conditionhuman 0.1919 0.1591 1.206 0.229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.12 on 196 degrees of freedom
## Multiple R-squared: 0.007365, Adjusted R-squared: 0.002301
## F-statistic: 1.454 on 1 and 196 DF, p-value: 0.2293summary(lm(companionship ~ condition, d))##
## Call:
## lm(formula = companionship ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1414 -0.1414 -0.1010 0.8586 0.8990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1010 0.1067 57.166 <2e-16 ***
## conditionhuman 0.0404 0.1509 0.268 0.789
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.062 on 196 degrees of freedom
## Multiple R-squared: 0.0003655, Adjusted R-squared: -0.004735
## F-statistic: 0.07166 on 1 and 196 DF, p-value: 0.7892summary(lm(sexual_grat ~ condition, d))##
## Call:
## lm(formula = sexual_grat ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2424 -0.7677 0.2323 1.2323 2.2323
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.7677 0.1640 29.073 <2e-16 ***
## conditionhuman 0.4747 0.2319 2.047 0.042 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.632 on 196 degrees of freedom
## Multiple R-squared: 0.02093, Adjusted R-squared: 0.01594
## F-statistic: 4.19 on 1 and 196 DF, p-value: 0.04199summary(lm(love ~ condition, d))##
## Call:
## lm(formula = love ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1010 -0.2323 0.7677 0.8990 0.8990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.1010 0.1100 55.481 <2e-16 ***
## conditionhuman 0.1313 0.1555 0.844 0.399
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.094 on 196 degrees of freedom
## Multiple R-squared: 0.003624, Adjusted R-squared: -0.001459
## F-statistic: 0.713 on 1 and 196 DF, p-value: 0.3995summary(lm(expertise_relationship ~ condition, d))##
## Call:
## lm(formula = expertise_relationship ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5960 -1.2121 0.0960 0.7879 2.7879
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5960 0.1577 29.149 <2e-16 ***
## conditionhuman -0.3838 0.2230 -1.721 0.0868 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.569 on 196 degrees of freedom
## Multiple R-squared: 0.01489, Adjusted R-squared: 0.009867
## F-statistic: 2.963 on 1 and 196 DF, p-value: 0.08676summary(lm(self_growth ~ condition, d))##
## Call:
## lm(formula = self_growth ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5253 -0.5253 0.4747 1.4747 1.5354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.46465 0.14273 38.29 <2e-16 ***
## conditionhuman 0.06061 0.20186 0.30 0.764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.42 on 196 degrees of freedom
## Multiple R-squared: 0.0004597, Adjusted R-squared: -0.00464
## F-statistic: 0.09015 on 1 and 196 DF, p-value: 0.7643summary(lm(self_esteem ~ condition, d))##
## Call:
## lm(formula = self_esteem ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1616 -1.1616 -0.1111 1.8384 1.8889
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.16162 0.16731 30.850 <2e-16 ***
## conditionhuman -0.05051 0.23662 -0.213 0.831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.665 on 196 degrees of freedom
## Multiple R-squared: 0.0002324, Adjusted R-squared: -0.004868
## F-statistic: 0.04556 on 1 and 196 DF, p-value: 0.8312summary(lm(exclusitivity ~ condition, d))##
## Call:
## lm(formula = exclusitivity ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0101 -1.0101 0.1818 1.1818 2.1818
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0101 0.1866 26.848 <2e-16 ***
## conditionhuman -0.1919 0.2639 -0.727 0.468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.857 on 196 degrees of freedom
## Multiple R-squared: 0.002691, Adjusted R-squared: -0.002397
## F-statistic: 0.5289 on 1 and 196 DF, p-value: 0.468summary(lm(security ~ condition, d))##
## Call:
## lm(formula = security ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4949 -1.1717 0.5051 1.5051 1.8283
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.1717 0.1581 32.718 <2e-16 ***
