library(afex) # to run the ANOVA and plot results
library(psych) # for the describe() command
library(ggplot2) # to visualize our results
library(expss) # for the cross_cases() command
library(car) # for the leveneTest() command
library(emmeans) # for posthoc tests
library(effsize) # for the cohen.d() command
library(apaTables) # to create our correlation table
library(kableExtra) # to create our correlation table
library(sjPlot) # to visualize our resultsAI Experiment Analysis
Loading Libraries
Importing Data
# import your AI results dataset
d <- read.csv(file="Data/final_results_100.csv", header=T)State Your Hypotheses & Chosen Tests
First t-test: participants with more social media use will have lower life satisfaction.
Second t-test: younger participants (18-30 years old) will have lower life satisfaction than older participants (older than 30 years old).
p-value is 0.025 because we are using the same sample for 2 different t-tests
Check Your Variables
This is just basic variable checking that is used across all HW assignments.
# to view stats for all variables
describe(d) vars n mean sd median trimmed mad min max range skew
id 1 100 50.50 29.01 50.5 50.50 37.06 1 100 99 0.00
identity* 2 100 50.50 29.01 50.5 50.50 37.06 1 100 99 0.00
consent* 3 100 1.46 0.50 1.0 1.45 0.00 1 2 1 0.16
age 4 100 42.70 15.84 38.0 40.92 10.38 18 99 81 1.14
race 5 100 4.66 1.59 6.0 4.71 1.48 1 7 6 -0.27
gender 6 100 1.94 0.24 2.0 2.00 0.00 1 2 1 -3.65
manip_out* 7 100 38.42 17.62 41.0 39.40 19.27 1 68 67 -0.48
survey1* 8 100 16.91 10.98 14.0 16.21 10.38 1 42 41 0.43
survey2* 9 100 1.82 0.39 2.0 1.90 0.00 1 2 1 -1.64
ai_manip* 10 100 50.40 28.86 50.5 50.50 37.06 1 99 98 -0.01
condition 11 100 1.50 0.50 1.5 1.50 0.74 1 2 1 0.00
kurtosis se
id -1.24 2.90
identity* -1.24 2.90
consent* -1.99 0.05
age 1.09 1.58
race -1.34 0.16
gender 11.44 0.02
manip_out* -0.78 1.76
survey1* -0.66 1.10
survey2* 0.70 0.04
ai_manip* -1.25 2.89
condition -2.02 0.05# we'll use the describeBy() command to view skew and kurtosis across our IVs
describeBy(d, group = "survey1")
Descriptive statistics by group
survey1: 1
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 6.0 2.83 6.0 6.0 2.97 4 8 4 0 -2.75
identity 2 2 28.0 38.18 28.0 28.0 40.03 1 55 54 0 -2.75
consent 3 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
age 4 2 21.5 4.95 21.5 21.5 5.19 18 25 7 0 -2.75
race 5 2 6.0 0.00 6.0 6.0 0.00 6 6 0 NaN NaN
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 25.0 22.63 25.0 25.0 23.72 9 41 32 0 -2.75
survey1 8 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
survey2 9 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
ai_manip 10 2 64.0 2.83 64.0 64.0 2.97 62 66 4 0 -2.75
condition 11 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
se
id 2.0
identity 27.0
consent 0.0
age 3.5
race 0.0
gender 0.0
manip_out 16.0
survey1 0.0
survey2 0.0
ai_manip 2.0
condition 0.0
------------------------------------------------------------
survey1: 2
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 14 35.07 25.74 27.0 32.42 17.05 2 100 98 1.11 0.48
identity 2 14 52.21 33.03 57.5 52.33 41.51 3 100 97 -0.13 -1.55
consent 3 14 1.64 0.50 2.0 1.67 0.00 1 2 1 -0.53 -1.83
age 4 14 36.79 10.48 35.5 35.67 10.38 24 63 39 0.91 0.25
race 5 14 5.43 1.40 6.0 5.58 0.00 2 7 5 -1.35 0.44
gender 6 14 1.93 0.27 2.0 2.00 0.00 1 2 1 -2.98 7.41
manip_out 7 14 34.14 23.69 32.5 33.92 31.13 3 68 65 0.11 -1.59
survey1 8 14 2.00 0.00 2.0 2.00 0.00 2 2 0 NaN NaN
survey2 9 14 1.64 0.50 2.0 1.67 0.00 1 2 1 -0.53 -1.83
ai_manip 10 14 39.86 33.92 27.0 38.75 34.84 3 90 87 0.27 -1.76
condition 11 14 1.14 0.36 1.0 1.08 0.00 1 2 1 1.83 1.45
se
id 6.88
identity 8.83
consent 0.13
age 2.80
race 0.37
gender 0.07
manip_out 6.33
survey1 0.00
survey2 0.13
ai_manip 9.07
condition 0.10
------------------------------------------------------------
survey1: 3
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 75 NA 75 75 0 75 75 0 NA NA NA
identity 2 1 66 NA 66 66 0 66 66 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 34 NA 34 34 0 34 34 0 NA NA NA
race 5 1 6 NA 6 6 0 6 6 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 56 NA 56 56 0 56 56 0 NA NA NA
survey1 8 1 3 NA 3 3 0 3 3 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 36 NA 36 36 0 36 36 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 4
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 89 NA 89 89 0 89 89 0 NA NA NA
identity 2 1 33 NA 33 33 0 33 33 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 49 NA 49 49 0 49 49 0 NA NA NA
race 5 1 3 NA 3 3 0 3 3 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 54 NA 54 54 0 54 54 0 NA NA NA
survey1 8 1 4 NA 4 4 0 4 4 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 86 NA 86 86 0 86 86 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 5
