R Notebook
Notes
This was the first experiment of the summer. I have not felt good about status ascriptions as a DV. I am curious about other outcomes.
Design
This is what I think will be Study 1 - starting where Mooijman and Atari left off.
I asked participants to imagine that they were in a facebook group
with the following moral composition:
- HIGH Moral diversity.
- RELATIVE Moral diversity.
- LOW Moral diversity.
I presented participants with the following measures. However, I will ask them about people in the environment, rather than the actrualy environment.
CULTURAL TIGHTNESS.
- There are many social norms that people should abide by in this
Facebook group.
- In this Facebook group, there are very clear expectations for how
people should act in most situations.
- In this Facebook group, people agree upon what behaviors are
appropriate versus inappropriate in most situations.
- People in this Facebook group have a great deal of freedom in deciding
how they want to behave in most situations.
- In this Facebook group, if someone acts in an inappropriate way,
others will strongly disapprove.
- People in this Facebook group almost always comply with social
norms.
SENSE OF POWER.
- can get others in the group to listen to what he says.
- can get others in the group to do what he wants.
- has views with little sway in the group.
- has a great deal of power in the group.
- has ideas and opinions that are often ignored.
- is not able to get his way in the group, even if he tries.
- gets to make the decisions in the group, if he wants to.
Backlash.
- they like him.
- they dislike him.
- he is popular with them.
- they are happy that he is in the group.
- they think he offers a positive contribution to the group.
Report They would report Fred.
Flags They would flag Fred’s message as inappropriate.
Load data
Replicate Tightness Measures
## Df Sum Sq Mean Sq F value Pr(>F)
## cond 2 7.46 3.729 3.512 0.0353 *
## Residuals 69 73.27 1.062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(aov(sop~cond, expdv1_clean))## Df Sum Sq Mean Sq F value Pr(>F)
## cond 2 21.2 10.60 9.727 0.00019 ***
## Residuals 69 75.2 1.09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(tight~cond, expdv1_clean))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = tight ~ cond, data = expdv1_clean)
##
## $cond
## diff lwr upr p adj
## 2. Relative-1. Homogeneous -0.7333833 -1.4225867 -0.04417989 0.0344058
## 3. Diverse-1. Homogeneous -0.6057971 -1.3604791 0.14888488 0.1399606
## 3. Diverse-2. Relative 0.1275862 -0.5898656 0.84503798 0.9049711summary(aov(backlash~cond, expdv1_clean))## Df Sum Sq Mean Sq F value Pr(>F)
## cond 2 40.32 20.158 16.98 1.01e-06 ***
## Residuals 69 81.91 1.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(backlash~cond, expdv1_clean))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = backlash ~ cond, data = expdv1_clean)
##
## $cond
## diff lwr upr p adj
## 2. Relative-1. Homogeneous 0.9085457 0.1798541 1.637237 0.0107634
## 3. Diverse-1. Homogeneous 1.9413043 1.1433825 2.739226 0.0000005
## 3. Diverse-2. Relative 1.0327586 0.2742001 1.791317 0.0048530summary(aov(report~cond, expdv1_clean))## Df Sum Sq Mean Sq F value Pr(>F)
## cond 2 39.06 19.532 10.83 8.12e-05 ***
## Residuals 69 124.44 1.803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(report~cond, expdv1_clean))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = report ~ cond, data = expdv1_clean)
##
## $cond
## diff lwr upr p adj
## 2. Relative-1. Homogeneous -0.8155922 -1.713746 0.08256187 0.0826877
## 3. Diverse-1. Homogeneous -1.9086957 -2.892180 -0.92521142 0.0000459
## 3. Diverse-2. Relative -1.0931034 -2.028070 -0.15813677 0.0179403summary(aov(flag~cond, expdv1_clean))## Df Sum Sq Mean Sq F value Pr(>F)
## cond 2 52.13 26.067 12.52 2.29e-05 ***
## Residuals 69 143.64 2.082
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov(flag~cond, expdv1_clean))## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = flag ~ cond, data = expdv1_clean)
##
## $cond
## diff lwr upr p adj
## 2. Relative-1. Homogeneous -1.2158921 -2.180877 -0.25090671 0.0098455
## 3. Diverse-1. Homogeneous -2.1934783 -3.250143 -1.13681338 0.0000137
## 3. Diverse-2. Relative -0.9775862 -1.982123 0.02695095 0.0581446Control Variables
##
## Call:
## lm(formula = tight ~ cond + issue_f + feeling_affirm_1 + FB_use_1 +
## Conservatism + Race, data = expdv1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.42526 -0.61211 -0.05452 0.57456 2.12417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.176582 0.365291 11.434 <2e-16 ***
