[PILOT/COPILOT ENTER RELEVANT REPORT DETAILS HERE]
articleID <- NA # "CARPS_EXT_29-4-2015_CLASS"
reportType <- NA # specify whether this is the 'pilot' report or 'final' report
pilotNames <- NA # "Melissa Mesinas"
copilotNames <- NA # # "Jinxiao Zhang"
pilotTTC <- NA # insert the pilot's estimated time to complete (in minutes, fine to approximate) e.g., 120
copilotTTC <- NA # insert the co-pilot's estimated time to complete (in minutes, fine to approximate) e.g., 120
pilotStartDate <- NA # as.Date("11/04/18", format = "%m/%d/%y")
copilotStartDate <- NA # as.Date("11/04/18", format = "%m/%d/%y")
completionDate <- NA # 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")[PILOT/COPILOT write a brief summary of the methods underlying the target outcomes written in your own words]
The authors videotaped M.B.A students giving spoken elevator pitches to their potential employers. Evaluators then watched, listened to, or read transcripts of the videos. Eighteen M.B.A students at the University of Chicago Booth School of Business (mean age = 28.2 years, SD = 2.07; 11 males) responded to the request of serving as job candidates in exchange for a small gift card. Then, 162 people (mean age = 36.86 years, SD = 15.01; 80 males) were recruited at the Museum of Science and Industry in Chicago to evaluate the candidates in exchange for a food item. There were three conditions in which evaluators (1) watched, (2) listened, or (3) read) the pitch. Three people evaluate each of the 18 candidates in each of the three conditions (i.e. slightly more than 50 evaluators per condition). Fifty participants per condition yields 80& power to detect a medium-sized effect.
[PILOT/COPILOT Some useful packages are being loaded below. You can add any additional ones you might need too.]
# load packages
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(CARPSreports) # custom report functions
library(heplots)[PILOT/COPILOT DO NOT MAKE CHANGES TO THE CODE CHUNK BELOW]
# 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_sav("/Users/melissamesinas/Documents/GitHub/CARPS_EXT_29-4-2015_CLASS/data/Study1 data.sav")
data <- read_sav("data/Study1 data.sav")#Data is already tidy
head(data)## # A tibble: 6 x 31
## ParticipantNumb… Condition Speaker Date RA competent thoughtful
## <dbl> <chr> <dbl> <date> <chr> <dbl> <dbl>
## 1 1 video 15 2013-12-28 TK&JO -2 -1
## 2 2 video 19 2013-12-28 TK&JO 0 2
## 3 3 transcri… 19 2013-12-28 TK&JO 4 0
## 4 4 audio 13 2013-12-28 TK&JO 1 2
## 5 5 audio 18 2013-12-28 TK&JO 3 3
## 6 6 audio 20 2013-12-28 TK&JO 1 1
## # ... with 24 more variables: intelligent <dbl>, like <dbl>,
## # positive_impression <dbl>, negative_impression <dbl>, hire <dbl>,
## # age <dbl>, gender <dbl+lbl>, ethnicity <chr>, English <dbl>,
## # cond2 <dbl+lbl>, negimpressionR <dbl>, intellect <dbl>,
## # impression <dbl>, speaker_age <dbl>, speaker_gender <dbl+lbl>,
## # video <dbl>, audio <dbl>, transcript <dbl>, meanhire <dbl>,
## # meanintellect <dbl>, meanimpression <dbl>, centhire <dbl>,
## # centintellect <dbl>, centimpression <dbl>attach(data)[you can remove this section if no pre-processing is required]
#I don't think I need to pre-process anything. #z-scores for audio, transcript, and video conditions for intellect