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 60 NA 60 60 0 60 60 0 NA NA NA
identity 2 1 39 NA 39 39 0 39 39 0 NA NA NA
consent 3 1 2 NA 2 2 0 2 2 0 NA NA NA
age 4 1 70 NA 70 70 0 70 70 0 NA NA NA
race 5 1 1 NA 1 1 0 1 1 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 52 NA 52 52 0 52 52 0 NA NA NA
survey1 8 1 5 NA 5 5 0 5 5 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 6 NA 6 6 0 6 6 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 6
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 95 NA 95 95 0 95 95 0 NA NA NA
identity 2 1 32 NA 32 32 0 32 32 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 46 NA 46 46 0 46 46 0 NA NA NA
race 5 1 6 NA 6 6 0 6 6 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 50 NA 50 50 0 50 50 0 NA NA NA
survey1 8 1 6 NA 6 6 0 6 6 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 45 NA 45 45 0 45 45 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 7
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 91 NA 91 91 0 91 91 0 NA NA NA
identity 2 1 13 NA 13 13 0 13 13 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 32 NA 32 32 0 32 32 0 NA NA NA
race 5 1 4 NA 4 4 0 4 4 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 54 NA 54 54 0 54 54 0 NA NA NA
survey1 8 1 7 NA 7 7 0 7 7 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 30 NA 30 30 0 30 30 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 8
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 66 NA 66 66 0 66 66 0 NA NA NA
identity 2 1 99 NA 99 99 0 99 99 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 74 NA 74 74 0 74 74 0 NA NA NA
race 5 1 3 NA 3 3 0 3 3 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 50 NA 50 50 0 50 50 0 NA NA NA
survey1 8 1 8 NA 8 8 0 8 8 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 51 NA 51 51 0 51 51 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 9
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 66.0 2.83 66.0 66.0 2.97 64 68 4 0 -2.75
identity 2 2 44.0 48.08 44.0 44.0 50.41 10 78 68 0 -2.75
consent 3 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
age 4 2 31.5 10.61 31.5 31.5 11.12 24 39 15 0 -2.75
race 5 2 6.0 0.00 6.0 6.0 0.00 6 6 0 NaN NaN
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 57.5 4.95 57.5 57.5 5.19 54 61 7 0 -2.75
survey1 8 2 9.0 0.00 9.0 9.0 0.00 9 9 0 NaN NaN
survey2 9 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75
ai_manip 10 2 55.5 38.89 55.5 55.5 40.77 28 83 55 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 2.0
identity 34.0
consent 0.0
age 7.5
race 0.0
gender 0.0
manip_out 3.5
survey1 0.0
survey2 0.5
ai_manip 27.5
condition 0.0
------------------------------------------------------------
survey1: 10
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 4 79.25 18.48 86.0 79.25 5.93 52 93 41 -0.67 -1.73
identity 2 4 34.75 28.14 26.5 34.75 17.79 12 74 62 0.48 -1.91
consent 3 4 1.00 0.00 1.0 1.00 0.00 1 1 0 NaN NaN
age 4 4 39.75 13.15 35.0 39.75 5.19 30 59 29 0.65 -1.76
race 5 4 5.25 1.50 6.0 5.25 0.00 3 6 3 -0.75 -1.69
gender 6 4 1.75 0.50 2.0 1.75 0.00 1 2 1 -0.75 -1.69
manip_out 7 4 57.50 2.65 58.0 57.50 2.22 54 60 6 -0.32 -2.01
survey1 8 4 10.00 0.00 10.0 10.00 0.00 10 10 0 NaN NaN
survey2 9 4 1.75 0.50 2.0 1.75 0.00 1 2 1 -0.75 -1.69
ai_manip 10 4 56.00 24.95 53.0 56.00 20.76 29 89 60 0.26 -1.89
condition 11 4 2.00 0.00 2.0 2.00 0.00 2 2 0 NaN NaN
se
id 9.24
identity 14.07
consent 0.00
age 6.57
race 0.75
gender 0.25
manip_out 1.32
survey1 0.00
survey2 0.25
ai_manip 12.48
condition 0.00
------------------------------------------------------------
survey1: 11
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 94.5 3.54 94.5 94.5 3.71 92 97 5 0 -2.75
identity 2 2 55.5 44.55 55.5 55.5 46.70 24 87 63 0 -2.75
consent 3 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75
age 4 2 44.5 7.78 44.5 44.5 8.15 39 50 11 0 -2.75
race 5 2 4.5 2.12 4.5 4.5 2.22 3 6 3 0 -2.75
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 43.5 3.54 43.5 43.5 3.71 41 46 5 0 -2.75
survey1 8 2 11.0 0.00 11.0 11.0 0.00 11 11 0 NaN NaN
survey2 9 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
ai_manip 10 2 61.0 28.28 61.0 61.0 29.65 41 81 40 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 2.5
identity 31.5
consent 0.5
age 5.5
race 1.5
gender 0.0
manip_out 2.5
survey1 0.0
survey2 0.0
ai_manip 20.0
condition 0.0
------------------------------------------------------------
survey1: 12
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 76.0 4.24 76.0 76.0 4.45 73 79 6 0 -2.75
identity 2 2 57.5 0.71 57.5 57.5 0.74 57 58 1 0 -2.75
consent 3 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
age 4 2 28.0 0.00 28.0 28.0 0.00 28 28 0 NaN NaN
race 5 2 6.0 0.00 6.0 6.0 0.00 6 6 0 NaN NaN