## cond2. Relative -0.633761 0.313024 -2.025 0.0473 *
## cond3. Diverse -0.455082 0.334447 -1.361 0.1786
## issue_fCheating 0.683692 0.573396 1.192 0.2377
## issue_fPatriotism 0.766726 0.449388 1.706 0.0931 .
## issue_fInsubordination 1.131199 1.107145 1.022 0.3109
## issue_fDegradation 0.431159 0.625674 0.689 0.4934
## feeling_affirm_1 0.058352 0.081595 0.715 0.4773
## FB_use_1 -0.002290 0.003726 -0.615 0.5411
## Conservatism -0.130119 0.104558 -1.244 0.2181
## Race1,5 0.714974 0.819917 0.872 0.3866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.04 on 61 degrees of freedom
## Multiple R-squared: 0.1829, Adjusted R-squared: 0.0489
## F-statistic: 1.365 on 10 and 61 DF, p-value: 0.2181##
## Call:
## lm(formula = sop ~ cond + issue_f + feeling_affirm_1 + FB_use_1 +
## Conservatism + Race, data = expdv1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.30594 -0.59031 0.00477 0.86535 1.86930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5676886 0.3798569 9.392 1.86e-13 ***
## cond2. Relative 0.5189501 0.3255060 1.594 0.116041
## cond3. Diverse 1.3519403 0.3477830 3.887 0.000253 ***
## issue_fCheating 0.4109846 0.5962608 0.689 0.493267
## issue_fPatriotism -0.2207060 0.4673080 -0.472 0.638403
## issue_fInsubordination -0.8189589 1.1512933 -0.711 0.479586
## issue_fDegradation 0.6155979 0.6506235 0.946 0.347797
## feeling_affirm_1 -0.0031795 0.0848490 -0.037 0.970231
## FB_use_1 -0.0001149 0.0038746 -0.030 0.976437
## Conservatism 0.0157816 0.1087275 0.145 0.885073
## Race1,5 -0.7953938 0.8526116 -0.933 0.354554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.081 on 61 degrees of freedom
## Multiple R-squared: 0.2601, Adjusted R-squared: 0.1388
## F-statistic: 2.144 on 10 and 61 DF, p-value: 0.03404##
## Call:
## lm(formula = backlash ~ cond + issue_f + feeling_affirm_1 + FB_use_1 +
## Conservatism + Race, data = expdv1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.99849 -0.79386 0.00482 0.98861 2.07126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1084956 0.3884247 8.003 4.35e-11 ***
## cond2. Relative 0.9072257 0.3328479 2.726 0.00836 **
## cond3. Diverse 1.8911327 0.3556273 5.318 1.58e-06 ***
## issue_fCheating -0.0721871 0.6097097 -0.118 0.90614
## issue_fPatriotism 0.6422441 0.4778482 1.344 0.18392
## issue_fInsubordination 0.1369855 1.1772612 0.116 0.90775
## issue_fDegradation -0.2548892 0.6652985 -0.383 0.70296
## feeling_affirm_1 0.0570999 0.0867628 0.658 0.51294
## FB_use_1 0.0005617 0.0039620 0.142 0.88772
## Conservatism -0.0975913 0.1111799 -0.878 0.38351
## Race1,5 -1.1325636 0.8718425 -1.299 0.19882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.106 on 61 degrees of freedom
## Multiple R-squared: 0.3897, Adjusted R-squared: 0.2897
## F-statistic: 3.896 on 10 and 61 DF, p-value: 0.0004019##
## Call:
## lm(formula = report ~ cond + issue_f + feeling_affirm_1 + FB_use_1 +
## Conservatism + Race, data = expdv1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7237 -0.7447 -0.3799 0.9022 2.6004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.076742 0.477000 8.547 5.07e-12 ***
