articleID <- "4-11-2014_PS" # insert the article ID code here e.g., "10-3-2015_PS"
reportType <- 'final'
pilotNames <- "Barbara Born, Elizabeth Blevins" # insert the pilot's name here e.g., "Tom Hardwicke". If there are multiple pilots enter both names in a character string e.g., "Tom Hardwicke, Bob Dylan"
copilotNames <- "Michael Frank" # insert the co-pilot's name here e.g., "Michael Frank". If there are multiple co-pilots enter both names in a character string e.g., "Tom Hardwicke, Bob Dylan"
pilotTTC <- 720 # insert the pilot's estimated time to complete (in minutes, fine to approximate) e.g., 120
copilotTTC <- 60 # insert the co- pilot's estimated time to complete (in minutes, fine to approximate) e.g., 120
pilotStartDate <- as.Date("10/29/17", format = "%m/%d/%y") # insert the pilot's start date in US format e.g., as.Date("01/25/18", format = "%m/%d/%y")
copilotStartDate <- as.Date("06/13/18", format = "%m/%d/%y") # insert the co-pilot's start date in US format e.g., as.Date("01/25/18", format = "%m/%d/%y")
completionDate <- as.Date("06/13/18", format = "%m/%d/%y") # copilot insert the date of final report completion (after any necessary rounds of author assistance) in US format e.g., as.Date("01/25/18", format = "%m/%d/%y")In the paper Multisensory Integration in Complete Unawareness: Evidence from Audiovisual Congruency Priming (Faivre, Mudrik, Schwartz, et al., 2014), the authors measured multisensory integration of two subliminal stimuli at the behavioral level. Multisensory integration* is the combination of information that is spread across sensory modalities into a new, unified representation. In this study, participants were presented with a pair of digits - one auditory and one visual - and asked to judge whether the two were identical or different from one another.
In experiment 01, that is the focus os this replication, the goal was to demonstrate whether a subliminal visual stimulus can be integrated with a supraliminal auditory stimulus. In this particular experiment, 26 students from the California Institute of Technology, aged 18 to 34, participated in 192 trials testing their reaction to pairs of stimulus. In each trial, they have a prime situation consisting of a pair of written and spoken digits that could be 2, 4, 6, and 8, and they were asked to determine whether the auditory digit was identical or different from the visual digit. Then, they received the target pair, consisting of a written and a spoken letter that could be “b,” “j,” “k,” or “m,” and they were asked the same question. In the first 96 trials, both visual and spoken primes were supraliminal, i.e., participants were in the conscious condition. In the next 96 trials, the visual condition was masked, creating an unconscious condition. 9 participants who could discriminate of the prime pairs with an accuracy above 65% in the unconscious block were excluded from the analysis. The final sample analyzed in this replication, therefore, consists of 17 subjects.
The analysis of experiment 01 focused on the unconscious condition, i.e., the final 96 trials of that experiment. This replication focused on the following statistics:
Primes accuracy: 53.3 [50.2, 56.4]
t-test: t(16) = 2.06, p = .06
Targets accuracy: 92.3 [89.8, 94.8]
The accuracy refers to the percentage of correct identification of whether the pairs were identical or different, both in prime and target situations. They tested the prime accuracy against change (50%).
library(tidyverse) # for data munging
library(knitr) # for kable table formating
library(haven) # import and export 'SPSS', 'Stata' and 'SAS' Files
library(readxl) # import excel files
library(ReproReports) # custom report functions# Prepare report object. This will be updated automatically by the reproCheck function each time values are compared.