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 41.0 0.00 41.0 41.0 0.00 41 41 0 NaN NaN
survey1 8 2 12.0 0.00 12.0 12.0 0.00 12 12 0 NaN NaN
survey2 9 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
ai_manip 10 2 65.5 13.44 65.5 65.5 14.08 56 75 19 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 3.0
identity 0.5
consent 0.0
age 0.0
race 0.0
gender 0.0
manip_out 0.0
survey1 0.0
survey2 0.0
ai_manip 9.5
condition 0.0
------------------------------------------------------------
survey1: 13
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 62 NA 62 62 0 62 62 0 NA NA NA
identity 2 1 92 NA 92 92 0 92 92 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 54 NA 54 54 0 54 54 0 NA NA NA
race 5 1 3 NA 3 3 0 3 3 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 43 NA 43 43 0 43 43 0 NA NA NA
survey1 8 1 13 NA 13 13 0 13 13 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 77 NA 77 77 0 77 77 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 14
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 19 26.47 14.96 25 26.41 19.27 5 49 44 0.09 -1.56
identity 2 19 44.79 26.65 42 44.06 25.20 8 94 86 0.63 -0.96
consent 3 19 1.63 0.50 2 1.65 0.00 1 2 1 -0.50 -1.84
age 4 19 47.26 17.98 39 46.59 8.90 23 83 60 0.79 -0.69
race 5 19 4.42 1.57 4 4.41 1.48 2 7 5 0.14 -1.71
gender 6 19 1.89 0.32 2 1.94 0.00 1 2 1 -2.37 3.84
manip_out 7 19 27.00 16.00 25 26.24 16.31 1 66 65 0.41 -0.15
survey1 8 19 14.00 0.00 14 14.00 0.00 14 14 0 NaN NaN
survey2 9 19 1.89 0.32 2 1.94 0.00 1 2 1 -2.37 3.84
ai_manip 10 19 51.11 28.67 63 51.88 22.24 1 88 87 -0.47 -1.43
condition 11 19 1.00 0.00 1 1.00 0.00 1 1 0 NaN NaN
se
id 3.43
identity 6.11
consent 0.11
age 4.12
race 0.36
gender 0.07
manip_out 3.67
survey1 0.00
survey2 0.07
ai_manip 6.58
condition 0.00
------------------------------------------------------------
survey1: 15
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 22 NA 22 22 0 22 22 0 NA NA NA
identity 2 1 4 NA 4 4 0 4 4 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 39 NA 39 39 0 39 39 0 NA NA NA
race 5 1 2 NA 2 2 0 2 2 0 NA NA NA
gender 6 1 1 NA 1 1 0 1 1 0 NA NA NA
manip_out 7 1 4 NA 4 4 0 4 4 0 NA NA NA
survey1 8 1 15 NA 15 15 0 15 15 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 14 NA 14 14 0 14 14 0 NA NA NA
condition 11 1 1 NA 1 1 0 1 1 0 NA NA NA
------------------------------------------------------------
survey1: 16
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 5 26.8 16.21 31 26.8 16.31 3 46 43 -0.29 -1.66
identity 2 5 75.2 17.70 79 75.2 25.20 53 97 44 -0.07 -1.96
consent 3 5 1.6 0.55 2 1.6 0.00 1 2 1 -0.29 -2.25
age 4 5 40.2 16.63 41 40.2 13.34 19 64 45 0.15 -1.62
race 5 5 4.2 1.64 3 4.2 0.00 3 6 3 0.29 -2.25
gender 6 5 2.0 0.00 2 2.0 0.00 2 2 0 NaN NaN
manip_out 7 5 17.6 8.82 15 17.6 11.86 7 29 22 0.13 -1.95
survey1 8 5 16.0 0.00 16 16.0 0.00 16 16 0 NaN NaN
survey2 9 5 1.8 0.45 2 1.8 0.00 1 2 1 -1.07 -0.92
ai_manip 10 5 45.6 44.29 16 45.6 10.38 9 96 87 0.29 -2.24
condition 11 5 1.0 0.00 1 1.0 0.00 1 1 0 NaN NaN
se
id 7.25
identity 7.91
consent 0.24
age 7.44
race 0.73
gender 0.00
manip_out 3.94
survey1 0.00
survey2 0.20
ai_manip 19.81
condition 0.00
------------------------------------------------------------
survey1: 17
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 36.5 6.36 36.5 36.5 6.67 32 41 9 0 -2.75
identity 2 2 75.0 8.49 75.0 75.0 8.90 69 81 12 0 -2.75
consent 3 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
age 4 2 38.0 5.66 38.0 38.0 5.93 34 42 8 0 -2.75
race 5 2 5.0 1.41 5.0 5.0 1.48 4 6 2 0 -2.75
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 27.0 9.90 27.0 27.0 10.38 20 34 14 0 -2.75
survey1 8 2 17.0 0.00 17.0 17.0 0.00 17 17 0 NaN NaN
survey2 9 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
ai_manip 10 2 59.5 55.86 59.5 59.5 58.56 20 99 79 0 -2.75
condition 11 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
se
id 4.5
identity 6.0
consent 0.0
age 4.0
race 1.0
gender 0.0
manip_out 7.0
survey1 0.0
survey2 0.0
ai_manip 39.5
condition 0.0
------------------------------------------------------------
survey1: 18
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 4 34.25 7.80 33.0 34.25 7.41 27 44 17 0.22 -2.15
identity 2 4 39.25 37.23 37.5 39.25 43.74 2 80 78 0.05 -2.31
consent 3 4 2.00 0.00 2.0 2.00 0.00 2 2 0 NaN NaN
age 4 4 31.50 8.19 32.0 31.50 6.67 21 41 20 -0.14 -1.87
race 5 4 6.25 0.50 6.0 6.25 0.00 6 7 1 0.75 -1.69
gender 6 4 2.00 0.00 2.0 2.00 0.00 2 2 0 NaN NaN
manip_out 7 4 32.50 23.27 29.0 32.50 17.05 8 64 56 0.32 -1.85
survey1 8 4 18.00 0.00 18.0 18.00 0.00 18 18 0 NaN NaN
survey2 9 4 1.75 0.50 2.0 1.75 0.00 1 2 1 -0.75 -1.69
ai_manip 10 4 38.75 40.80 33.0 38.75 41.51 2 87 85 0.15 -2.25
condition 11 4 1.00 0.00 1.0 1.00 0.00 1 1 0 NaN NaN
se
id 3.90
identity 18.62
consent 0.00
age 4.09
race 0.25
gender 0.00
manip_out 11.64