## cond2. Relative -0.764793 0.408750 -1.871 0.0661 .
## cond3. Diverse -1.909598 0.436724 -4.373 4.87e-05 ***
## issue_fCheating 0.479633 0.748746 0.641 0.5242
## issue_fPatriotism -0.502560 0.586815 -0.856 0.3951
## issue_fInsubordination -0.370428 1.445720 -0.256 0.7986
## issue_fDegradation -1.114565 0.817011 -1.364 0.1775
## feeling_affirm_1 0.049111 0.106548 0.461 0.6465
## FB_use_1 0.002942 0.004865 0.605 0.5476
## Conservatism 0.090186 0.136533 0.661 0.5114
## Race1,5 0.611691 1.070655 0.571 0.5699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.358 on 61 degrees of freedom
## Multiple R-squared: 0.312, Adjusted R-squared: 0.1992
## F-statistic: 2.766 on 10 and 61 DF, p-value: 0.006989##
## Call:
## lm(formula = flag ~ cond + issue_f + feeling_affirm_1 + FB_use_1 +
## Conservatism + Race, data = expdv1_clean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.99693 -0.87918 -0.01035 1.08804 2.68927
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.578086 0.520552 8.795 1.91e-12 ***
## cond2. Relative -1.199082 0.446070 -2.688 0.00925 **
## cond3. Diverse -2.194623 0.476598 -4.605 2.15e-05 ***
## issue_fCheating 0.340214 0.817109 0.416 0.67861
## issue_fPatriotism -0.435552 0.640394 -0.680 0.49899
## issue_fInsubordination 0.661786 1.577720 0.419 0.67636
## issue_fDegradation -0.953806 0.891607 -1.070 0.28894
## feeling_affirm_1 0.077422 0.116276 0.666 0.50802
## FB_use_1 0.003228 0.005310 0.608 0.54554
## Conservatism 0.037806 0.148999 0.254 0.80056
## Race1,5 0.266626 1.168410 0.228 0.82026
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.482 on 61 degrees of freedom
## Multiple R-squared: 0.3157, Adjusted R-squared: 0.2036
## F-statistic: 2.815 on 10 and 61 DF, p-value: 0.006179Graphs
Moderation results
Mediation
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = backlash ~ convergence + (tight), data = expdv1_clean)
##
## The DV (Y) was backlash . The IV (X) was convergence . The mediating variable(s) = tight .
##
## Total effect(c) of convergence on backlash = -0.97 S.E. = 0.17 t = -5.86 df= 70 with p = 1.4e-07
## Direct effect (c') of convergence on backlash removing tight = -0.96 S.E. = 0.17 t = -5.62 df= 69 with p = 3.7e-07
## Indirect effect (ab) of convergence on backlash through tight = -0.01
## Mean bootstrapped indirect effect = -0.01 with standard error = 0.05 Lower CI = -0.13 Upper CI = 0.07
## R = 0.57 R2 = 0.33 F = 16.97 on 2 and 69 DF p-value: 2.34e-08
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = flag ~ convergence + (tight), data = expdv1_clean)
##
## The DV (Y) was flag . The IV (X) was convergence . The mediating variable(s) = tight .
##
## Total effect(c) of convergence on flag = 1.1 S.E. = 0.22 t = 5.02 df= 70 with p = 3.7e-06
## Direct effect (c') of convergence on flag removing tight = 1.07 S.E. = 0.23 t = 4.74 df= 69 with p = 1.1e-05
## Indirect effect (ab) of convergence on flag through tight = 0.03
## Mean bootstrapped indirect effect = 0.04 with standard error = 0.07 Lower CI = -0.07 Upper CI = 0.21
## R = 0.52 R2 = 0.27 F = 12.66 on 2 and 69 DF p-value: 1.1e-06
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = report ~ convergence + (tight), data = expdv1_clean)
