setwd("~/Dropbox/Research/Bernd")
#reading in each time period separately
t1<-read.csv ("SzuChi Longitudinal Data T1.csv", header=T, sep=",")
t2<-read.csv ("SzuChi Longitudinal Data T2.csv", header=T, sep=",")
t3<-read.csv ("SzuChi Longitudinal Data T3.csv", header=T, sep=",")
#assigning each dataset its time period label
t1$time<-1
t2$time<-2
t3$time<-3
#selecting only the variables we need for analysis
t1<-t1[c(1,31,32,34,35,37:88,100,101)]
t2<-t2[c(67,20:62,69)]
t3<-t3[c(64,21:63,67)]
#making sure the ID variable has the same name in each dataset
library(plyr)
t2<-rename(t2, c("prolific_pid"="prolific.id"))
t3<-rename(t3, c("PROLIFIC_PID"="prolific.id"))
### TIME 1 ANALYSIS ###
t1$interest_dating<-(t1$InterestHumanMid_1+t1$InterestHumanMid_2+t1$InterestHumanMid_3+t1$InterestHumanMid_4)/4
summary(lm(interest_dating~condition, t1))
##
## Call:
## lm(formula = interest_dating ~ condition, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6228 -0.8506 0.1494 1.1494 2.3772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.6228 0.1276 51.916 <2e-16 ***
## conditionhuman 0.2278 0.1804 1.263 0.208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.658 on 336 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.004723, Adjusted R-squared: 0.001761
## F-statistic: 1.595 on 1 and 336 DF, p-value: 0.2075
summary(lm(interest_dating~condition * int_dat, t1))
##
## Call:
## lm(formula = interest_dating ~ condition * int_dat, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.2514 -0.5225 0.0475 0.7214 3.3464
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.95520 0.34265 5.706 2.55e-08 ***
## conditionhuman -0.19936 0.52524 -0.380 0.705
## int_dat 0.89945 0.06381 14.096 < 2e-16 ***
## conditionhuman:int_dat 0.05511 0.09676 0.570 0.569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.145 on 334 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.5284, Adjusted R-squared: 0.5242
## F-statistic: 124.8 on 3 and 334 DF, p-value: < 2.2e-16
#This is just interest in long term relationship (_4) - I also tried the other 3 individual measures, no effect there
summary(lm(InterestHumanMid_4~condition, t1))
##
## Call:
## lm(formula = InterestHumanMid_4 ~ condition, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.6627 -0.6627 0.3373 1.3373 1.7574
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.2426 0.1279 56.629 <2e-16 ***
## conditionhuman 0.4201 0.1809 2.323 0.0208 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.663 on 336 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.0158, Adjusted R-squared: 0.01287
## F-statistic: 5.395 on 1 and 336 DF, p-value: 0.02079
summary(lm(InterestHumanMid_3~condition*int_dat, t1))
##
## Call:
## lm(formula = InterestHumanMid_3 ~ condition * int_dat, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.6172 -0.7110 0.2594 0.9050 3.1015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.27351 0.39380 5.773 1.78e-08 ***
## conditionhuman 0.53024 0.60365 0.878 0.380
## int_dat 0.90625 0.07334 12.357 < 2e-16 ***
## conditionhuman:int_dat -0.08344 0.11120 -0.750 0.454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.316 on 334 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.43, Adjusted R-squared: 0.4248
## F-statistic: 83.97 on 3 and 334 DF, p-value: < 2.2e-16
t1$feel_relationship<-(t1$RelationshipMid_1+t1$RelationshipMid_2)/2
summary(lm(feel_relationship~condition, t1))
##
## Call:
## lm(formula = feel_relationship ~ condition, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4615 -0.5976 0.0385 0.8114 1.5385
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.46154 0.08358 65.348 <2e-16 ***
## conditionhuman 0.13609 0.11820 1.151 0.25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.086 on 336 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.00393, Adjusted R-squared: 0.0009659
## F-statistic: 1.326 on 1 and 336 DF, p-value: 0.2504
summary(lm(feel_relationship~condition*int_dat, t1))
##
## Call:
## lm(formula = feel_relationship ~ condition * int_dat, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2249 -0.4545 0.0840 0.5840 3.3047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.27370 0.26786 12.222 < 2e-16 ***
## conditionhuman -0.55036 0.41059 -1.340 0.181
## int_dat 0.42160 0.04988 8.452 8.99e-16 ***
## conditionhuman:int_dat 0.11693 0.07564 1.546 0.123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.895 on 334 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.3281, Adjusted R-squared: 0.3221
## F-statistic: 54.36 on 3 and 334 DF, p-value: < 2.2e-16
summary(lm(RelationshipMid_1~condition, t1))