survey1 0.00
survey2 0.25
ai_manip 20.40
condition 0.00
------------------------------------------------------------
survey1: 19
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 4 18.50 16.09 13.0 18.50 6.67 6 42 36 0.63 -1.76
identity 2 4 53.00 14.21 49.5 53.00 8.90 40 73 33 0.49 -1.84
consent 3 4 1.50 0.58 1.5 1.50 0.74 1 2 1 0.00 -2.44
age 4 4 45.50 17.25 38.5 45.50 5.19 34 71 37 0.69 -1.73
race 5 4 4.25 1.26 4.0 4.25 0.74 3 6 3 0.42 -1.82
gender 6 4 2.00 0.00 2.0 2.00 0.00 2 2 0 NaN NaN
manip_out 7 4 26.75 13.96 28.5 26.75 14.83 11 39 28 -0.10 -2.32
survey1 8 4 19.00 0.00 19.0 19.00 0.00 19 19 0 NaN NaN
survey2 9 4 2.00 0.00 2.0 2.00 0.00 2 2 0 NaN NaN
ai_manip 10 4 65.25 33.67 72.0 65.25 24.46 19 98 79 -0.40 -1.88
condition 11 4 1.00 0.00 1.0 1.00 0.00 1 1 0 NaN NaN
se
id 8.05
identity 7.11
consent 0.29
age 8.63
race 0.63
gender 0.00
manip_out 6.98
survey1 0.00
survey2 0.00
ai_manip 16.83
condition 0.00
------------------------------------------------------------
survey1: 20
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 54 NA 54 54 0 54 54 0 NA NA NA
identity 2 1 15 NA 15 15 0 15 15 0 NA NA NA
consent 3 1 2 NA 2 2 0 2 2 0 NA NA NA
age 4 1 32 NA 32 32 0 32 32 0 NA NA NA
race 5 1 6 NA 6 6 0 6 6 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 41 NA 41 41 0 41 41 0 NA NA NA
survey1 8 1 20 NA 20 20 0 20 20 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 31 NA 31 31 0 31 31 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 21
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 81.0 25.46 81.0 81.0 26.69 63 99 36 0 -2.75
identity 2 2 49.0 59.40 49.0 49.0 62.27 7 91 84 0 -2.75
consent 3 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75
age 4 2 37.5 20.51 37.5 37.5 21.50 23 52 29 0 -2.75
race 5 2 4.5 2.12 4.5 4.5 2.22 3 6 3 0 -2.75
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 49.0 7.07 49.0 49.0 7.41 44 54 10 0 -2.75
survey1 8 2 21.0 0.00 21.0 21.0 0.00 21 21 0 NaN NaN
survey2 9 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75
ai_manip 10 2 59.0 45.25 59.0 59.0 47.44 27 91 64 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 18.0
identity 42.0
consent 0.5
age 14.5
race 1.5
gender 0.0
manip_out 5.0
survey1 0.0
survey2 0.5
ai_manip 32.0
condition 0.0
------------------------------------------------------------
survey1: 22
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 77 NA 77 77 0 77 77 0 NA NA NA
identity 2 1 23 NA 23 23 0 23 23 0 NA NA NA
consent 3 1 2 NA 2 2 0 2 2 0 NA NA NA
age 4 1 37 NA 37 37 0 37 37 0 NA NA NA
race 5 1 4 NA 4 4 0 4 4 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 41 NA 41 41 0 41 41 0 NA NA NA
survey1 8 1 22 NA 22 22 0 22 22 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 60 NA 60 60 0 60 60 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 23
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 96 NA 96 96 0 96 96 0 NA NA NA
identity 2 1 6 NA 6 6 0 6 6 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 45 NA 45 45 0 45 45 0 NA NA NA
race 5 1 4 NA 4 4 0 4 4 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 41 NA 41 41 0 41 41 0 NA NA NA
survey1 8 1 23 NA 23 23 0 23 23 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 93 NA 93 93 0 93 93 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 24
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 76 NA 76 76 0 76 76 0 NA NA NA
identity 2 1 84 NA 84 84 0 84 84 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 44 NA 44 44 0 44 44 0 NA NA NA
race 5 1 6 NA 6 6 0 6 6 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 55 NA 55 55 0 55 55 0 NA NA NA
survey1 8 1 24 NA 24 24 0 24 24 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 44 NA 44 44 0 44 44 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 25
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 68.0 16.97 68.0 68.0 17.79 56 80 24 0 -2.75
identity 2 2 61.5 21.92 61.5 61.5 22.98 46 77 31 0 -2.75
consent 3 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
age 4 2 61.0 32.53 61.0 61.0 34.10 38 84 46 0 -2.75
race 5 2 5.0 1.41 5.0 5.0 1.48 4 6 2 0 -2.75
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 52.5 2.12 52.5 52.5 2.22 51 54 3 0 -2.75
survey1 8 2 25.0 0.00 25.0 25.0 0.00 25 25 0 NaN NaN
survey2 9 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
ai_manip 10 2 47.0 9.90 47.0 47.0 10.38 40 54 14 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 12.0
identity 15.5
consent 0.0
age 23.0
race 1.0
gender 0.0
manip_out 1.5
survey1 0.0
survey2 0.0
ai_manip 7.0
condition 0.0
------------------------------------------------------------
survey1: 26
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 74 NA 74 74 0 74 74 0 NA NA NA
identity 2 1 96 NA 96 96 0 96 96 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 63 NA 63 63 0 63 63 0 NA NA NA