reportObject <- data.frame(dummyRow = TRUE, reportedValue = NA, obtainedValue = NA, valueType = NA, percentageError = NA, comparisonOutcome = NA, eyeballCheck = NA)data1 = read.table("data/Exp1/Exp1_1.dat")
data2 = read.table("data/Exp1/Exp1_2.dat")
data3 = read.table("data/Exp1/Exp1_3.dat")
data4 = read.table("data/Exp1/Exp1_4.dat")
data5 = read.table("data/Exp1/Exp1_5.dat")
data6 = read.table("data/Exp1/Exp1_6.dat")
data7 = read.table("data/Exp1/Exp1_7.dat")
data8 = read.table("data/Exp1/Exp1_8.dat")
data9 = read.table("data/Exp1/Exp1_9.dat")
data10 = read.table("data/Exp1/Exp1_10.dat")
data11 = read.table("data/Exp1/Exp1_11.dat")
data12 = read.table("data/Exp1/Exp1_12.dat")
data13 = read.table("data/Exp1/Exp1_13.dat")
data14 = read.table("data/Exp1/Exp1_14.dat")
data15 = read.table("data/Exp1/Exp1_15.dat")
data16 = read.table("data/Exp1/Exp1_16.dat")
data17 = read.table("data/Exp1/Exp1_17.dat")#Append the datasets
data <- rbind(data1, data2, data3, data4, data5, data6, data7, data8, data9,
data10, data11, data12, data13, data14, data15, data16, data17)
#tidy data
##Renaming variables
data.tidy <- data %>%
rename(subj_id = V2,
trial_num = V3,
prim_cong = V4,
targ_cong = V5,
mask_audi = V6,
mask_visu = V7,
prim_audi = V8,
targ_audi = V9,
prim_visu = V10,
targ_visu = V11,
targ_resp = V12,
targ_rt = V13,
prim_resp = V14,
prim_rt = V15)%>%
filter(!(targ_rt < 300), !(targ_rt > 4000)) %>%
select(subj_id, trial_num, prim_cong, targ_cong, mask_audi,
mask_visu, prim_audi, targ_audi, prim_visu,
targ_visu, targ_resp, targ_rt, prim_resp, prim_rt)
# Keeping only the trials with the unconscious condition (those that were used in the target outcome)
data.tidy.unconscious <- subset(data.tidy, mask_visu==0)
#Finding the accuracy in responses for prime and target
temp = rep(0,nrow(data.tidy.unconscious))
temp[data.tidy.unconscious$targ_cong=="rel"&data.tidy.unconscious$targ_resp==1]=1
temp[data.tidy.unconscious$targ_cong=="unrel"&data.tidy.unconscious$targ_resp==0]=1
data.tidy.unconscious$target_accuracy = temp
temp = rep(0,nrow(data.tidy.unconscious))
temp[data.tidy.unconscious$prim_cong=="cong"&data.tidy.unconscious$prim_resp==1]=1
temp[data.tidy.unconscious$prim_cong=="incong"&data.tidy.unconscious$prim_resp==0]=1
data.tidy.unconscious$prime_accuracy = tempdata.tidy.unconscious <- data.tidy.unconscious %>%
mutate(prime_accuracy_pct = prime_accuracy*100)%>%
mutate(target_accuracy_pct = target_accuracy*100)1 - Primes accuracy: 53.3 [50.2, 56.4]
#obtaining prime mean, confidence interval, and ttest
ttest_prime <- data.tidy.unconscious%>%
group_by(subj_id) %>%
summarise(mean_prime = mean(prime_accuracy_pct))
# compare against chance (50%), two-tailed t-test
tt_prime <- t.test(ttest_prime$mean_prime, mu=50, conf.level=0.95)
reportObject <- reproCheck(reportedValue = "53.3", obtainedValue = tt_prime$estimate,
valueType = 'mean')## [1] "MINOR_ERROR for mean. The reported value (53.3) and the obtained value (53.4) differed by 0.19%. Note that the obtained value was rounded to 1 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "50.2", obtainedValue = tt_prime$conf.int[1],
valueType = 'ci')## [1] "MINOR_ERROR for ci. The reported value (50.2) and the obtained value (49.9) differed by 0.6%. Note that the obtained value was rounded to 1 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "56.4", obtainedValue = tt_prime$conf.int[2],
valueType = 'ci')## [1] "MINOR_ERROR for ci. The reported value (56.4) and the obtained value (56.9) differed by 0.89%. Note that the obtained value was rounded to 1 decimal places to match the reported value."2 - t-test: t(16) = 2.06, p = .06
reportObject <- reproCheck(reportedValue = "16", obtainedValue = tt_prime$parameter,
valueType = 'df')## [1] "MATCH for df. The reported value (16) and the obtained value (16) differed by 0%. Note that the obtained value was rounded to 0 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "2.06", obtainedValue = tt_prime$statistic,
valueType = 't')## [1] "MINOR_ERROR for t. The reported value (2.06) and the obtained value (2.03) differed by 1.46%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = ".06", obtainedValue = tt_prime$p.value,
valueType = 'p')## [1] "MATCH for p. The reported value (0.06) and the obtained value (0.06) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."Calculating the Percentage Error for each target outcomes
3 - Targets Accuracy: 92.3 [89.8, 94.8]
#obtaining target mean, confidence interval, and ttest
ttest_target <- data.tidy.unconscious%>%
group_by(subj_id) %>%
summarise(mean_target = mean(target_accuracy_pct))
# compare against chance (50%), two-tailed t-test
tt_targ <- t.test(ttest_target$mean_target, mu=50, conf.level=0.95)
reportObject <- reproCheck(reportedValue = "92.3", obtainedValue = tt_targ$estimate,
valueType = 'mean')## [1] "MINOR_ERROR for mean. The reported value (92.3) and the obtained value (92.5) differed by 0.22%. Note that the obtained value was rounded to 1 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "89.8", obtainedValue = tt_targ$conf.int[1],
valueType = 'ci')## [1] "MINOR_ERROR for ci. The reported value (89.8) and the obtained value (90) differed by 0.22%. Note that the obtained value was rounded to 1 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "94.8", obtainedValue = tt_targ$conf.int[2],
valueType = 'ci')## [1] "MINOR_ERROR for ci. The reported value (94.8) and the obtained value (95.1) differed by 0.32%. Note that the obtained value was rounded to 1 decimal places to match the reported value."We successfully reproduced all target outcomes.
Author_Assistance = FALSE # was author assistance provided? (if so, enter TRUE)
Insufficient_Information_Errors <- 0 # how many discrete insufficient information issues did you encounter?
# Assess the causal locus (discrete reproducibility issues) of any reproducibility errors. Note that there doesn't necessarily have to be a one-to-one correspondance between discrete reproducibility issues and reproducibility errors. For example, it could be that the original article neglects to mention that a Greenhouse-Geisser correct was applied to ANOVA outcomes. This might result in multiple reproducibility errors, but there is a single causal locus (discrete reproducibility issue).
locus_typo <- NA # how many discrete issues did you encounter that related to typographical errors?
locus_specification <- NA # how many discrete issues did you encounter that related to incomplete, incorrect, or unclear specification of the original analyses?
locus_analysis <- NA # how many discrete issues did you encounter that related to errors in the authors' original analyses?
locus_data <- NA # how many discrete issues did you encounter that related to errors in the data files shared by the authors?
locus_unidentified <- NA # how many discrete issues were there for which you could not identify the cause
# How many of the above issues were resolved through author assistance?
locus_typo_resolved <- NA # how many discrete issues did you encounter that related to typographical errors?
locus_specification_resolved <- NA # how many discrete issues did you encounter that related to incomplete, incorrect, or unclear specification of the original analyses?
locus_analysis_resolved <- NA # how many discrete issues did you encounter that related to errors in the authors' original analyses?
locus_data_resolved <- NA # how many discrete issues did you encounter that related to errors in the data files shared by the authors?
locus_unidentified_resolved <- NA # how many discrete issues were there for which you could not identify the cause
Affects_Conclusion <- FALSE # Do any reproducibility issues encounter appear to affect the conclusions made in the original article? This is a subjective judgement, but you should taking into account multiple factors, such as the presence/absence of decision errors, the number of target outcomes that could not be reproduced, the type of outcomes that could or could not be reproduced, the difference in magnitude of effect sizes, and the predictions of the specific hypothesis under scrutiny.reportObject <- reportObject %>%
filter(dummyRow == FALSE) %>% # remove the dummy row
select(-dummyRow) %>% # remove dummy row designation
mutate(articleID = articleID) %>% # add the articleID
select(articleID, everything()) # make articleID first column
# decide on final outcome
if(any(!(reportObject$comparisonOutcome %in% c("MATCH", "MINOR_ERROR"))) | Insufficient_Information_Errors > 0){
finalOutcome <- "Failure without author assistance"
if(Author_Assistance == T){
finalOutcome <- "Failure despite author assistance"
}
}else{
finalOutcome <- "Success without author assistance"
if(Author_Assistance == T){
finalOutcome <- "Success with author assistance"
}
}
# collate report extra details
reportExtras <- data.frame(articleID, pilotNames, copilotNames, pilotTTC, copilotTTC, pilotStartDate, copilotStartDate, completionDate, Author_Assistance, finalOutcome, Insufficient_Information_Errors, locus_typo, locus_specification, locus_analysis, locus_data, locus_unidentified, locus_typo_resolved, locus_specification_resolved, locus_analysis_resolved, locus_data_resolved, locus_unidentified_resolved)
# save report objects
if(reportType == "pilot"){
write_csv(reportObject, "pilotReportDetailed.csv")
write_csv(reportExtras, "pilotReportExtras.csv")
}
if(reportType == "final"){
write_csv(reportObject, "finalReportDetailed.csv")
write_csv(reportExtras, "finalReportExtras.csv")
}devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.0 (2020-04-24)
## os macOS Catalina 10.15.4
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Europe/London
## date 2020-05-06
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## assertthat 0.2.1 2019-03-21 [1]
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## rlang 0.4.6 2020-05-02 [1]
## rmarkdown 2.1 2020-01-20 [1]
## rprojroot 1.3-2 2018-01-03 [1]
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## scales 1.1.0 2019-11-18 [1]
## sessioninfo 1.1.1 2018-11-05 [1]
## stringi 1.4.6 2020-02-17 [1]
## stringr * 1.4.0 2019-02-10 [1]
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## source
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