# Loading required package, setting working directory, and reading the data file.
require(psych)
## Loading required package: psych
require(BayesFactor)
## Loading required package: BayesFactor
## Loading required package: coda
## Loading required package: Matrix
## ************
## Welcome to BayesFactor 0.9.12-2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
##
## Type BFManual() to open the manual.
## ************
setwd("/Users/ivanropovik/OneDrive/MANUSCRIPTS/2016 Many Labs 5/Pilot 1/Analyses/")
pilot1 <- read.csv(file = "Pilot_1_Kozminski.csv", header = TRUE, sep = ";")
# Computing means of the two scale items for each scenario
# If one of the two target items is missing,
# the score of the other item is used as the estimate for the given scenario
pilot1$scenario1_Q3 <- rowMeans(pilot1[,c("Q9", "Q12")], na.rm = TRUE)
pilot1$scenario2_Q15 <- rowMeans(pilot1[,c("Q20", "Q23")], na.rm = TRUE)
pilot1$scenario3_Q26 <- rowMeans(pilot1[,c("Q31", "Q34")], na.rm = TRUE)
#pilot1$scenario4_Q239 <- rowMeans(pilot1[,c("Q244", "Q247")], na.rm = TRUE)
pilot1$scenario5_Q37 <- rowMeans(pilot1[,c("Q42", "Q45")], na.rm = TRUE)
#pilot1$scenario6_Q250 <- rowMeans(pilot1[,c("Q255", "Q258")], na.rm = TRUE)
pilot1$scenario7_Q48 <- rowMeans(pilot1[,c("Q53", "Q56")], na.rm = TRUE)
pilot1$scenario8_Q59 <- rowMeans(pilot1[,c("Q64", "Q67")], na.rm = TRUE)
#pilot1$scenario9_Q261 <- rowMeans(pilot1[,c("Q266", "Q269")], na.rm = TRUE)
pilot1$scenario10_Q81 <- rowMeans(pilot1[,c("Q86", "Q89")], na.rm = TRUE)
pilot1$scenario11_Q92 <- rowMeans(pilot1[,c("Q97", "Q100")], na.rm = TRUE)
pilot1$scenario12_Q103 <- rowMeans(pilot1[,c("Q108", "Q111")], na.rm = TRUE)
#pilot1$scenario13_Q272 <- rowMeans(pilot1[,c("Q277", "Q280")], na.rm = TRUE)
#pilot1$scenario14_Q283 <- rowMeans(pilot1[,c("Q288", "Q291")], na.rm = TRUE)
pilot1$scenario15_Q114 <- rowMeans(pilot1[,c("Q119", "Q122")], na.rm = TRUE)
#pilot1$scenario16_Q228 <- rowMeans(pilot1[,c("Q233", "Q236")], na.rm = TRUE)
pilot1$scenario17_Q125 <- rowMeans(pilot1[,c("Q130", "Q133")], na.rm = TRUE)
pilot1$scenario18_Q136 <- rowMeans(pilot1[,c("Q141", "Q144")], na.rm = TRUE)
pilot1$scenario19_Q147 <- rowMeans(pilot1[,c("Q152", "Q155")], na.rm = TRUE)
pilot1$scenario20_Q158 <- rowMeans(pilot1[,c("Q163", "Q166")], na.rm = TRUE)
pilot1$scenario21_Q169 <- rowMeans(pilot1[,c("Q174", "Q177")], na.rm = TRUE)
# Sample size
nrow(pilot1)
## [1] 52
# Frequencies for demographic variables
# Q180 = Gender
# Q181 = Year of study
# Q182 = Age
lapply(pilot1[,c("Q180", "Q181", "Q182")],
function(x){table(x, useNA = "ifany")})
## $Q180
## x
## 1 2
## 31 21
##
## $Q181
## x
## 1 2 3 4 5
## 1 37 6 5 3
##
## $Q182
## x
## 18 19 20 21 22 23 24 29 <NA>
## 1 1 21 18 5 3 1 1 1
# Descriptive statistics for each scenario
lapply(pilot1[,c("scenario1_Q3", "scenario2_Q15", "scenario3_Q26", "scenario5_Q37",
"scenario7_Q48", "scenario8_Q59", "scenario10_Q81", "scenario11_Q92",
"scenario12_Q103", "scenario15_Q114", "scenario17_Q125", "scenario18_Q136",
"scenario19_Q147", "scenario20_Q158", "scenario21_Q169")],
function(x){describe(x, na.rm = TRUE, fast = TRUE)})