##
## Call:
## lm(formula = RelationshipMid_1 ~ condition, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3018 -0.4142 -0.3018 0.6982 1.6982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.30178 0.09219 57.509 <2e-16 ***
## conditionhuman 0.11243 0.13038 0.862 0.389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.198 on 336 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.002208, Adjusted R-squared: -0.0007615
## F-statistic: 0.7436 on 1 and 336 DF, p-value: 0.3891
summary(lm(RelationshipMid_2~condition, t1))
##
## Call:
## lm(formula = RelationshipMid_2 ~ condition, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6213 -0.6213 0.2189 1.2189 1.3787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.62130 0.08587 65.466 <2e-16 ***
## conditionhuman 0.15976 0.12143 1.316 0.189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.116 on 336 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.005125, Adjusted R-squared: 0.002164
## F-statistic: 1.731 on 1 and 336 DF, p-value: 0.1892
t1$relationship_mid<-(t1$RelationshipMid_1+t1$RelationshipMid_2)/2
summary(lm(relationship_mid~condition*int_dat, t1))
##
## Call:
## lm(formula = relationship_mid ~ condition * int_dat, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2249 -0.4545 0.0840 0.5840 3.3047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.27370 0.26786 12.222 < 2e-16 ***
## conditionhuman -0.55036 0.41059 -1.340 0.181
## int_dat 0.42160 0.04988 8.452 8.99e-16 ***
## conditionhuman:int_dat 0.11693 0.07564 1.546 0.123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.895 on 334 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.3281, Adjusted R-squared: 0.3221
## F-statistic: 54.36 on 3 and 334 DF, p-value: < 2.2e-16
t1$feelings_dating<-8-(t1$anxious_abt_dating+t1$stressed_abt_dating+t1$insecure_abt_dating+(8-t1$conf_abt_dating)+(8-t1$calm_abt_dating)+(8-t1$comf_abt_dating))/6
summary(lm(feelings_dating~condition, t1))
##
## Call:
## lm(formula = feelings_dating ~ condition, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4635 -1.0069 -0.1302 0.8264 3.6598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3402 0.1052 31.741 <2e-16 ***
## conditionhuman 0.1233 0.1488 0.828 0.408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.368 on 336 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.002038, Adjusted R-squared: -0.0009322
## F-statistic: 0.6861 on 1 and 336 DF, p-value: 0.4081
#I looked at each of the "how much you value" traits individually, only these 2 were significant
summary(lm(communication~condition, t1))
##
## Call:
## lm(formula = communication ~ condition, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3432 -0.7278 0.2722 1.2722 1.6568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.7278 0.1123 77.748 <2e-16 ***
## conditionhuman -0.3846 0.1588 -2.423 0.0159 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.459 on 336 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.01717, Adjusted R-squared: 0.01424
## F-statistic: 5.869 on 1 and 336 DF, p-value: 0.01593
summary(lm(security~condition, t1))
##
## Call:
## lm(formula = security ~ condition, data = t1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6627 -0.6627 0.3373 1.3373 1.6391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6627 0.1003 56.482 <2e-16 ***
## conditionhuman -0.3018 0.1418 -2.128 0.034 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.303 on 336 degrees of freedom
## (47 observations deleted due to missingness)
## Multiple R-squared: 0.0133, Adjusted R-squared: 0.01037
## F-statistic: 4.53 on 1 and 336 DF, p-value: 0.03403
### TIME 2 ###
#Merging time 2 data with the condition data from Time 1 since Time 2 data didnt have condition
id_cond1<-t1[c("condition", "prolific.id", "int_dat")]
time2<-merge(id_cond1, t2, by="prolific.id", all.x=FALSE)
table(time2$condition)
##
## AI human
## 7 101 103
time2<-subset(time2, condition=="AI" | condition=="human")
time2$condition<-droplevels(time2$condition)
time2$interest_dating<-(time2$InterestHumanMid_1+time2$InterestHumanMid_2+time2$InterestHumanMid_3+time2$InterestHumanMid_4)/4
summary(lm(interest_dating~condition*int_dat, time2))