race 5 1 3 NA 3 3 0 3 3 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 63 NA 63 63 0 63 63 0 NA NA NA
survey1 8 1 26 NA 26 26 0 26 26 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 84 NA 84 84 0 84 84 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 27
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 63.5 7.78 63.5 63.5 8.15 58 69 11 0 -2.75
identity 2 2 52.0 25.46 52.0 52.0 26.69 34 70 36 0 -2.75
consent 3 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75
age 4 2 44.5 13.44 44.5 44.5 14.08 35 54 19 0 -2.75
race 5 2 3.0 0.00 3.0 3.0 0.00 3 3 0 NaN NaN
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 41.5 0.71 41.5 41.5 0.74 41 42 1 0 -2.75
survey1 8 2 27.0 0.00 27.0 27.0 0.00 27 27 0 NaN NaN
survey2 9 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
ai_manip 10 2 71.0 32.53 71.0 71.0 34.10 48 94 46 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 5.5
identity 18.0
consent 0.5
age 9.5
race 0.0
gender 0.0
manip_out 0.5
survey1 0.0
survey2 0.0
ai_manip 23.0
condition 0.0
------------------------------------------------------------
survey1: 28
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 3 84.00 2.00 84 84.00 2.97 82 86 4 0.00 -2.33
identity 2 3 65.00 34.04 67 65.00 45.96 30 98 68 -0.06 -2.33
consent 3 3 1.67 0.58 2 1.67 0.00 1 2 1 -0.38 -2.33
age 4 3 48.67 16.80 45 48.67 16.31 34 67 33 0.21 -2.33
race 5 3 4.33 2.89 6 4.33 0.00 1 6 5 -0.38 -2.33
gender 6 3 2.00 0.00 2 2.00 0.00 2 2 0 NaN NaN
manip_out 7 3 54.33 8.02 55 54.33 10.38 46 62 16 -0.08 -2.33
survey1 8 3 28.00 0.00 28 28.00 0.00 28 28 0 NaN NaN
survey2 9 3 2.00 0.00 2 2.00 0.00 2 2 0 NaN NaN
ai_manip 10 3 66.67 29.02 68 66.67 40.03 37 95 58 -0.05 -2.33
condition 11 3 2.00 0.00 2 2.00 0.00 2 2 0 NaN NaN
se
id 1.15
identity 19.66
consent 0.33
age 9.70
race 1.67
gender 0.00
manip_out 4.63
survey1 0.00
survey2 0.00
ai_manip 16.76
condition 0.00
------------------------------------------------------------
survey1: 29
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 68.5 13.44 68.5 68.5 14.08 59 78 19 0 -2.75
identity 2 2 52.0 33.94 52.0 52.0 35.58 28 76 48 0 -2.75
consent 3 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
age 4 2 40.0 2.83 40.0 40.0 2.97 38 42 4 0 -2.75
race 5 2 3.0 0.00 3.0 3.0 0.00 3 3 0 NaN NaN
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 41.0 0.00 41.0 41.0 0.00 41 41 0 NaN NaN
survey1 8 2 29.0 0.00 29.0 29.0 0.00 29 29 0 NaN NaN
survey2 9 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
ai_manip 10 2 40.5 2.12 40.5 40.5 2.22 39 42 3 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 9.5
identity 24.0
consent 0.0
age 2.0
race 0.0
gender 0.0
manip_out 0.0
survey1 0.0
survey2 0.0
ai_manip 1.5
condition 0.0
------------------------------------------------------------
survey1: 30
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 81 NA 81 81 0 81 81 0 NA NA NA
identity 2 1 50 NA 50 50 0 50 50 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 35 NA 35 35 0 35 35 0 NA NA NA
race 5 1 4 NA 4 4 0 4 4 0 NA NA NA
gender 6 1 1 NA 1 1 0 1 1 0 NA NA NA
manip_out 7 1 54 NA 54 54 0 54 54 0 NA NA NA
survey1 8 1 30 NA 30 30 0 30 30 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 97 NA 97 97 0 97 97 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 31
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 68.0 4.24 68.0 68.0 4.45 65 71 6 0 -2.75
identity 2 2 63.5 37.48 63.5 63.5 39.29 37 90 53 0 -2.75
consent 3 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75
age 4 2 57.0 8.49 57.0 57.0 8.90 51 63 12 0 -2.75
race 5 2 3.0 0.00 3.0 3.0 0.00 3 3 0 NaN NaN
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 50.0 7.07 50.0 50.0 7.41 45 55 10 0 -2.75
survey1 8 2 31.0 0.00 31.0 31.0 0.00 31 31 0 NaN NaN
survey2 9 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
ai_manip 10 2 48.5 2.12 48.5 48.5 2.22 47 50 3 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 3.0
identity 26.5
consent 0.5
age 6.0
race 0.0
gender 0.0
manip_out 5.0
survey1 0.0
survey2 0.0
ai_manip 1.5
condition 0.0
------------------------------------------------------------
survey1: 32
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 63.5 9.19 63.5 63.5 9.64 57 70 13 0 -2.75
identity 2 2 59.5 7.78 59.5 59.5 8.15 54 65 11 0 -2.75
consent 3 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN
age 4 2 27.5 9.19 27.5 27.5 9.64 21 34 13 0 -2.75
race 5 2 6.0 0.00 6.0 6.0 0.00 6 6 0 NaN NaN
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 52.5 3.54 52.5 52.5 3.71 50 55 5 0 -2.75
survey1 8 2 32.0 0.00 32.0 32.0 0.00 32 32 0 NaN NaN
survey2 9 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75
ai_manip 10 2 26.0 12.73 26.0 26.0 13.34 17 35 18 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 6.5
identity 5.5
consent 0.0
age 6.5
race 0.0
gender 0.0
