# 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+ManyLabs5+Bamberg.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] 51
# 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 <NA>
## 31 4 16
##
## $Q181
## x
## 1 1. 1. Bachelor
## 15 15 2 1
## 1. Fachsemester 3 3. Fachsemester 4.
## 1 13 1 1
## 5 7
## 1 1
##
## $Q182
## x
## 18 19 20 21 22 24 29 31 35 42 46 <NA>
## 6 6 10 3 3 2 2 1 1 1 1 15
# 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", "scenario16_Q228", "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 38 5.54 1.81 1 9 8 0.29
##
## $scenario2_Q15
## vars n mean sd min max range se
## X1 1 38 5.87 1.76 2.5 9 6.5 0.29
##
## $scenario3_Q26
## vars n mean sd min max range se
## X1 1 39 4.01 1.5 1 7 6 0.24
##
## $scenario5_Q37
## vars n mean sd min max range se
## X1 1 38 5.67 1.68 1 9 8 0.27
##
## $scenario7_Q48
## vars n mean sd min max range se
## X1 1 39 5.4 1.26 2 8 6 0.2
##
## $scenario8_Q59
## vars n mean sd min max range se
## X1 1 39 7.53 1.9 1 9 8 0.3
##
## $scenario10_Q81
## vars n mean sd min max range se
## X1 1 38 5.61 1.95 1 8.5 7.5 0.32
##
## $scenario11_Q92
## vars n mean sd min max range se
## X1 1 36 6.24 2.11 2 9 7 0.35
##
## $scenario12_Q103
## vars n mean sd min max range se
## X1 1 39 5.28 2.38 1 9 8 0.38
##
## $scenario15_Q114
## vars n mean sd min max range se
## X1 1 37 4.03 1.59 1 7 6 0.26
##
## $scenario16_Q228
## vars n mean sd min max range se
## X1 1 37 6.42 1.43 3 9 6 0.24
##
## $scenario17_Q125
## vars n mean sd min max range se
## X1 1 38 6.11 1.86 1.5 9 7.5 0.3
##
## $scenario18_Q136
## vars n mean sd min max range se
## X1 1 38 4.46 2.31 1 9 8 0.37
##
## $scenario19_Q147
## vars n mean sd min max range se
## X1 1 36 4.57 1.93 1 7.5 6.5 0.32
##
## $scenario20_Q158
## vars n mean sd min max range se
## X1 1 39 5.26 2.27 1 9 8 0.36
##
## $scenario21_Q169
## vars n mean sd min max range se
## X1 1 37 6.42 1.71 1 9 8 0.28
# 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", "scenario16_Q228", "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 = 1.8337, df = 37, p-value = 0.07475
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.943377 6.135571
## sample estimates:
## mean of x
## 5.539474
##
##
## $scenario2_Q15
##
## One Sample t-test
##
## data: x
## t = 3.0386, df = 37, p-value = 0.004344
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.289341 6.447501
## sample estimates:
## mean of x
## 5.868421
##
##
## $scenario3_Q26
##
## One Sample t-test
##
## data: x
## t = -4.1161, df = 38, p-value = 0.0001998
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 3.527306 4.498335
## sample estimates:
## mean of x
## 4.012821
##
##
## $scenario5_Q37
##
## One Sample t-test
##
## data: x
## t = 2.4598, df = 37, p-value = 0.0187
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.118297 6.223808
## sample estimates:
## mean of x
## 5.671053
##
##
## $scenario7_Q48
##
## One Sample t-test
##
## data: x
## t = 1.9656, df = 38, p-value = 0.05669
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.988109 5.806763
## sample estimates:
## mean of x
## 5.397436
##
##
## $scenario8_Q59
##
## One Sample t-test
##
## data: x
## t = 8.2925, df = 38, p-value = 4.727e-10
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 6.909072 8.142210
## sample estimates:
## mean of x
## 7.525641
##
##
## $scenario10_Q81
##
## One Sample t-test
##
## data: x
## t = 1.9108, df = 37, p-value = 0.06381
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.963434 6.247092
## sample estimates:
## mean of x
## 5.605263
##
##
## $scenario11_Q92
##
## One Sample t-test
##
## data: x
## t = 3.5216, df = 35, p-value = 0.001214
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.523521 6.948701
## sample estimates:
## mean of x
## 6.236111
##
##
## $scenario12_Q103
##
## One Sample t-test
##
## data: x
## t = 0.73897, df = 38, p-value = 0.4645
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.509380 6.054722
## sample estimates:
## mean of x
## 5.282051
##
##
## $scenario15_Q114
##
## One Sample t-test
##
## data: x
## t = -3.7128, df = 36, p-value = 0.0006905
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 3.495553 4.558501
## sample estimates:
## mean of x
## 4.027027
##
##
## $scenario16_Q228
##
## One Sample t-test
##
## data: x
## t = 6.0299, df = 36, p-value = 6.339e-07
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.941679 6.896159
## sample estimates:
## mean of x
## 6.418919
##
##
## $scenario17_Q125
##
## One Sample t-test
##
## data: x
## t = 3.6549, df = 37, p-value = 0.0007937
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.492528 6.717999
## sample estimates:
## mean of x
## 6.105263
##
##
## $scenario18_Q136
##
## One Sample t-test
##
## data: x
## t = -1.4387, df = 37, p-value = 0.1586
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 3.700758 5.220295
## sample estimates:
## mean of x
## 4.460526
##
##
## $scenario19_Q147
##
## One Sample t-test
##
## data: x
## t = -1.34, df = 35, p-value = 0.1889
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 3.917166 5.221723
## sample estimates:
## mean of x
## 4.569444
##
##
## $scenario20_Q158
##
## One Sample t-test
##
## data: x
## t = 0.70429, df = 38, p-value = 0.4855
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 4.519387 5.993434
## sample estimates:
## mean of x
## 5.25641
##
##
## $scenario21_Q169
##
## One Sample t-test
##
## data: x
## t = 5.0476, df = 36, p-value = 1.299e-05
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
## 5.848806 6.989032
## sample estimates:
## mean of x
## 6.418919
# 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", "scenario16_Q228", "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 : 1.015058 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario2_Q15
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.1063537 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario3_Q26
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.007636963 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario5_Q37
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.3494989 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario7_Q48
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.8328306 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario8_Q59
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 5.342173e-08 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario10_Q81
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.9027209 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario11_Q92
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.03603996 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario12_Q103
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 3.352897 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario15_Q114
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.02231179 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario16_Q228
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 4.344729e-05 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario17_Q125
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.02522951 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario18_Q136
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 1.732446 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario19_Q147
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 1.909219 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario20_Q158
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 3.426736 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS
##
##
## $scenario21_Q169
## Bayes factor analysis
## --------------
## [1] Null, mu=5 : 0.0006731251 ±0%
##
## Against denominator:
## Alternative, r = 0.5, mu =/= 5
## ---
## Bayes factor type: BFoneSample, JZS