## conditionhuman 0.3232 0.2235 1.446 0.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.573 on 196 degrees of freedom
## Multiple R-squared: 0.01055, Adjusted R-squared: 0.005506
## F-statistic: 2.091 on 1 and 196 DF, p-value: 0.1498summary(lm(social_support ~ condition, d))##
## Call:
## lm(formula = social_support ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3131 -1.9192 -0.1212 1.6869 2.8788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1212 0.1989 20.717 <2e-16 ***
## conditionhuman 0.1919 0.2813 0.682 0.496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.979 on 196 degrees of freedom
## Multiple R-squared: 0.002369, Adjusted R-squared: -0.002721
## F-statistic: 0.4654 on 1 and 196 DF, p-value: 0.4959summary(lm(happiness ~ condition, d))##
## Call:
## lm(formula = happiness ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1111 -0.8788 0.1212 0.8889 1.1212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8788 0.1108 53.046 <2e-16 ***
## conditionhuman 0.2323 0.1567 1.482 0.14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.103 on 196 degrees of freedom
## Multiple R-squared: 0.01109, Adjusted R-squared: 0.006041
## F-statistic: 2.197 on 1 and 196 DF, p-value: 0.1399summary(lm(learning_about_other_sex ~ condition, d))##
## Call:
## lm(formula = learning_about_other_sex ~ condition, data = d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3232 -1.2424 -0.2424 0.7576 2.7576
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.32323 0.16741 25.824 <2e-16 ***
## conditionhuman -0.08081 0.23676 -0.341 0.733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.666 on 196 degrees of freedom
## Multiple R-squared: 0.000594, Adjusted R-squared: -0.004505
## F-statistic: 0.1165 on 1 and 196 DF, p-value: 0.7332Separating gender. First looking at just males:
1 2
95 103
Call:
lm(formula = human_DV ~ condition * interest.dating.now, data = male)
Residuals:
Min 1Q Median 3Q Max
-4.4636 -0.5669 0.2080 0.7129 2.3153
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.8913 0.6725 4.299 4.29e-05
conditionhuman -0.6289 0.9391 -0.670 0.505
interest.dating.now 0.7645 0.1414 5.406 5.15e-07
conditionhuman:interest.dating.now 0.1155 0.1915 0.603 0.548
(Intercept) ***
conditionhuman
interest.dating.now ***
conditionhuman:interest.dating.now
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.261 on 91 degrees of freedom
Multiple R-squared: 0.4552, Adjusted R-squared: 0.4372
F-statistic: 25.34 on 3 and 91 DF, p-value: 5.244e-12
Call:
lm(formula = human_DV ~ condition, data = male)
Residuals:
Min 1Q Median 3Q Max
-5.3883 -0.8452 0.1979 1.1979 2.6117
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.3883 0.2462 25.952 <2e-16 ***
conditionhuman 0.1638 0.3463 0.473 0.637
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.688 on 93 degrees of freedom
Multiple R-squared: 0.0024, Adjusted R-squared: -0.008327
F-statistic: 0.2237 on 1 and 93 DF, p-value: 0.6373Then female
Call:
lm(formula = human_DV ~ condition * interest.dating.now, data = female)
Residuals:
Min 1Q Median 3Q Max
-6.2628 -0.7828 0.2172 0.9822 2.9672
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.79772 0.71039 5.346 5.78e-07
conditionhuman 0.60245 0.95325 0.632 0.528845
interest.dating.now 0.49501 0.14426 3.431 0.000877
conditionhuman:interest.dating.now 0.02801 0.19648 0.143 0.886935
(Intercept) ***
conditionhuman
interest.dating.now ***
conditionhuman:interest.dating.now
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.351 on 99 degrees of freedom
Multiple R-squared: 0.2486, Adjusted R-squared: 0.2258
F-statistic: 10.92 on 3 and 99 DF, p-value: 2.947e-06
Call:
lm(formula = human_DV ~ condition, data = female)
Residuals:
Min 1Q Median 3Q Max
-5.1490 -0.9642 -0.0294 1.2206 2.8510
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.1490 0.2094 29.360 <2e-16 ***
conditionhuman 0.6304 0.2976 2.118 0.0366 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.51 on 101 degrees of freedom
Multiple R-squared: 0.04253, Adjusted R-squared: 0.03305
F-statistic: 4.486 on 1 and 101 DF, p-value: 0.03663Merging 2 studies for more power
Call:
lm(formula = interest_dating_robot_week ~ AI_mindC, data = male)
Residuals:
Min 1Q Median 3Q Max
-3.2870 -1.8052 -0.9584 2.1182 5.9831
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.3234 0.5681 2.329 0.02200 *
AI_mindC 0.5292 0.1880 2.814 0.00597 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.37 on 93 degrees of freedom
Multiple R-squared: 0.07848, Adjusted R-squared: 0.06857
F-statistic: 7.92 on 1 and 93 DF, p-value: 0.005966
Call:
lm(formula = interest_dating_robot_week ~ AI_competentC, data = male)
Residuals:
Min 1Q Median 3Q Max
-2.771 -1.811 -1.171 1.722 5.656
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0378 1.1655 0.032 0.9742
AI_competentC 0.5333 0.2225 2.397 0.0185 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.396 on 93 degrees of freedom
Multiple R-squared: 0.05818, Adjusted R-squared: 0.04805
F-statistic: 5.745 on 1 and 93 DF, p-value: 0.01854