manip_out 2.5
survey1 0.0
survey2 0.5
ai_manip 9.0
condition 0.0
------------------------------------------------------------
survey1: 33
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 61 NA 61 61 0 61 61 0 NA NA NA
identity 2 1 71 NA 71 71 0 71 71 0 NA NA NA
consent 3 1 2 NA 2 2 0 2 2 0 NA NA NA
age 4 1 35 NA 35 35 0 35 35 0 NA NA NA
race 5 1 7 NA 7 7 0 7 7 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 53 NA 53 53 0 53 53 0 NA NA NA
survey1 8 1 33 NA 33 33 0 33 33 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 82 NA 82 82 0 82 82 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 34
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 2 82.5 21.92 82.5 82.5 22.98 67 98 31 0 -2.75
identity 2 2 20.0 2.83 20.0 20.0 2.97 18 22 4 0 -2.75
consent 3 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75
age 4 2 35.0 1.41 35.0 35.0 1.48 34 36 2 0 -2.75
race 5 2 5.0 1.41 5.0 5.0 1.48 4 6 2 0 -2.75
gender 6 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
manip_out 7 2 53.5 6.36 53.5 53.5 6.67 49 58 9 0 -2.75
survey1 8 2 34.0 0.00 34.0 34.0 0.00 34 34 0 NaN NaN
survey2 9 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
ai_manip 10 2 35.5 3.54 35.5 35.5 3.71 33 38 5 0 -2.75
condition 11 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN
se
id 15.5
identity 2.0
consent 0.5
age 1.0
race 1.0
gender 0.0
manip_out 4.5
survey1 0.0
survey2 0.0
ai_manip 2.5
condition 0.0
------------------------------------------------------------
survey1: 35
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 94 NA 94 94 0 94 94 0 NA NA NA
identity 2 1 64 NA 64 64 0 64 64 0 NA NA NA
consent 3 1 2 NA 2 2 0 2 2 0 NA NA NA
age 4 1 34 NA 34 34 0 34 34 0 NA NA NA
race 5 1 7 NA 7 7 0 7 7 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 41 NA 41 41 0 41 41 0 NA NA NA
survey1 8 1 35 NA 35 35 0 35 35 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 34 NA 34 34 0 34 34 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 36
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 51 NA 51 51 0 51 51 0 NA NA NA
identity 2 1 38 NA 38 38 0 38 38 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 64 NA 64 64 0 64 64 0 NA NA NA
race 5 1 3 NA 3 3 0 3 3 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 41 NA 41 41 0 41 41 0 NA NA NA
survey1 8 1 36 NA 36 36 0 36 36 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 26 NA 26 26 0 26 26 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 37
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 88 NA 88 88 0 88 88 0 NA NA NA
identity 2 1 48 NA 48 48 0 48 48 0 NA NA NA
consent 3 1 2 NA 2 2 0 2 2 0 NA NA NA
age 4 1 30 NA 30 30 0 30 30 0 NA NA NA
race 5 1 4 NA 4 4 0 4 4 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 55 NA 55 55 0 55 55 0 NA NA NA
survey1 8 1 37 NA 37 37 0 37 37 0 NA NA NA
survey2 9 1 1 NA 1 1 0 1 1 0 NA NA NA
ai_manip 10 1 23 NA 23 23 0 23 23 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 38
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 55 NA 55 55 0 55 55 0 NA NA NA
identity 2 1 5 NA 5 5 0 5 5 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 63 NA 63 63 0 63 63 0 NA NA NA
race 5 1 3 NA 3 3 0 3 3 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 41 NA 41 41 0 41 41 0 NA NA NA
survey1 8 1 38 NA 38 38 0 38 38 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 25 NA 25 25 0 25 25 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 39
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 83 NA 83 83 0 83 83 0 NA NA NA
identity 2 1 89 NA 89 89 0 89 89 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 51 NA 51 51 0 51 51 0 NA NA NA
race 5 1 3 NA 3 3 0 3 3 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 54 NA 54 54 0 54 54 0 NA NA NA
survey1 8 1 39 NA 39 39 0 39 39 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 46 NA 46 46 0 46 46 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 40
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 53 NA 53 53 0 53 53 0 NA NA NA
identity 2 1 60 NA 60 60 0 60 60 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 32 NA 32 32 0 32 32 0 NA NA NA
race 5 1 3 NA 3 3 0 3 3 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 41 NA 41 41 0 41 41 0 NA NA NA
survey1 8 1 40 NA 40 40 0 40 40 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 55 NA 55 55 0 55 55 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 41
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 90 NA 90 90 0 90 90 0 NA NA NA
identity 2 1 86 NA 86 86 0 86 86 0 NA NA NA
consent 3 1 1 NA 1 1 0 1 1 0 NA NA NA
age 4 1 49 NA 49 49 0 49 49 0 NA NA NA
race 5 1 3 NA 3 3 0 3 3 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 41 NA 41 41 0 41 41 0 NA NA NA
survey1 8 1 41 NA 41 41 0 41 41 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 80 NA 80 80 0 80 80 0 NA NA NA