##
## Call:
## lm(formula = interest_dating ~ condition * int_dat, data = time2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4516 -0.9321 0.3179 0.8412 2.5679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.37075 0.54777 4.328 2.38e-05 ***
## conditionhuman 0.11978 0.81098 0.148 0.883
## int_dat 0.81617 0.10137 8.051 7.33e-14 ***
## conditionhuman:int_dat -0.02786 0.14845 -0.188 0.851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.338 on 199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.372, Adjusted R-squared: 0.3625
## F-statistic: 39.29 on 3 and 199 DF, p-value: < 2.2e-16
summary(lm(InterestHumanMid_4~condition*int_dat, time2))
##
## Call:
## lm(formula = InterestHumanMid_4 ~ condition * int_dat, data = time2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0718 -0.7179 0.3453 0.9304 4.2711
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1437 0.6513 6.362 1.34e-09 ***
## conditionhuman -0.6108 0.9643 -0.633 0.527
## int_dat 0.5852 0.1205 4.855 2.43e-06 ***
## conditionhuman:int_dat 0.1226 0.1765 0.695 0.488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.591 on 199 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2135, Adjusted R-squared: 0.2017
## F-statistic: 18.01 on 3 and 199 DF, p-value: 2.216e-10
time2$feel_relationship<-(time2$RelationshipMid_1+time2$RelationshipMid_2)/2
summary(lm(feel_relationship~condition*int_dat, time2))
##
## Call:
## lm(formula = feel_relationship ~ condition * int_dat, data = time2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0529 -1.0529 0.4345 1.1350 4.0070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6500 0.6673 3.971 0.00010 ***
## conditionhuman -1.0529 0.9870 -1.067 0.28738
## int_dat 0.3430 0.1233 2.782 0.00593 **
## conditionhuman:int_dat 0.1507 0.1805 0.835 0.40496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.627 on 197 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.1024, Adjusted R-squared: 0.08877
## F-statistic: 7.494 on 3 and 197 DF, p-value: 8.931e-05
summary(lm(RelationshipMid_1~condition*int_dat, time2))
##
## Call:
## lm(formula = RelationshipMid_1 ~ condition * int_dat, data = time2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0359 -1.1371 0.2613 1.1461 4.4564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1452 0.6953 3.086 0.00232 **
## conditionhuman -1.2464 1.0283 -1.212 0.22695
## int_dat 0.3984 0.1285 3.101 0.00221 **
## conditionhuman:int_dat 0.1926 0.1881 1.024 0.30708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.695 on 197 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.1266, Adjusted R-squared: 0.1133
## F-statistic: 9.516 on 3 and 197 DF, p-value: 6.717e-06
time2$feelings_dating<-8-(time2$anxious_abt_dating+time2$stressed_abt_dating+time2$insecure_abt_dating+(8-time2$conf_abt_dating)+(8-time2$calm_abt_dating)+(8-time2$comf_abt_dating))/6
summary(lm(feelings_dating~condition, time2))
##
## Call:
## lm(formula = feelings_dating ~ condition, data = time2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3997 -0.8997 0.1003 0.9337 2.6100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3997 0.1261 34.877 < 2e-16 ***
## conditionhuman -0.5097 0.1762 -2.892 0.00425 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.249 on 199 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.04034, Adjusted R-squared: 0.03552
## F-statistic: 8.365 on 1 and 199 DF, p-value: 0.00425
summary(lm(learning~condition, time2))
##
## Call:
## lm(formula = learning ~ condition, data = time2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2330 -1.2330 0.1354 1.1354 3.1354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8646 0.1754 22.036 <2e-16 ***
## conditionhuman 0.3684 0.2438 1.511 0.132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.718 on 197 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.01146, Adjusted R-squared: 0.006445
## F-statistic: 2.284 on 1 and 197 DF, p-value: 0.1323
summary(lm(security~condition, time2))
##
## Call:
## lm(formula = security ~ condition, data = time2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5833 -0.6796 0.3204 1.3204 1.4167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.58333 0.13792 40.481 <2e-16 ***
## conditionhuman 0.09628 0.19171 0.502 0.616
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 197 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.001279, Adjusted R-squared: -0.003791
## F-statistic: 0.2522 on 1 and 197 DF, p-value: 0.6161
### TIME 3 ###
time3<-merge(id_cond1, t3, by="prolific.id", all.x=FALSE)
table(time3$condition)
##
## AI human
## 2 84 86
time3<-subset(time3, condition=="AI" | condition=="human")