Call:
lm(formula = interest.dating.now ~ study, data = dc)
Residuals:
Min 1Q Median 3Q Max
-3.7263 -0.7263 0.2737 0.4545 2.4545
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.3646 0.2990 14.599 <2e-16 ***
study 0.1809 0.1963 0.921 0.358
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.432 on 214 degrees of freedom
Multiple R-squared: 0.003951, Adjusted R-squared: -0.0007037
F-statistic: 0.8488 on 1 and 214 DF, p-value: 0.3579
Call:
lm(formula = AI_mindC ~ study, data = dc)
Residuals:
Min 1Q Median 3Q Max
-1.7305 -1.0628 -0.2628 0.9372 4.5372
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.1951 0.2610 8.409 5.81e-15 ***
study 0.2677 0.1714 1.562 0.12
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.25 on 214 degrees of freedom
Multiple R-squared: 0.01127, Adjusted R-squared: 0.006652
F-statistic: 2.44 on 1 and 214 DF, p-value: 0.1198
Call:
lm(formula = AI_competentC ~ study, data = dc)
Residuals:
Min 1Q Median 3Q Max
-4.1200 -0.7421 0.0579 0.6800 2.2579
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.3643 0.2150 20.300 < 2e-16 ***
study 0.3779 0.1412 2.677 0.00801 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.03 on 214 degrees of freedom
Multiple R-squared: 0.03239, Adjusted R-squared: 0.02787
F-statistic: 7.165 on 1 and 214 DF, p-value: 0.008012
Call:
lm(formula = AI_lifelikeC ~ study, data = dc)
Residuals:
Min 1Q Median 3Q Max
-2.2989 -0.6989 0.1011 0.8030 3.7011
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.0950 0.2320 13.341 <2e-16 ***
study 0.1020 0.1523 0.669 0.504
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.111 on 214 degrees of freedom
Multiple R-squared: 0.00209, Adjusted R-squared: -0.002573
F-statistic: 0.4481 on 1 and 214 DF, p-value: 0.5039
Call:
lm(formula = AI_likingC ~ study, data = dc)
Residuals:
Min 1Q Median 3Q Max
-3.3411 -0.5411 0.0589 0.4893 2.4893
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.48043 0.21510 20.829 <2e-16 ***
study 0.03031 0.14124 0.215 0.83
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.03 on 214 degrees of freedom
Multiple R-squared: 0.0002151, Adjusted R-squared: -0.004457
F-statistic: 0.04605 on 1 and 214 DF, p-value: 0.8303
1 2
121 95
Call:
lm(formula = interest_date_this_week ~ condition * AI_competentC,
data = dc)
Residuals:
Min 1Q Median 3Q Max
-4.7288 -1.2771 0.3546 1.4500 3.7432
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6570 0.9100 5.118 6.92e-07 ***
conditionhuman 0.1174 1.2806 0.092 0.927
AI_competentC 0.1531 0.1788 0.856 0.393
conditionhuman:AI_competentC -0.0191 0.2552 -0.075 0.940
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.95 on 212 degrees of freedom
Multiple R-squared: 0.00598, Adjusted R-squared: -0.008086
F-statistic: 0.4251 on 3 and 212 DF, p-value: 0.7352
Call:
lm(formula = interest_date_this_week ~ condition * AI_mindC,
data = dc)
Residuals:
Min 1Q Median 3Q Max
-4.636 -1.220 0.180 1.448 3.905
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.8616 0.4339 11.205 <2e-16 ***
conditionhuman 0.2425 0.6120 0.396 0.692
AI_mindC 0.2336 0.1635 1.429 0.155
conditionhuman:AI_mindC -0.1180 0.2160 -0.546 0.585
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.943 on 212 degrees of freedom
Multiple R-squared: 0.01263, Adjusted R-squared: -0.001345
F-statistic: 0.9037 on 3 and 212 DF, p-value: 0.4402
Call:
lm(formula = interest_date_this_week ~ condition, data = dc)
Residuals:
Min 1Q Median 3Q Max
-4.4234 -1.4190 0.5766 1.5766 3.5810
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.419048 0.189941 28.530 <2e-16 ***
conditionhuman 0.004376 0.264962 0.017 0.987
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.946 on 214 degrees of freedom
Multiple R-squared: 1.274e-06, Adjusted R-squared: -0.004672
F-statistic: 0.0002727 on 1 and 214 DF, p-value: 0.9868
Call:
lm(formula = interest_date_this_week ~ condition * interest.dating.now,
data = dc)
Residuals:
Min 1Q Median 3Q Max
-4.7289 -0.7656 0.1036 1.2344 3.2711
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.4759 0.5105 4.850 2.39e-06
conditionhuman -1.3615 0.7338 -1.855 0.0649
interest.dating.now 0.6506 0.1075 6.051 6.44e-09
conditionhuman:interest.dating.now 0.2622 0.1517 1.729 0.0853
(Intercept) ***
conditionhuman .
interest.dating.now ***
conditionhuman:interest.dating.now .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.588 on 212 degrees of freedom
Multiple R-squared: 0.3404, Adjusted R-squared: 0.3311
F-statistic: 36.47 on 3 and 212 DF, p-value: < 2.2e-16