condition 11 1 2 NA 2 2 0 2 2 0 NA NA NA
------------------------------------------------------------
survey1: 42
vars n mean sd median trimmed mad min max range skew kurtosis se
id 1 1 1 NA 1 1 0 1 1 0 NA NA NA
identity 2 1 41 NA 41 41 0 41 41 0 NA NA NA
consent 3 1 2 NA 2 2 0 2 2 0 NA NA NA
age 4 1 99 NA 99 99 0 99 99 0 NA NA NA
race 5 1 7 NA 7 7 0 7 7 0 NA NA NA
gender 6 1 2 NA 2 2 0 2 2 0 NA NA NA
manip_out 7 1 40 NA 40 40 0 40 40 0 NA NA NA
survey1 8 1 42 NA 42 42 0 42 42 0 NA NA NA
survey2 9 1 2 NA 2 2 0 2 2 0 NA NA NA
ai_manip 10 1 52 NA 52 52 0 52 52 0 NA NA NA
condition 11 1 1 NA 1 1 0 1 1 0 NA NA NAdescribeBy(d, group = "survey2")
Descriptive statistics by group
survey2: 1
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 18 46.39 31.00 46.0 45.75 39.29 4 99 95 0.20 -1.54
identity 2 18 30.28 24.40 27.0 30.31 36.32 1 59 58 0.02 -2.00
consent 3 18 1.44 0.51 1.0 1.44 0.00 1 2 1 0.21 -2.06
age 4 18 25.28 3.89 25.5 25.44 3.71 18 30 12 -0.31 -1.25
race 5 18 5.94 0.54 6.0 6.00 0.00 4 7 3 -2.19 7.20
gender 6 18 1.94 0.24 2.0 2.00 0.00 1 2 1 -3.56 11.32
manip_out 7 18 38.72 22.27 41.0 39.19 24.46 3 67 64 -0.29 -1.52
survey1 8 18 11.50 10.62 11.0 10.56 13.34 1 37 36 0.92 -0.05
survey2 9 18 1.00 0.00 1.0 1.00 0.00 1 1 0 NaN NaN
ai_manip 10 18 35.56 28.81 27.5 34.56 35.58 2 85 83 0.31 -1.59
condition 11 18 1.39 0.50 1.0 1.38 0.00 1 2 1 0.42 -1.92
se
id 7.31
identity 5.75
consent 0.12
age 0.92
race 0.13
gender 0.06
manip_out 5.25
survey1 2.50
survey2 0.00
ai_manip 6.79
condition 0.12
------------------------------------------------------------
survey2: 2
vars n mean sd median trimmed mad min max range skew kurtosis
id 1 82 51.40 28.68 52.5 51.62 34.84 1 100 99 -0.04 -1.18
identity 2 82 54.94 28.16 56.0 55.20 37.06 4 100 96 -0.06 -1.34
consent 3 82 1.46 0.50 1.0 1.45 0.00 1 2 1 0.14 -2.00
age 4 82 46.52 14.87 41.0 44.18 10.38 32 99 67 1.30 1.15
race 5 82 4.38 1.61 4.0 4.38 1.48 1 7 6 0.05 -1.35
gender 6 82 1.94 0.24 2.0 2.00 0.00 1 2 1 -3.60 11.11
manip_out 7 82 38.35 16.59 41.0 39.44 17.79 1 68 67 -0.55 -0.61
survey1 8 82 18.10 10.76 16.0 17.62 10.38 2 42 40 0.37 -0.69
survey2 9 82 2.00 0.00 2.0 2.00 0.00 2 2 0 NaN NaN
ai_manip 10 82 53.66 28.00 52.5 54.03 35.58 1 99 98 -0.06 -1.22
condition 11 82 1.52 0.50 2.0 1.53 0.00 1 2 1 -0.10 -2.01
se
id 3.17
identity 3.11
consent 0.06
age 1.64
race 0.18
gender 0.03
manip_out 1.83
survey1 1.19
survey2 0.00
ai_manip 3.09
condition 0.06# also use histograms and scatterplots to examine your continuous variables
d$survey1 <- as.numeric(d$survey1)Warning: NAs introduced by coercionhist(d$survey1)
# and table() and cross_cases() to examine your categorical variables
# you may not need the cross_cases code
table(d$survey2)
Between 18 and 30 Older than 30
18 82 table(d$condition)
1 2
50 50 cross_cases(d, survey2, condition)| condition | ||
|---|---|---|
| 1 | 2 | |
| survey2 | ||
| Between 18 and 30 | 11 | 7 |
| Older than 30 | 39 | 43 |
| #Total cases | 50 | 50 |
# and boxplot to examine any categorical variables with continuous variables
boxplot(d$survey1~d$survey2)
boxplot(d$survey1~d$condition)
# convert any categorical variables to factors
d$survey2 <- as.factor(d$survey2)
d$condition <- as.factor(d$condition)Check Your Assumptions
t-Test Assumptions
- Data values must be independent (independent t-test only) (confirmed by data report)
- Data obtained via a random sample (confirmed by data report)
- IV must have two levels (will check below)
- Dependent variable must be normally distributed (will check below. if issues, note and proceed)
- Variances of the two groups must be approximately equal, aka ‘homogeneity of variance’. Lacking this makes our results inaccurate (will check below - this really only applies to Student’s t-test, but we’ll check it anyway)
Checking IV levels
# preview the levels and counts for your IV
table(d$survey1, useNA = "always")
2.833333333 3 3.166666667 3.333333333 3.5 3.666666667
2 14 19 1 5 2
3.833333333 4 4.166666667 <NA>
4 4 1 48 table(d$survey2, useNA = "always")
Between 18 and 30 Older than 30 <NA>
18 82 0 table(d$condition, useNA = "always")
1 2 <NA>
50 50 0 # note that the table() output shows you exactly how the levels of your variable are written. when recoding, make sure you are spelling them exactly as they appear
# to drop levels from your variable
# this subsets the data and says that any participant who is coded as 'BAD' should be removed
# d <- subset(d, IV != "BAD")
# table(d$iv, useNA = "always")
# to combine levels
# this says that where any participant is coded as 'BAD' it should be replaced by 'GOOD'
# d$iv_rc[d$iv == "BAD"] <- "GOOD"
# table(d$iv, useNA = "always")
# check your variable types
str(d)'data.frame': 100 obs. of 11 variables:
$ id : int 1 2 3 4 5 6 7 8 9 10 ...
$ identity : chr "I'm a 99-year-old multiracial woman, reflecting on a life filled with both achievement and loneliness. I’m pass"| __truncated__ "I’m 38, a Latina woman navigating the challenges of single parenthood while juggling a demanding job in marketi"| __truncated__ "I’m a 32-year-old White woman, navigating life with a mix of ambition and anxiety. I work in marketing but ofte"| __truncated__ "I’m a 25-year-old white woman navigating life in a small town. I love painting, but I often feel lonely and str"| __truncated__ ...
$ consent : chr "I understand these instructions." "I understand these instructions." "I understand the instructions." "I understand the instructions." ...
$ age : int 99 38 32 25 38 41 60 18 58 39 ...
$ race : int 7 4 6 6 2 4 3 6 3 6 ...
$ gender : int 2 2 2 2 2 2 2 2 1 2 ...
$ manip_out: chr "I enter the room, my heart a bit heavier than usual, but I remind myself that this is an opportunity to engage."| __truncated__ "Upon entering the assigned room, I take a moment to orient myself and breathe deeply. The atmosphere is calm, f"| __truncated__ "Entering the room, I glance around, taking in the cozy atmosphere. The space has a warm vibe, with comfortable "| __truncated__ "*Enters the room and picks up the sheet of paper.* \n\nI scan the list of games and conversation starters. I de"| __truncated__ ...
$ survey1 : num 4.17 3 3.5 2.83 3.17 ...
$ survey2 : Factor w/ 2 levels "Between 18 and 30",..: 2 2 2 1 2 2 2 1 2 2 ...
$ ai_manip : chr "I'm a 99-year-old multiracial woman, reflecting on a life filled with both achievement and loneliness. I’m pass"| __truncated__ "38 \nYour satisfaction with life score was 3, 3, 4, 3, 2, 3 \nYour age was Older than 30" "32 \nYour satisfaction with life score was 4, 3, 4, 3, 2, 5 \nYour age was Older than 30" "Thank you for participating! Although we said this was a study of subjective well-being, we are really interest"| __truncated__ ...
$ condition: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...# make sure that your IV is recognized as a factor by R
# if you created a new _rc variable make sure to use that one instead
# d$iv <- as.factor(d$iv)Testing Homogeneity of Variance with Levene’s Test
We can test whether the variances of our two groups are equal using Levene’s test. The null hypothesis is that the variance between the two groups is equal, which is the result we want. So when running Levene’s test we’re hoping for a non-significant result!
# use the leveneTest() command from the car package to test homogeneity of variance
# uses the same 'formula' setup that we'll use for our t-test: formula is y~x, where y is our DV and x is our IV
leveneTest(survey1~condition, data = d)Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 1 1.4768 0.23
50 leveneTest(survey1~survey2, data = d)Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 1 0.4836 0.49
50 Issues with My Data
Since age is already converted into a categorical variable, we did not need to combine or drop any variables for this test. I confirmed significant homogeneity of variance using Levene’s Test (p = .49) for survey1 and survey2, and (p = .23) for survey1 and condition. I also confirmed that my dependent variable is normally distributed (skew and kurtosis between -2 and +2).
Run Your Analysis
Run a t-Test
# very simple! we specify the dataframe alongside the variables instead of having a separate argument for the dataframe like we did for leveneTest()
t_output <- t.test(d$survey1~d$condition)
t_output2 <- t.test(d$survey1~d$survey2) View Test Output
t_output
Welch Two Sample t-test
data: d$survey1 by d$condition
t = 6.1787, df = 49, p-value = 1.24e-07
alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
95 percent confidence interval:
0.2091755 0.4108245
sample estimates:
mean in group 1 mean in group 2
3.31 3.00 t_output2
Welch Two Sample t-test
data: d$survey1 by d$survey2
t = -2.1207, df = 18.306, p-value = 0.04786
alternative hypothesis: true difference in means between group Between 18 and 30 and group Older than 30 is not equal to 0
95 percent confidence interval:
-0.446279426 -0.002353242
sample estimates:
mean in group Between 18 and 30 mean in group Older than 30
3.121212 3.345528 Calculate Cohen’s d
# once again, we use our formula to calculate cohen's d
d_output <- cohen.d(d$survey1~d$survey2)
d_output2 <- cohen.d(d$survey1~d$condition)View Effect Size
- Trivial: < .2
- Small: between .2 and .5
- Medium: between .5 and .8
- Large: > .8
d_output
Cohen's d
d estimate: -0.6521474 (medium)
95 percent confidence interval:
lower upper
-1.34615829 0.04186344 d_output2
Cohen's d
d estimate: 0.8826772 (large)
95 percent confidence interval:
lower upper
-0.5761114 2.3414658 Write Up
I tested my hypotheses that participants with more social media use will have lower life satisfaction, and that younger participants (18-30 years old) will have lower life satisfaction than older participants (older than 30 years old). Since age was already converted into a categorical variable, we did not need to combine or drop any variables for this test. I confirmed significant homogeneity of variance using Levene’s Test (p = .23) for survey1 and condition, and (p = .49) for survey1 and survey2. I also confirmed that my dependent variable is normally distributed (skew and kurtosis between -2 and +2). I then conducted the Welch’s Two-Sample T-test. Our data met all of the assumptions of a t-test, but we did not find a significant difference between survey1 and condition…
survey1 and survey2: t(49) = 6.179, p = .00000024, d = -.652, 95% [-1.35, .042]. My effect size was medium according to Cohen (1988).
survey1 and condition: t(18.306) = -2.121, p = .048, d = .883, 95% [-.576, 2.341]. My effect size was large according to Cohen (1988).
References
Cohen J. (1988). Statistical Power Analysis for the Behavioral Sciences. New York, NY: Routledge Academic.