## $scenario1_Q3
## vars n mean sd min max range se
## X1 1 51 5 1.37 1.5 9 7.5 0.19
##
## $scenario2_Q15
## vars n mean sd min max range se
## X1 1 52 5.97 1.62 3 9 6 0.22
##
## $scenario3_Q26
## vars n mean sd min max range se
## X1 1 51 4.86 1.85 1 9 8 0.26
##
## $scenario5_Q37
## vars n mean sd min max range se
## X1 1 50 5.89 1.83 2 9 7 0.26
##
## $scenario7_Q48
## vars n mean sd min max range se
## X1 1 50 5.29 1.2 3 9 6 0.17
##
## $scenario8_Q59
## vars n mean sd min max range se
## X1 1 50 6.66 2.11 1 9 8 0.3
##
## $scenario10_Q81
## vars n mean sd min max range se
## X1 1 51 5.77 1.66 3 9 6 0.23
##
## $scenario11_Q92
## vars n mean sd min max range se
## X1 1 50 5.11 2.01 1 9 8 0.28
##
## $scenario12_Q103
## vars n mean sd min max range se
## X1 1 52 6.04 1.92 1 9 8 0.27
##
## $scenario15_Q114
## vars n mean sd min max range se
## X1 1 49 4.7 1.62 1 9 8 0.23
##
## $scenario17_Q125
## vars n mean sd min max range se
## X1 1 50 5.79 1.9 2.5 9 6.5 0.27
##
## $scenario18_Q136
## vars n mean sd min max range se
## X1 1 49 4.81 1.84 1 9 8 0.26
##
## $scenario19_Q147
## vars n mean sd min max range se
## X1 1 51 4.54 1.91 1 9 8 0.27
##
## $scenario20_Q158
## vars n mean sd min max range se
## X1 1 52 4.82 1.61 1 9 8 0.22
##
## $scenario21_Q169
## vars n mean sd min max range se
## X1 1 50 5.99 1.83 2.5 9 6.5 0.26
# One-sample t-test for each scenario with population parameter mu set at 5.
lapply(pilot1[,c("scenario1_Q3", "scenario2_Q15", "scenario3_Q26", "scenario5_Q37",
"scenario7_Q48", "scenario8_Q59", "scenario10_Q81", "scenario11_Q92",
"scenario12_Q103", "scenario15_Q114", "scenario17_Q125", "scenario18_Q136",
"scenario19_Q147", "scenario20_Q158", "scenario21_Q169")],
function(x){t.test(x, data = pilot1, mu = 5, na.rm = TRUE)})
## $scenario1_Q3
##
## One Sample t-test
##
## data: x
## t = 0, df = 50, p-value = 1
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.614363 5.385637
## sample estimates:
## mean of x
## 5
##
##
## $scenario2_Q15
##
## One Sample t-test
##
## data: x
## t = 4.3332, df = 51, p-value = 6.91e-05
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.521219 6.421089
## sample estimates:
## mean of x
## 5.971154
##
##
## $scenario3_Q26
##
## One Sample t-test
##
## data: x
## t = -0.5292, df = 50, p-value = 0.599
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.341795 5.383695
## sample estimates:
## mean of x
## 4.862745
##
##
## $scenario5_Q37
##
## One Sample t-test
##
## data: x
## t = 3.4437, df = 49, p-value = 0.001185
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.370635 6.409365
## sample estimates:
## mean of x
## 5.89
##
##
## $scenario7_Q48
##
## One Sample t-test
##
## data: x
## t = 1.703, df = 49, p-value = 0.0949
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.947794 5.632206
## sample estimates:
## mean of x
## 5.29
##
##
## $scenario8_Q59
##
## One Sample t-test
##
## data: x
## t = 5.5558, df = 49, p-value = 1.123e-06
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 6.059566 7.260434
## sample estimates:
## mean of x
## 6.66
##
##
## $scenario10_Q81
##
## One Sample t-test
##
## data: x
## t = 3.3274, df = 50, p-value = 0.00165
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.306989 6.242030
## sample estimates:
## mean of x
## 5.77451
##
##
## $scenario11_Q92
##
## One Sample t-test
##
## data: x
## t = 0.3868, df = 49, p-value = 0.7006
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.538505 5.681495
## sample estimates:
## mean of x
## 5.11
##
##
## $scenario12_Q103
##
## One Sample t-test
##
## data: x
## t = 3.9011, df = 51, p-value = 0.0002813
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.504049 6.572874
## sample estimates:
## mean of x
## 6.038462
##
##
## $scenario15_Q114
##
## One Sample t-test
##
## data: x
## t = -1.2787, df = 48, p-value = 0.2071
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.238785 5.169378