##
## The DV (Y) was report . The IV (X) was convergence . The mediating variable(s) = tight .
##
## Total effect(c) of convergence on report = 0.95 S.E. = 0.2 t = 4.66 df= 70 with p = 1.5e-05
## Direct effect (c') of convergence on report removing tight = 0.95 S.E. = 0.21 t = 4.48 df= 69 with p = 2.9e-05
## Indirect effect (ab) of convergence on report through tight = 0
## Mean bootstrapped indirect effect = 0.01 with standard error = 0.06 Lower CI = -0.1 Upper CI = 0.16
## R = 0.49 R2 = 0.24 F = 10.72 on 2 and 69 DF p-value: 7.23e-06
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = sop ~ convergence + (tight), data = expdv1_clean)
##
## The DV (Y) was sop . The IV (X) was convergence . The mediating variable(s) = tight .
##
## Total effect(c) of convergence on sop = -0.7 S.E. = 0.16 t = -4.42 df= 70 with p = 3.5e-05
## Direct effect (c') of convergence on sop removing tight = -0.67 S.E. = 0.16 t = -4.12 df= 69 with p = 1e-04
## Indirect effect (ab) of convergence on sop through tight = -0.03
## Mean bootstrapped indirect effect = -0.03 with standard error = 0.05 Lower CI = -0.16 Upper CI = 0.05
## R = 0.47 R2 = 0.22 F = 9.98 on 2 and 69 DF p-value: 1.51e-05
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = sop ~ convergence + (backlash), data = expdv1_clean)
##
## The DV (Y) was sop . The IV (X) was convergence . The mediating variable(s) = backlash .
##
## Total effect(c) of convergence on sop = -0.7 S.E. = 0.16 t = -4.42 df= 70 with p = 3.5e-05
## Direct effect (c') of convergence on sop removing backlash = -0.01 S.E. = 0.13 t = -0.05 df= 69 with p = 0.96
## Indirect effect (ab) of convergence on sop through backlash = -0.69
## Mean bootstrapped indirect effect = -0.69 with standard error = 0.16 Lower CI = -1.03 Upper CI = -0.41
## R = 0.81 R2 = 0.65 F = 65.44 on 2 and 69 DF p-value: 3.83e-20
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = backlash ~ convergence + (sop), data = expdv1_clean)
##
## The DV (Y) was backlash . The IV (X) was convergence . The mediating variable(s) = sop .
##
## Total effect(c) of convergence on backlash = -0.97 S.E. = 0.17 t = -5.86 df= 70 with p = 1.4e-07
## Direct effect (c') of convergence on backlash removing sop = -0.42 S.E. = 0.13 t = -3.38 df= 69 with p = 0.0012
## Indirect effect (ab) of convergence on backlash through sop = -0.55
## Mean bootstrapped indirect effect = -0.54 with standard error = 0.14 Lower CI = -0.82 Upper CI = -0.28
## R = 0.84 R2 = 0.7 F = 81.99 on 2 and 69 DF p-value: 1.06e-22
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = report ~ convergence + (backlash), data = expdv1_clean)
##
## The DV (Y) was report . The IV (X) was convergence . The mediating variable(s) = backlash .
##
## Total effect(c) of convergence on report = 0.95 S.E. = 0.2 t = 4.66 df= 70 with p = 1.5e-05
## Direct effect (c') of convergence on report removing backlash = 0.15 S.E. = 0.19 t = 0.79 df= 69 with p = 0.43
## Indirect effect (ab) of convergence on report through backlash = 0.8
## Mean bootstrapped indirect effect = 0.8 with standard error = 0.18 Lower CI = 0.47 Upper CI = 1.18
## R = 0.76 R2 = 0.58 F = 48.15 on 2 and 69 DF p-value: 6.67e-17
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary
##
## Mediation/Moderation Analysis
## Call: psych::mediate(y = flag ~ convergence + (backlash), data = expdv1_clean)
##
## The DV (Y) was flag . The IV (X) was convergence . The mediating variable(s) = backlash .
##
## Total effect(c) of convergence on flag = 1.1 S.E. = 0.22 t = 5.02 df= 70 with p = 3.7e-06
## Direct effect (c') of convergence on flag removing backlash = 0.19 S.E. = 0.19 t = 1.01 df= 69 with p = 0.32
## Indirect effect (ab) of convergence on flag through backlash = 0.91
## Mean bootstrapped indirect effect = 0.92 with standard error = 0.2 Lower CI = 0.55 Upper CI = 1.32
## R = 0.8 R2 = 0.63 F = 59.5 on 2 and 69 DF p-value: 4.17e-19
##
## To see the longer output, specify short = FALSE in the print statement or ask for the summary