articleID <- "5-4-2015_PS" # insert the article ID code here e.g., "10-3-2015_PS"
reportType <- 'final'
pilotNames <- "Benjamin deMayo, Katherine Hermann" # 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 <- "Emily Hembacher" # 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 <- 240 # insert the pilot's estimated time to complete (in minutes, fine to approximate) e.g., 120
copilotTTC <- 150 # insert the co- pilot's estimated time to complete (in minutes, fine to approximate) e.g., 120
pilotStartDate <- as.Date("11/05/17", 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("06/14/18", 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("06/15/18", 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")This is an analytic reproduction of Experiment 6 in Shah, Shafir & Mullainathan (2015) in Psychological Science. The authors were interested in the effect of scarcity on people’s consistency of valuation judgments. In this study, participants played a game of Family Feud and were given either 75 s (budget - “poor” condition) or 250 s (budget - “rich” condition) to complete the game. After playing the game, participants were either primed to think about a small account of time necessary to play one round of the game (account -“small” condition) or a large account (their overall time budget to play the entire game, account - “large” condition.) Participants rated how costly it would feel to lose 10s of time to play the game. The researchers were primarily interested in an interaction between the between-subjects factors of scarcity and account, hypothesizing that those in the budget - “poor” condition would be more consistent in their valuation of the 10s regardless of account in comparison with those in the budget - “rich” condition. The authors tested this hypothesis with a 2x2 between-subjects ANOVA.
“One participant was excluded because of a computer malfunction during the game. Time-rich participants rated the loss as more expensive when they thought about a small account (M = 8.31, 95% CI = [7.78, 8.84]) than when they thought about a large account (M = 6.50, 95% CI = [5.42, 7.58]), whereas time-poor participants’ evaluations did not differ between the small-account condition (M = 8.33, 95% CI = [7.14, 9.52]) and the large account condition (M = 8.83, 95% CI = [7.97, 9.69]). A 2 (scarcity condition) × 2 (account condition) analysis of variance revealed a significant interaction, F(1, 69) = 5.16, p < .05, ηp2 = .07.” (Shah, Shafir & Mullainathan, 2015) ——
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
library(afex) #anova functions
library(langcog) #95 percent confidence intervals# 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)data <- read_excel("data/study 6-accessible-feud.xlsx")The data are already tidy as provided by the authors.
One participant was excluded because of a computer malfunction during the game (Shah, Shafir, & Mullainathan, 2015, p. 408)
Initially we were not able to identify this participant in the data file and noted an Insufficient Information Error. However, we then contacted the authors and they informed us which participant needed to be excluded.
#data exclusion-- per author communication, subject #16 should be dropped from analyses
excluded <- "16"
d1 <- data %>%
filter(!Subject %in% excluded)%>% #participant exclusions
select(Subject, Cond, Slack, Large, expense) %>% #select relevant data columns
dplyr::rename(budget = Slack, account = Large) #rename columns to be more descriptive
#rename data entries to make them more descriptive
d1$budget <- d1$budget %>%
recode('0' = "poor", '1' = "rich")
d1$account <- d1$account %>%
recode('0' = "small", '1' = "large")
d1$Cond <- d1$Cond %>%
recode('0' = "poor_small",
'2' = "poor_large",
'1' = "rich_small",
'3' = "rich_large")Time-rich participants rated the loss as more expensive when they thought about a small account (M = 8.31, 95% CI = [7.78, 8.84]) than when they thought about a large account (M = 6.50, 95% CI = [5.42, 7.58]), whereas time-poor participants’ evaluations did not differ between the small-account condition (M = 8.33, 95% CI = [7.14, 9.52]) and the large- account condition (M = 8.83, 95% CI = [7.97, 9.69]). (Shah, Shafir, & Mullainathan, 2015, p. 408)
#Summary table of means and confidence intervals
summary <- d1 %>%
group_by(Cond) %>%
multi_boot_standard(col = "expense")
#store mean and CI values for comparison to reported values
mean_rich_small <- summary$mean[4]
lower_ci_rich_small <- summary$ci_lower[4]
upper_ci_rich_small <- summary$ci_upper[4]
mean_rich_large <- summary$mean[3]
lower_ci_rich_large <- summary$ci_lower[3]
upper_ci_rich_large <- summary$ci_upper[3]
mean_poor_small <- summary$mean[2]
lower_ci_poor_small <- summary$ci_lower[2]
upper_ci_poor_small <- summary$ci_upper[2]
mean_poor_large <- summary$mean[1]
lower_ci_poor_large <- summary$ci_lower[1]
upper_ci_poor_large <- summary$ci_upper[1]#compare reported values of means and CIs to obtained values.
#time rich - small account
reportObject <- reproCheck(reportedValue = "8.31", obtainedValue = mean_rich_small, valueType = 'mean')## [1] "MATCH for mean. The reported value (8.31) and the obtained value (8.31) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "7.78", obtainedValue = lower_ci_rich_small, valueType = 'ci')#lower ci## [1] "MINOR_ERROR for ci. The reported value (7.78) and the obtained value (7.87) differed by 1.16%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "8.84", obtainedValue = upper_ci_rich_small, valueType = 'ci')#upper ci## [1] "MINOR_ERROR for ci. The reported value (8.84) and the obtained value (8.88) differed by 0.45%. Note that the obtained value was rounded to 2 decimal places to match the reported value."#time rich - large account
reportObject <- reproCheck(reportedValue = "6.50", obtainedValue = mean_rich_large, valueType = 'mean')## [1] "MATCH for mean. The reported value (6.5) and the obtained value (6.5) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "5.42", obtainedValue = lower_ci_rich_large, valueType = 'ci')#lower ci## [1] "MINOR_ERROR for ci. The reported value (5.42) and the obtained value (5.5) differed by 1.48%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "7.58", obtainedValue = upper_ci_rich_large, valueType = 'ci')#upper ci## [1] "MINOR_ERROR for ci. The reported value (7.58) and the obtained value (7.56) differed by 0.26%. Note that the obtained value was rounded to 2 decimal places to match the reported value."#time poor - small account
reportObject <- reproCheck(reportedValue = "8.33", obtainedValue = mean_poor_small, valueType = 'mean')## [1] "MATCH for mean. The reported value (8.33) and the obtained value (8.33) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "7.14", obtainedValue = lower_ci_poor_small, valueType = 'ci')#lower ci## [1] "MATCH for ci. The reported value (7.14) and the obtained value (7.14) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "9.52", obtainedValue = upper_ci_poor_small, valueType = 'ci')#upper ci## [1] "MATCH for ci. The reported value (9.52) and the obtained value (9.52) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."#time poor - large account
reportObject <- reproCheck(reportedValue = "8.83", obtainedValue = mean_poor_large, valueType = 'mean')## [1] "MATCH for mean. The reported value (8.83) and the obtained value (8.83) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "7.97", obtainedValue = lower_ci_poor_large, valueType = 'ci')#lower ci## [1] "MINOR_ERROR for ci. The reported value (7.97) and the obtained value (8) differed by 0.38%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "9.69", obtainedValue = upper_ci_poor_large, valueType = 'ci')#upper ci## [1] "MINOR_ERROR for ci. The reported value (9.69) and the obtained value (9.67) differed by 0.21%. Note that the obtained value was rounded to 2 decimal places to match the reported value."A 2 (scarcity condition) × 2 (account condition) analysis of variance revealed a significant interaction, F(1, 69) = 5.16, p < .05, ηp2 = .07.
aov_BudgetAccount <- aov_ez(id = "Subject", dv = "expense", data = d1, between = c("budget", "account"), anova_table = list(es = "pes"))
summary(aov_BudgetAccount)## Anova Table (Type 3 tests)
##
## Response: expense
## num Df den Df MSE F pes Pr(>F)
## budget 1 69 4.6827 5.3498 0.071954 0.02371 *
## account 1 69 4.6827 1.6629 0.023533 0.20152
## budget:account 1 69 4.6827 5.1621 0.069606 0.02621 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df_budget_account <- aov_BudgetAccount$anova_table$`den Df`[3]
F_budget_account <- aov_BudgetAccount$anova_table$F[3]
pes_budget_account <- aov_BudgetAccount$anova_table$pes[3]
p_budget_account <- aov_BudgetAccount$anova_table$`Pr(>F)`[3]
reportObject <- reproCheck(reportedValue = "69", obtainedValue = df_budget_account, valueType = 'df') #degrees of freedom## [1] "MATCH for df. The reported value (69) and the obtained value (69) differed by 0%. Note that the obtained value was rounded to 0 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "5.16", obtainedValue = F_budget_account, valueType = 'F') #F-statistic## [1] "MATCH for F. The reported value (5.16) and the obtained value (5.16) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = ".07", obtainedValue = pes_budget_account, valueType = 'pes') #partial eta ## [1] "MATCH for pes. The reported value (0.07) and the obtained value (0.07) differed by 0%. Note that the obtained value was rounded to 2 decimal places to match the reported value."reportObject <- reproCheck(reportedValue = "<.05", obtainedValue = .01902, valueType = 'p', eyeballCheck = TRUE) #p value## [1] "MATCH for p. Eyeball comparison only."Although it was specified in the paper that one participant was dropped from analyses due to a computer malfunction, the data files provided online did not specify which participant was dropped. Thus, prior to communication with the author, we were not able to reproduce the key outcomes. Once the author communicated to us which participant should be dropped, we were able to reproduce all target outcomes, with only minor errors in the confidence intervals around the condition means. This could be due to different software packages producing different confidence interval estimates.
Author_Assistance = TRUE # 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 <- 0 # how many discrete issues did you encounter that related to typographical errors?
locus_specification <- 1 # how many discrete issues did you encounter that related to incomplete, incorrect, or unclear specification of the original analyses?
locus_analysis <- 0 # how many discrete issues did you encounter that related to errors in the authors' original analyses?
locus_data <- 0 # how many discrete issues did you encounter that related to errors in the data files shared by the authors?
locus_unidentified <- 0 # how many discrete issues were there for which you could not identify the cause
locus_typo_resolved <- 0 # how many discrete issues did you encounter that related to typographical errors?
locus_specification_resolved <- 1 # how many discrete issues did you encounter that related to incomplete, incorrect, or unclear specification of the original analyses?
locus_analysis_resolved <- 0 # how many discrete issues did you encounter that related to errors in the authors' original analyses?
locus_data_resolved <- 0 # how many discrete issues did you encounter that related to errors in the data files shared by the authors?
locus_unidentified_resolved <- 0 # how many discrete issues were there for which you could not identify the cause
Affects_Conclusion <- NA # 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
## abind 1.4-5 2016-07-21 [1]
## acepack 1.4.1 2016-10-29 [1]
## afex * 0.27-2 2020-03-28 [1]
## assertthat 0.2.1 2019-03-21 [1]
## backports 1.1.6 2020-04-05 [1]
## base64enc 0.1-3 2015-07-28 [1]
## boot 1.3-24 2019-12-20 [1]
## broom 0.5.6 2020-04-20 [1]
## callr 3.4.3 2020-03-28 [1]
## car 3.0-7 2020-03-11 [1]
## carData 3.0-3 2019-11-16 [1]
## cellranger 1.1.0 2016-07-27 [1]
## checkmate 2.0.0 2020-02-06 [1]
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## latticeExtra 0.6-29 2019-12-19 [1]
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## readr * 1.3.1 2018-12-21 [1]
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## ReproReports * 0.1 2020-05-06 [1]
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## rio 0.5.16 2018-11-26 [1]
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## scales 1.1.0 2019-11-18 [1]
## sessioninfo 1.1.1 2018-11-05 [1]
## statmod 1.4.34 2020-02-17 [1]
## stringi 1.4.6 2020-02-17 [1]
## stringr * 1.4.0 2019-02-10 [1]
## survival 3.1-12 2020-04-10 [1]
## testthat 2.3.2 2020-03-02 [1]
## tibble * 3.0.1 2020-04-20 [1]
## tidyr * 1.0.2 2020-01-24 [1]
## tidyselect 1.0.0 2020-01-27 [1]
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## xtable 1.8-4 2019-04-21 [1]
## yaml 2.2.1 2020-02-01 [1]
## zip 2.0.4 2019-09-01 [1]
## source
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## CRAN (R 4.0.0)
## CRAN (R 4.0.0)
## CRAN (R 4.0.0)
## CRAN (R 4.0.0)
## CRAN (R 4.0.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library