int_aud <- data %>%
filter(audio == 1) %>%
summarize(mean_int_aud = mean(intellect, na.rm = TRUE) ,sd_int_aud = sd(intellect, na.rm = TRUE))
z_aud <- (int_aud$mean_int_aud[1] - mean(intellect, na.rm = TRUE))/sd(intellect,na.rm = TRUE)
int_tra <- data %>%
filter(transcript == 1) %>%
summarize(mean_int_tra = mean(intellect, na.rm = TRUE) ,sd_int_tra = sd(intellect, na.rm = TRUE))
z_tra <- (int_tra$mean_int_tra[1] - mean(intellect, na.rm = TRUE))/sd(intellect,na.rm = TRUE)
int_vid <- data %>%
filter(video == 1) %>%
summarize(mean_int_vid = mean(intellect, na.rm = TRUE) ,sd_int_vid = sd(intellect, na.rm = TRUE))
z_vid <- (int_vid$mean_int_vid[1] - mean(intellect, na.rm = TRUE))/sd(intellect,na.rm = TRUE)
int_aud## # A tibble: 1 x 2
## mean_int_aud sd_int_aud
## <dbl> <dbl>
## 1 0.909 1.79int_tra## # A tibble: 1 x 2
## mean_int_tra sd_int_tra
## <dbl> <dbl>
## 1 -0.698 2.81int_vid## # A tibble: 1 x 2
## mean_int_vid sd_int_vid
## <dbl> <dbl>
## 1 1.09 1.80c(z_aud, z_tra, z_vid)## [1] 0.2079438 -0.4839637 0.2851513#z-scores for audio, transcript, and video conditions for general impression
imp_aud <- data %>%
filter(audio == 1) %>%
summarize(mean_imp_aud = mean(impression, na.rm = TRUE) ,sd_imp_aud = sd(impression, na.rm = TRUE))
z_aud_imp <- (imp_aud$mean_imp_aud[1] - mean(impression, na.rm = TRUE))/sd(impression,na.rm = TRUE)
imp_tra <- data %>%
filter(transcript == 1) %>%
summarize(mean_imp_tra = mean(impression, na.rm = TRUE) ,sd_imp_tra = sd(impression, na.rm = TRUE))
z_tra_imp <- (imp_tra$mean_imp_tra[1] - mean(impression, na.rm = TRUE))/sd(impression,na.rm = TRUE)
imp_vid <- data %>%
filter(video == 1) %>%
summarize(mean_imp_vid = mean(impression, na.rm = TRUE) ,sd_imp_vid = sd(impression, na.rm = TRUE))
z_vid_imp <- (imp_vid$mean_imp_vid[1] - mean(impression, na.rm = TRUE))/sd(impression,na.rm = TRUE)
imp_aud## # A tibble: 1 x 2
## mean_imp_aud sd_imp_aud
## <dbl> <dbl>
## 1 5.69 1.96imp_tra## # A tibble: 1 x 2
## mean_imp_tra sd_imp_tra
## <dbl> <dbl>
## 1 4.78 2.64imp_vid ## # A tibble: 1 x 2
## mean_imp_vid sd_imp_vid
## <dbl> <dbl>
## 1 5.98 1.91c(z_aud_imp, z_tra_imp, z_vid_imp)## [1] 0.09319522 -0.31309920 0.21990399#z-scores for audio, transcript, and video conditions for hiring likelihood
hire_aud <- data %>%
filter(audio == 1) %>%
summarize(mean_hire_aud = mean(hire, na.rm = TRUE) ,sd_hire_aud = sd(hire, na.rm = TRUE))
z_aud_hire <- (hire_aud$mean_hire_aud[1] - mean(hire, na.rm = TRUE))/sd(hire,na.rm = TRUE)
hire_tra <- data %>%
filter(transcript == 1) %>%
summarize(mean_hire_tra = mean(hire, na.rm = TRUE) ,sd_hire_tra = sd(hire, na.rm = TRUE))
z_tra_hire <- (hire_tra$mean_hire_tra[1] - mean(hire, na.rm = TRUE))/sd(hire,na.rm = TRUE)
hire_vid <- data %>%
filter(video == 1) %>%
summarize(mean_hire_vid = mean(hire, na.rm = TRUE) ,sd_hire_vid = sd(hire, na.rm = TRUE))
z_vid_hire <- (hire_vid$mean_hire_vid[1] - mean(hire, na.rm = TRUE))/sd(hire,na.rm = TRUE)
hire_aud## # A tibble: 1 x 2
## mean_hire_aud sd_hire_aud
## <dbl> <dbl>
## 1 4.34 2.26hire_tra## # A tibble: 1 x 2
## mean_hire_tra sd_hire_tra
## <dbl> <dbl>
## 1 3.06 3.15hire_vid## # A tibble: 1 x 2
## mean_hire_vid sd_hire_vid
## <dbl> <dbl>
## 1 4.46 2.43c(z_aud_hire,z_tra_hire,z_vid_hire)## [1] 0.1398867 -0.3353246 0.1856090#Compute the analysis of variance
res.aov <- aov(intellect ~ Condition, data = data)
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 2 103.8 51.88 10.81 3.99e-05 ***
## Residuals 157 753.2 4.80
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness#Eta squared
etasq(res.aov) ## Partial eta^2
## Condition 0.121064
## Residuals NA#T-test for audio and transcript on intellect rating
with(data, t.test(intellect[audio == 1], intellect[transcript == 1]))##
## Welch Two Sample t-test
##
## data: intellect[audio == 1] and intellect[transcript == 1]
## t = 3.5331, df = 90.186, p-value = 0.00065
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.703105 2.509567
## sample estimates:
## mean of x mean of y
## 0.9088050 -0.6975309#T-test for audio and video on intellect rating
with(data, t.test(intellect[audio == 1], intellect[video == 1]))##
## Welch Two Sample t-test
##
## data: intellect[audio == 1] and intellect[video == 1]
## t = -0.51433, df = 104, p-value = 0.6081
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.8703435 0.5118530
## sample estimates:
## mean of x mean of y
## 0.908805 1.088050#T-test for audio and transcript on impression rating
with(data, t.test(impression[audio == 1], impression[transcript == 1]))##
## Welch Two Sample t-test
##
## data: impression[audio == 1] and impression[transcript == 1]
## t = 2.0353, df = 97.733, p-value = 0.04452
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02272291 1.79826474
## sample estimates:
## mean of x mean of y
## 5.691358 4.780864#T-test for audio and transcript on hire rating
with(data, t.test(hire[audio == 1], hire[transcript == 1]))##
## Welch Two Sample t-test
##
## data: hire[audio == 1] and hire[transcript == 1]
## t = 2.3905, df = 92.395, p-value = 0.01886
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.216910 2.346951
## sample estimates:
## mean of x mean of y
## 4.339623 3.057692#Cohen's d for audio and transcript on intellect ratings
mean_aud <- int_aud[1]
mean_trans <- int_tra[1]
sd_aud <- int_aud[2]
sd_trans <- int_tra[2]
n_1_2 <- 54
alpha_int <- .88
pooled_sd_n <- (((n_1_2 - 1)*(sd_aud**2))+(n_1_2 - 1)*(sd_trans**2))
pooled_sd_d <- ((n_1_2*2)-2)
pooled_sd <- sqrt(pooled_sd_n/pooled_sd_d)
cohens.d <- (mean_aud - mean_trans) / pooled_sd
cohens.d## mean_int_aud
## 1 0.6817891#Cohen's d for audio and transcript on impression ratings
mean_aud <- imp_aud[1]
mean_trans <- imp_tra[1]
sd_aud <- imp_aud[2]
sd_trans <- imp_tra[2]
n_1_2 <- 54
alpha_int <- .89
pooled_sd_n <- (((n_1_2 - 1)*(sd_aud**2))+(n_1_2 - 1)*(sd_trans**2))
pooled_sd_d <- sqrt(pooled_sd_n/pooled_sd_d)
cohens.d <- (mean_aud - mean_trans) / pooled_sd
#Cohens d for audio and transcript on hire ratings
mean_aud <- hire_aud[1]
mean_trans <- hire_tra[1]
sd_aud <- hire_aud[2]
sd_trans <- hire_tra[2]
n_1_2 <- 54
pooled_sd_n <- (((n_1_2 - 1)*(sd_aud**2))+(n_1_2 - 1)*(sd_trans**2))
pooled_sd_d <- sqrt(pooled_sd_n/pooled_sd_d)
cohens.d <- (mean_aud - mean_trans) / pooled_sdThe overall replication was replicable, but it took a lot of consideration of multiple directions the authors could have gone as they were not clear about their directions on how they proceeded with their analyses.
[PILOT/COPILOT ENTER RELEVANT INFORMATION BELOW]
Author_Assistance = FALSE # was author assistance provided? (if so, enter TRUE)
Major_Numerical_Errors <- 1
# compareValues(reportedValue = 3.79 , obtainedValue = 3.53)
#t-test for audio and transcript on intellect ratings
Minor_Numerical_Errors <- 2
#compareValues(reportedValue = .03, obtainedValue = .04) #t-test p-value for audio and transcript on impression rating, the obtained value (.04) and the reported value (.03) differed by 25%
#compareValues(reportedValue = 2.49, obatinedValue = 2.39) #t-test for audio and transcript on hire rating, the obtained value (.02) and the reported value (.01) differed by 50%
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? 0
locus_specification <- NA # how many discrete issues did you encounter that related to incomplete, incorrect, or unclear specification of the original analyses? 0
locus_analysis <- NA # how many discrete issues did you encounter that related to errors in the authors' original analyses? 0
locus_data <- NA # how many discrete issues did you encounter that related to errors in the data files shared by the authors? 0
locus_unidentified <- NA # how many discrete issues were there for which you could not identify the cause 0
# How many of the above issues were resolved through author assistance? N/A
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 <- NA # Do any reproducibility issues encounter appear to affect the conclusions made in the original article? TRUE, FALSE, or NA. 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.[PILOT/COPILOT DOD NOT EDIT THE CODE CHUNK BELOW]
# I tried to run the compareValues function but it would not run as R could not find the function even though the library was loaded. [This function will output information about the package versions used in this report:]
devtools::session_info()## ─ Session info ──────────────────────────────────────────────────────────
## setting value
## version R version 3.5.1 (2018-07-02)
## os macOS High Sierra 10.13.6
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Los_Angeles
## date 2018-11-05
##
## ─ Packages ──────────────────────────────────────────────────────────────
## package * version date lib
## abind 1.4-5 2016-07-21 [1]
## assertthat 0.2.0 2017-04-11 [1]
## backports 1.1.2 2017-12-13 [1]
## base64enc 0.1-3 2015-07-28 [1]
## bindr 0.1.1 2018-03-13 [1]
## bindrcpp * 0.2.2 2018-03-29 [1]
## broom 0.5.0 2018-07-17 [1]
## callr 3.0.0 2018-08-24 [1]
## car * 3.0-2 2018-08-23 [1]
## carData * 3.0-2 2018-09-30 [1]
## CARPSreports * 0.1 2018-11-02 [1]
## cellranger 1.1.0 2016-07-27 [1]
## cli 1.0.1 2018-09-25 [1]
## colorspace 1.3-2 2016-12-14 [1]
## crayon 1.3.4 2017-09-16 [1]
## curl 3.2 2018-03-28 [1]
## data.table 1.11.8 2018-09-30 [1]
## desc 1.2.0 2018-05-01 [1]
## devtools 2.0.1 2018-10-26 [1]
## digest 0.6.18 2018-10-10 [1]
## dplyr * 0.7.7 2018-10-16 [1]
## evaluate 0.12 2018-10-09 [1]
## fansi 0.4.0 2018-10-05 [1]
## forcats * 0.3.0 2018-02-19 [1]
## foreign 0.8-71 2018-07-20 [1]
## fs 1.2.6 2018-08-23 [1]
## ggplot2 * 3.1.0 2018-10-25 [1]
## glue 1.3.0 2018-07-17 [1]
## gtable 0.2.0 2016-02-26 [1]
## haven * 1.1.2 2018-06-27 [1]
## heplots * 1.3-5 2018-04-03 [1]
## hms 0.4.2 2018-03-10 [1]
## htmltools 0.3.6 2017-04-28 [1]
## httr 1.3.1 2017-08-20 [1]
## jsonlite 1.5 2017-06-01 [1]
## knitr * 1.20 2018-02-20 [1]
## lattice 0.20-35 2017-03-25 [1]
## lazyeval 0.2.1 2017-10-29 [1]
## lubridate 1.7.4 2018-04-11 [1]
## magrittr 1.5 2014-11-22 [1]
## MASS 7.3-50 2018-04-30 [1]
## memoise 1.1.0 2017-04-21 [1]
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## pkgbuild 1.0.2 2018-10-16 [1]
## pkgconfig 2.0.2 2018-08-16 [1]
## pkgload 1.0.2 2018-10-29 [1]
## plyr 1.8.4 2016-06-08 [1]
## prettyunits 1.0.2 2015-07-13 [1]
## processx 3.2.0 2018-08-16 [1]
## ps 1.2.0 2018-10-16 [1]
## purrr * 0.2.5 2018-05-29 [1]
## R6 2.3.0 2018-10-04 [1]
## Rcpp 0.12.19 2018-10-01 [1]
## readr * 1.1.1 2017-05-16 [1]
## readxl * 1.1.0 2018-04-20 [1]
## remotes 2.0.2 2018-10-30 [1]
## rio 0.5.10 2018-03-29 [1]
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## rmarkdown 1.10 2018-06-11 [1]
## rprojroot 1.3-2 2018-01-03 [1]
## rstudioapi 0.8 2018-10-02 [1]
## rvest 0.3.2 2016-06-17 [1]
## scales 1.0.0 2018-08-09 [1]
## sessioninfo 1.1.0 2018-09-25 [1]
## stringi 1.2.4 2018-07-20 [1]
## stringr * 1.3.1 2018-05-10 [1]
## tibble * 1.4.2 2018-01-22 [1]
## tidyr * 0.8.2 2018-10-28 [1]
## tidyselect 0.2.5 2018-10-11 [1]
## tidyverse * 1.2.1 2017-11-14 [1]
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## zip 1.0.0 2017-04-25 [1]
## source
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