time3$condition<-droplevels(time3$condition)
time3$interest_dating<-(time3$InterestHumanMid_1+time3$InterestHumanMid_2+time3$InterestHumanMid_3+time3$InterestHumanMid_4)/4
summary(lm(interest_dating~condition, time3))
##
## Call:
## lm(formula = interest_dating ~ condition, data = time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7411 -0.8169 0.2589 1.2589 2.2589
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.74107 0.19423 34.707 <2e-16 ***
## conditionhuman 0.07579 0.27308 0.278 0.782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.78 on 168 degrees of freedom
## Multiple R-squared: 0.0004583, Adjusted R-squared: -0.005491
## F-statistic: 0.07703 on 1 and 168 DF, p-value: 0.7817
summary(lm(InterestHumanMid_4~condition*int_dat, time3))
##
## Call:
## lm(formula = InterestHumanMid_4 ~ condition * int_dat, data = time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2806 -0.5269 0.3112 0.9658 3.9661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5029 0.7390 6.094 7.46e-09 ***
## conditionhuman -0.9905 1.0680 -0.927 0.355039
## int_dat 0.5310 0.1361 3.901 0.000139 ***
## conditionhuman:int_dat 0.2227 0.1951 1.141 0.255508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.547 on 166 degrees of freedom
## Multiple R-squared: 0.215, Adjusted R-squared: 0.2008
## F-statistic: 15.15 on 3 and 166 DF, p-value: 9.194e-09
time3$feel_relationship<-(time3$RelationshipMid_1+time3$RelationshipMid_2)/2
summary(lm(feel_relationship~condition, time3))
##
## Call:
## lm(formula = feel_relationship ~ condition, data = time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.494 -1.401 0.506 1.506 2.599
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.49398 0.20121 22.335 <2e-16 ***
## conditionhuman -0.09281 0.28206 -0.329 0.743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.833 on 167 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.000648, Adjusted R-squared: -0.005336
## F-statistic: 0.1083 on 1 and 167 DF, p-value: 0.7425
summary(lm(RelationshipMid_1~condition*int_dat, time3))
##
## Call:
## lm(formula = RelationshipMid_1 ~ condition * int_dat, data = time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8712 -1.4473 0.4601 1.4601 3.9202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7878 0.8920 3.125 0.0021 **
## conditionhuman -0.8840 1.2842 -0.688 0.4922
## int_dat 0.2920 0.1650 1.770 0.0785 .
## conditionhuman:int_dat 0.1319 0.2351 0.561 0.5756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.853 on 165 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.05593, Adjusted R-squared: 0.03877
## F-statistic: 3.258 on 3 and 165 DF, p-value: 0.02306
time3$feelings_dating<-8-(time3$anxious_abt_dating+time3$stressed_abt_dating+time3$insecure_abt_dating+(8-time3$conf_abt_dating)+(8-time3$calm_abt_dating)+(8-time3$comf_abt_dating))/6
summary(lm(feelings_dating~condition, time3))
##
## Call:
## lm(formula = feelings_dating ~ condition, data = time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3869 -0.9651 -0.0093 0.9464 2.7016
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3869 0.1433 30.607 <2e-16 ***
## conditionhuman -0.4218 0.2015 -2.093 0.0378 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.314 on 168 degrees of freedom
## Multiple R-squared: 0.02541, Adjusted R-squared: 0.01961
## F-statistic: 4.381 on 1 and 168 DF, p-value: 0.03784
summary(lm(communication~condition, time3))
##
## Call:
## lm(formula = communication ~ condition, data = time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7108 -0.7108 0.2892 1.2892 1.3837
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.71084 0.15180 57.384 <2e-16 ***
## conditionhuman -0.09456 0.21280 -0.444 0.657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.383 on 167 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.001181, Adjusted R-squared: -0.0048
## F-statistic: 0.1975 on 1 and 167 DF, p-value: 0.6573
summary(lm(security~condition, time3))
##
## Call:
## lm(formula = security ~ condition, data = time3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.686 -0.686 0.314 1.314 1.422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5783 0.1483 37.620 <2e-16 ***
## conditionhuman 0.1077 0.2079 0.518 0.605
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.351 on 167 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.001606, Adjusted R-squared: -0.004372
## F-statistic: 0.2686 on 1 and 167 DF, p-value: 0.6049