## sample estimates:
## mean of x
## 4.704082
##
##
## $scenario17_Q125
##
## One Sample t-test
##
## data: x
## t = 2.9346, df = 49, p-value = 0.00507
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.249027 6.330973
## sample estimates:
## mean of x
## 5.79
##
##
## $scenario18_Q136
##
## One Sample t-test
##
## data: x
## t = -0.73667, df = 48, p-value = 0.4649
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.276964 5.335281
## sample estimates:
## mean of x
## 4.806122
##
##
## $scenario19_Q147
##
## One Sample t-test
##
## data: x
## t = -1.7204, df = 50, p-value = 0.09154
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.001259 5.077172
## sample estimates:
## mean of x
## 4.539216
##
##
## $scenario20_Q158
##
## One Sample t-test
##
## data: x
## t = -0.81729, df = 51, p-value = 0.4176
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.368543 5.266072
## sample estimates:
## mean of x
## 4.817308
##
##
## $scenario21_Q169
##
## One Sample t-test
##
## data: x
## t = 3.8353, df = 49, p-value = 0.0003588
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.47127 6.50873
## sample estimates:
## mean of x
## 5.99
# Bayes factor in favour of the null (BF01) for each scenario
# with population parameter mu set at 5 and prior width of 0.5.
lapply(pilot1[,c("scenario1_Q3", "scenario2_Q15", "scenario3_Q26", "scenario5_Q37",
"scenario7_Q48", "scenario8_Q59", "scenario10_Q81", "scenario11_Q92",
"scenario12_Q103", "scenario15_Q114", "scenario17_Q125", "scenario18_Q136",
"scenario19_Q147", "scenario20_Q158", "scenario21_Q169")],
function(x){1/ttestBF(x[!is.na(x)], mu = 5, rscale = 0.5, na.rm = TRUE)})
## $scenario1_Q3
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 4.785326 ±0.04%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario2_Q15
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.003060549 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario3_Q26
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 4.219296 ±0.01%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario5_Q37
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.03712297 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario7_Q48
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 1.328027 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario8_Q59
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 7.27325e-05 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario10_Q81
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.04956036 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario11_Q92
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 4.435315 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario12_Q103
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.01064857 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario15_Q114
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 2.279411 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario17_Q125
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.1282007 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario18_Q136
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 3.688975 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario19_Q147
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 1.302357 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario20_Q158
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 3.576393 ±0.01%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario21_Q169
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.01311812 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS